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Clinical applications of passive long-term heart rate (HR) monitoring in patients with cardiac arrhythmias include adequate drug titration of atrioventricular (AV) nodal drugs and assessment of medical compliance with treatment. A majority of patients treated with beta-blockers, especially patients with atrial fibrillation (AF), require some degree of drug titration during the first 6 months of treatment to ensure that adequate HR control and medicine compliance has been achieved. Failing to achieve adequate rate control in patients with AF can lead to worsening symptoms, heart failure exacerbations, and potentially tachycardia-induced cardiomyopathy. Enabling video-based monitoring during telehealth patient visits could facilitate providers to measure heart rate (HR) without the need for a dedicated home device (smartwatch, SPO2 device, or others). Videoplethysmography (VPG) is a monitoring technology that measures pulse rate by utilizing front-facing cameras embedded in smart devices. VPG provides a remote and contactless cardiac monitoring solution. We conducted a clinical experiment to evaluate the accuracy of VPG in measuring HR while running on two portable devices: Samsung S10 smartphones and S3 tablets. We used a single‑lead ECG to measure the heart rate at the time of the VPG recordings in AF patients. We employed the Bland-Altman method to measure the level of agreement between videoplethysmography and ECG-based measurements of HR. The findings reveal that the mean difference in videoplethysmography and ECG-based heart rate was inferior to 1 bpm across the 2 devices with confidence intervals ranging from 3 to 12 BPM. Our facial video-based HR monitoring solution could assist providers in measuring heart rates in their patients with AF during remote telehealth visits., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Background: Contactless vital signs (VS) measurement with video photoplethysmography (vPPG), motion analysis (MA), and passive infrared thermometry (pIR) has shown promise., Objectives: To compare conventional (contact-based) and experimental contactless VS measurement approaches for emergency department (ED) walk-in triage in pandemic conditions., Methods: Patients' heart rates (HR), respiratory rates (RR), and temperatures were measured with cardiorespiratory monitor and vPPG, manual count and MA, and contact thermometers and pIR, respectively., Results: There were 475 walk-in ED patients studied (95% of eligible). Subjects were 35.2 ± 20.8 years old (range 4 days‒95 years); 52% female, 0.2% transgender; had Fitzpatrick skin type of 2.3 ± 1.4 (range 1‒6), Emergency Severity Index of 3.0 ± 0.6 (range 2‒5), and contact temperature of 36.83°C (range 35.89-39.4°C) (98.3°F [96.6‒103°F]). Pediatric HR and RR data were excluded from analysis due to research challenges associated with pandemic workflow. For a 30-s, unprimed "Triage" window in 377 adult patients, vPPG-MA acquired 377 (100%) HR measurements featuring a mean difference with cardiorespiratory monitor HR of 5.9 ± 12.8 beats/min (R = 0.6833) and 252 (66.8%) RR measurements featuring a mean difference with manual RR of -0.4 ± 2.6 beats/min (R = 0.8128). Subjects' Emergency Severity Index components based on conventional VS and contactless VS matched for 83.8% (HR) and 89.3% (RR). Filtering out vPPG-MA measurements with low algorithmic confidence reduced VS acquired while improving correlation with conventional measurements. The mean difference between contact and pIR temperatures was 0.83 ± 0.67°C (range -1.16-3.5°C) (1.5 ± 1.2°F [range -2.1-6.3°F]); pIR fever detection improved with post hoc adjustment for mean bias., Conclusion: Contactless VS acquisition demonstrated good agreement with contact methods during adult walk-in ED patient triage in pandemic conditions; clinical applications will need further study., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Early diagnosis can be crucial to limit both the mortality and economic burden of cardiovascular diseases. Recent developments have focused on the continuous monitoring of cardiac activity for a prompt diagnosis. Nowadays, wearable devices are gaining broad interest for a continuous monitoring of the heart rate (HR). One of the most promising methods to estimate HR is the seismocardiography (SCG) which allows to record the thoracic vibrations with high non-invasiveness in out-of-laboratory settings. Despite significant progress on SCG, the current state-of-the-art lacks both information on standardized sensor positioning and optimization of wearables design. Here, we introduce a soft wearable system (SWS), whose novel design, based on a soft polymer matrix embedding an array of fiber Bragg gratings, provides a good adhesion to the body and enables the simultaneous recording of SCG signals from multiple measuring sites. The feasibility assessment on healthy volunteers revealed that the SWS is a suitable wearable solution for HR monitoring and its performance in HR estimation is strongly influenced by sensor positioning and improved by a multi-sensor configuration. These promising characteristics open the possibility of using the SWS in monitoring patients with cardiac pathologies in clinical (e.g., during cardiac magnetic resonance procedures) and everyday life settings., (© 2021. The Author(s).)

Objective: Current estimates of the incidence of tachyarrhythmias in infants rely on clinical documentation and may not reflect the true rate in the general population. Our aim was to describe the epidemiology of tachyarrhythmia detected in a large cohort of infants using direct-to-consumer heart rate (HR) monitoring., Study Design: Data were collected from Owlet Smart Sock devices used in infants in the US with birthdates between February 2017 and February 2019. We queried the HR data for episodes of tachyarrhythmia (HR of ≥240 bpm for >60 seconds)., Results: The study included 100 949 infants (50.8% male) monitored for more than 200 million total hours. We identified 5070 episodes of tachyarrhythmia in 2508 infants. The cumulative incidence of tachyarrhythmia in our cohort was 2.5% over the first year of life. The median age at the time of the first episode of tachyarrhythmia was 36 days (range, 1-358 days). Tachyarrhythmia was more common in infants with congenital heart disease (4.0% vs 2.4%; P = .015) and in females (2.7% vs 2.0%; P < .001). The median length of an episode was 7.3 minutes (range, 60 seconds to 5.4 hours) and the probability of an episode lasting longer than 45 minutes was 16.8% (95% CI, 15.4%-18.3%)., Conclusions: We found the cumulative incidence of tachyarrhythmia among infants using direct-to-consumer HR monitors to be higher than previously reported in studies relying on clinical diagnosis. This finding may represent previously undetected subclinical disease in young infants, the significance of which remains uncertain. Clinicians should be prepared to discuss these events with parents., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Pacemakers use heart rate histograms (% beats) and sensor indicated rate histograms (% time) to illustrate rate distributions. When programmed to the rate adaptive modes, these data are used to determine the appropriateness of rate response to activity. These histograms are generated from instantaneous heart rate calculations. In humans, such data are compared to known histographic rate profiles. Such rate profiles during 24 h in the dog are not available. Moreover, data representation differ between Holter monitoring and pacemakers making comparisons challenging. The rate distribution in dogs >7-years of age was determined over 24 h using instantaneous and rolling average heart rate. Such data could serve as a guide to programming pacing rates for dogs. Sinus arrhythmia resulted in dissimilar heart rate profiles depending on the method of determining rate. The long intervals of sinus arrhythmia resulted in median values for the percent of time with an instantaneous heart rate of <50 beats/min (bpm) of 15%, whereas a rolling average heart rate of <50 bpm was 0.2%. Based on the cumulative time of the rolling average rate, dogs spent 26.3% of the day between 70-90 bpm with rates <65 bpm and >90 bpm approximating 30% for each. Rates >160 bpm were uncommon (<1%). However, high variability existed between dogs. This study demonstrated the shortcomings of both instantaneous and averaging methods to evaluate heart rate profiles in the dog and that both methods should be incorporated when making pacing rate decisions during programming., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Objectives: The maturation of the sympathetic nervous system (SNS) occurs steadily throughout gestation while the myelinated vagus has accelerated maturation periods, between 25 and 32 weeks of gestation and a further increase around 37-38 weeks of gestation. The aim was to quantify the cardiac autonomic regulation maturation, as a function of gestational age (GA) in a cohort of low risk preterm infants born between 28 and 32 weeks of gestation by assessing heart rate variability (HRV) at week 32, and at week 35 postmenstrual age (PMA)., Methods: Forty preterm infants were recruited, 24 h recordings of breathing rate and RR intervals were obtained at week 32 and week 35 PMA., Results: A significant difference was noted between preterm infants born before 32 weeks GA and preterm infants born at week 32; the latter present higher HRV values throughout the follow-up period. No significant change over time was noted for the parasympathetic HRV measures while a significant increase was found in the sympathetic system. Moreover, a significant interaction effect of time and system was found, the increase in values of the sympathetic system over time was significantly larger than the change noted in the vagal HRV measures., Conclusions: Given the beneficial influence of vagal tone on health and developmental outcomes in preterm infants, the findings of the current study highlight the need for further studies on the impact of specifics gestational age on vagal development and later assessing interventions associate with its continue development and maturation at these specific periods., (© 2021 Walter de Gruyter GmbH, Berlin/Boston.)

Heart rate variability (HRV) analysis has been performed on ECG-derived data sets for more than 170 years but is currently undergoing a rapid evolution, thanks to the expansion of the human and veterinary medical technology sector. Traditional HRV analysis was initially performed to identify changes in vago-sympathetic balance, while the most recent focus has expanded to include the use of complex computer algorithms, neural networks and machine learning technology to identify cardiac arrhythmias, particularly atrial fibrillation (AF). Some of these techniques have recently been translated for use in the field of equine cardiology, with particular focus on improving the diagnosis of arrhythmias both at rest and during exercise. This review focuses on understanding the basic HRV variables and important factors to consider when collecting data for use in HRV analysis. In addition, the use of HRV analysis for the diagnosis of arrhythmias is discussed from human, small animal and equine perspectives. Finally, the future of HRV analysis is briefly introduced, including an overview of future developments in this rapidly expanding and exciting field., Competing Interests: Declaration of Competing Interest The authors report no declarations of interest., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Background: Patients with obstructive sleep apnea syndrome (OSAS) experience excessive daytime sleepiness and insomnia and they are at risk of developing cardiovascular disease and stroke. Continuous positive airway pressure therapy could improve symptoms and decrease these risks; however, adherence is problematic. Although the oral appliance is another therapeutic option, patient satisfaction is limited and the effect of the nasal airway stent - a new device - remains unclear., Objectives: The aim of this study was to evaluate the effect of NAS therapy in patients with mild-to-moderate OSAS in a prospective, single-arm, interventional pilot study., Method: Patients with mild/moderate sleep apnea (n = 71; Apnea-Hypopnea Index [AHI], 5-20 events/h on polysomnography) were recruited. Sleep-associated events were measured using a portable device (WatchPAT200) pre- and immediately post-treatment and at 1 month follow-up. AHI (including supine and non-supine AHI), Oxygen Desaturation Index (ODI), Respiratory Disturbance Index (RDI), percutaneous oxygen saturation, heart rate, and snore volume were evaluated. Symptoms were assessed using the Epworth Sleepiness Scale, Pittsburgh Sleep Quality Index, and Hospital Anxiety and Depression Scale., Results: NAS use significantly improved AHI, supine AHI, RD, ODI, and snore volume compared to pre-intervention (r = 0.44, 0.48, 0.3, 0.42, and 0.34; p < 0.001, p < 0.001, p = 0.011, p < 0.001, and p = 0.048, respectively). Additionally, 25 and 10% of patients showed complete and partial response for AHI, respectively; these improvements remained significant 1 month later. Pittsburgh Sleep Quality Index scores improved from 6.0 to 5.3 (r = 0.46, p = 0.022)., Conclusions: NAS therapy reduced severity and snoring in patients with mild-to-moderate OSAS. Approximately 30% of patients did not tolerate NAS due to side effects., (© 2020 The Author(s) Published by S. Karger AG, Basel.)

Wrist-worn devices are popular for heart rate monitoring, including use of photoplethysmography. The Apple Watch series 4 can identify atrial fibrillation. We describe a case of identification re-entrant supraventricular tachycardia not identified by outpatient rhythm monitoring, however, was identified by the Apple Watch series 4, which lead to electrophysiology study and successful ablation of atrioventricular nodal re-entrant tachycardia., (© 2020 The Authors. Annals of Noninvasive Electrocardiology published by Wiley Periodicals, Inc.)

Validation of heart rate responses in wearable technology devices is generally composed of laboratory-based protocols that are steady state in nature and as a result, high accuracy measures are returned. However, there is a need to understand device validity in applied settings that include varied intensities of exercise. The purpose was to determine concurrent heart rate validity during trail running. Twenty-one healthy participants volunteered (female n = 10, [mean (SD)]: age = 31 [11] years, height = 173.0 [7] cm, mass = 75.6 [13] kg). Participants were outfitted with wearable technology devices (Garmin Fenix 5 wristwatch, Jabra Elite Sport earbuds, Motiv ring, Scosche Rhythm+ forearm band, Suunto Spartan Sport watch with accompanying chest strap) and completed a self-paced 3.22 km trail run while concurrently wearing a criterion heart rate strap (Polar H7 heart rate monitor). The trail runs were out-and-back with the first 1.61 km in an uphill direction, and the 1.61 return being downhill in nature. Validity was determined through three methods: Mean Absolute Percent Error (MAPE), Bland-Altman Limits of Agreement (LOA), and Lin's Concordance Coefficient (rC). Validity measures overall are as follows: Garmin Fenix 5 (MAPE = 13%, LOA = -32 to 162, rC = 0.32), Jabra Elite Sport (MAPE = 23%, LOA = -464 to 503, rC = 0.38), Motiv ring (MAPE = 16%, LOA = -52 to 96, rC = 0.29), Scosche Rhythm+ (MAPE = 6%, LOA = -114 to 120, rC = 0.79), Suunto Spartan Sport (MAPE = 2%, LOA = -62 to 61, rC = 0.96). All photoplethysmography-based (PPG) devices displayed poor heart rate agreement during variable intensity trail running. Until technological advances occur in PPG-based devices allowing for acceptable agreement, heart rate in outdoor environments should be obtained using an ECG-based chest strap that can be connected to a wristwatch or other comparable receiver., Competing Interests: The authors have declared that no competing interests exist.

Current techniques for heart rate determination in adult zebrafish require specialist expertise and are often invasive, technically challenging and not readily transferable to other laboratories for routine assessment. Here, we present a simple, noninvasive and inexpensive light-cardiogram technique to assess heart rate and frequency in adult zebrafish. Brightfield microscope paired with a high-resolution camera and ImageJ (an open source software) were employed as core recording and processing platforms respectively. The heart was visualised ventrally and located by juxtaposing an isosceles triangle between the opercula as reference to analyse pixel intensity fluctuations generated by each cardiac cycle to derive heart rate and frequency. Compared to transparent embryos, the cardiograms generated reverse light signal oscillations, with contraction and relaxation of the heart (ventricle) corresponding to reduced and increased pixel intensities respectively. The heart rates (♂ 122.58 ± 2.15 and ♀ 121.37 ± 2.63 beat/min) and mean dominant frequency (♂ 2.04 ± 0.035 and ♀ 2.05 ± 0.048 Hz) between the sexes were not significantly (P > .05) different at 28 °C. However, the F D amplitudes between males (0.26 ± 0.03) and females (0.45 ± 0.05) were significantly different (P < .05) suggesting sex specific diastolic cardiac outputs. Collectively, the technique can be used to measure heartbeats as well as readily adaptable to record relative cardiac outputs and compare differences between physiological states (e.g. sexes). Moreover, the approach could be amenable to automation and applicable to other fish species, enabling researchers the flexibility to measure these and other critical heart health endpoint with relative ease., Competing Interests: Declaration of Competing Interest The authors declare that there are no conflicts of interest., (Crown Copyright © 2020. Published by Elsevier Inc. All rights reserved.)

Background: Smartphone apps for heart rate variability (HRV) measurement have been extensively developed in the last decade. However, ultra-short-term HRV recordings taken by wearable devices have not been examined., Objective: The aims of this study were the following: (1) to compare the validity and reliability of ultra-short-term and short-term HRV time-domain and frequency-domain variables in a novel smartphone app, Pulse Express Pro (PEP), and (2) to determine the agreement of HRV assessments between an electrocardiogram (ECG) and PEP., Methods: In total, 60 healthy adults were recruited to participate in this study (mean age 22.3 years [SD 3.0 years], mean height 168.4 cm [SD 8.0 cm], mean body weight 64.2 kg [SD 11.5 kg]). A 5-minute resting HRV measurement was recorded via ECG and PEP in a sitting position. Standard deviation of normal R-R interval (SDNN), root mean square of successive R-R interval (RMSSD), proportion of NN50 divided by the total number of RR intervals (pNN50), normalized very-low-frequency power (nVLF), normalized low-frequency power (nLF), and normalized high-frequency power (nHF) were analyzed within 9 time segments of HRV recordings: 0-1 minute, 1-2 minutes, 2-3 minutes, 3-4 minutes, 4-5 minutes, 0-2 minutes, 0-3 minutes, 0-4 minutes, and 0-5 minutes (standard). Standardized differences (ES), intraclass correlation coefficients (ICC), and the Spearman product-moment correlation were used to compare the validity and reliability of each time segment to the standard measurement (0-5 minutes). Limits of agreement were assessed by using Bland-Altman plot analysis., Results: Compared to standard measures in both ECG and PEP, pNN50, SDNN, and RMSSD variables showed trivial ES (<0.2) and very large to nearly perfect ICC and Spearman correlation coefficient values in all time segments (>0.8). The nVLF, nLF, and nHF demonstrated a variation of ES (from trivial to small effects, 0.01-0.40), ICC (from moderate to nearly perfect, 0.39-0.96), and Spearman correlation coefficient values (from moderate to nearly perfect, 0.40-0.96). Furthermore, the Bland-Altman plots showed relatively narrow values of mean difference between the ECG and PEP after consecutive 1-minute recordings for SDNN, RMSSD, and pNN50. Acceptable limits of agreement were found after consecutive 3-minute recordings for nLF and nHF., Conclusions: Using the PEP app to facilitate a 1-minute ultra-short-term recording is suggested for time-domain HRV indices (SDNN, RMSSD, and pNN50) to interpret autonomic functions during stabilization. When using frequency-domain HRV indices (nLF and nHF) via the PEP app, a recording of at least 3 minutes is needed for accurate measurement., (©Yung-Sheng Chen, Wan-An Lu, Jeffrey C Pagaduan, Cheng-Deng Kuo. Originally published in JMIR mHealth and uHealth (http://mhealth.jmir.org), 31.07.2020.)

Background: Growing prevalence of atrial fibrillation (AF) in the ageing population and its associated life-changing health and resource implications have led to a need to improve its early detection. Primary care is an ideal place to screen for AF; however, this is limited by shortages in general practitioner (GP) resources. Recent increases in the number of clinical pharmacists within primary care makes them ideally placed to conduct AF screening. This study aimed to determine the feasibility of GP practice-based clinical pharmacists to screen the over-65s for AF, using digital technology and pulse palpation during the influenza vaccination season., Methods and Findings: Screening was conducted over two influenza vaccination seasons, 2017-2018 and 2018-2019, in four GP practices in Kent, United Kingdom. Pharmacists were trained by a cardiologist to pulse palpate, record, and interpret a single-lead ECG (SLECG). Eligible persons aged ≥65 years (y) attending an influenza vaccination clinic were offered a free heart rhythm check. Six hundred four participants were screened (median age 73 y, 42.7% male). Total prevalence of AF was 4.3%. All participants with AF qualified for anticoagulation and were more likely to be male (57.7%); be older; have an increased body mass index (BMI); and have a CHA2DS2-VASc (Congestive heart failure, Hypertension, Age ≥ 75 years, Diabetes, previous Stroke, Vascular disease, Age 65-74 years, Sex category) score ≥ 3. The sensitivity and specificity of clinical pharmacists diagnosing AF using pulse palpation was 76.9% (95% confidence interval [CI] 56.4-91.0) and 92.2% (95% CI 89.7-94.3), respectively. This rose to 88.5% (95% CI 69.9-97.6) and 97.2% (95% CI 95.5-98.4) with an SLECG. At follow-up, four participants (0.7%) were diagnosed with new AF and three (0.5%) were initiated on anticoagulation. Screening with SLECG also helped identify new non-AF cardiovascular diagnoses, such as left ventricular hypertrophy, in 28 participants (4.6%). The screening strategy was cost-effective in 71.8% and 64.3% of the estimates for SLECG or pulse palpation, respectively. Feedback from participants (422/604) was generally positive. Key limitations of the study were that the intervention did not reach individuals who did not attend the practice for an influenza vaccination and there was a limited representation of UK ethnic minority groups in the study cohort., Conclusions: This study demonstrates that AF screening performed by GP practice-based pharmacists was feasible, economically viable, and positively endorsed by participants. Furthermore, diagnosis of AF by the clinical pharmacist using an SLECG was more sensitive and more specific than the use of pulse palpation alone. Future research should explore the key barriers preventing the adoption of national screening programmes., Competing Interests: The authors have declared that no competing interests exist.

Atrial fibrillation (AF) is a major cause of morbidity and mortality globally, and much of this is driven by challenges in its timely diagnosis and treatment. Existing and emerging mobile technologies have been used to successfully identify AF in a variety of clinical and community settings, and while these technologies offer great promise for revolutionizing AF detection and screening, several major barriers may impede their effectiveness. The unclear clinical significance of device-detected AF, potential challenges in integrating patient-generated data into existing healthcare systems and clinical workflows, harm resulting from potential false positives, and identifying the appropriate scope of population-based screening efforts are all potential concerns that warrant further investigation. It is crucial for stakeholders such as healthcare providers, researchers, funding agencies, insurers, and engineers to actively work together in fulfilling the tremendous potential of mobile technologies to improve AF identification and management on a population level.

Atrial fibrillation (AF) is a common and morbid arrhythmia. Stroke is a major hazard of AF and may be preventable with oral anticoagulation. Yet since AF is often asymptomatic, many individuals with AF may be unaware and do not receive treatment that could prevent a stroke. Screening for AF has gained substantial attention in recent years as several studies have demonstrated that screening is feasible. Advances in technology have enabled a variety of approaches to facilitate screening for AF using both medical-prescribed devices as well as consumer electronic devices capable of detecting AF. Yet controversy about the utility of AF screening remains owing to concerns about potential harms resulting from screening in the absence of randomized data demonstrating effectiveness of screening on outcomes such as stroke and bleeding. In this review, we summarize current literature, present technology, population-based screening considerations, and consensus guidelines addressing the role of AF screening in practice.

Objective: To compare 2 different methods (auscultation with a stethoscope and umbilical cord palpation) of heart rate (HR) estimation in newborns at risk for resuscitation in a low-resource setting., Study Design: Sixty newborns at risk for resuscitation born at the St. Luke Catholic Hospital in Wolisso (Ethiopia) were randomized to HR assessment by auscultation using a stethoscope or umbilical cord palpation. HR was assessed at 60, 90, 120 seconds, and 5 minutes of life. The primary outcome was the agreement of HR obtained by auscultation or palpation compared with the HR determined by electrocardiogram., Results: Mean difference between auscultation using a stethoscope and electrocardiogram was -13 bpm, -4 bpm, -6 bpm, and -10 bpm at 60, 90, 120 seconds, and at 5 minutes of life. Mean difference between palpation and electrocardiogram of was -20 bpm, -25 bpm, -23 bpm, and -31 bpm at 60, 90, 120 seconds, and at 5 minutes of life. The magnitude of the difference between auscultation and electrocardiogram was lower than that between palpation and electrocardiogram over time (P = .007). HR range was correctly identified in 14 out of 16 measurements (87%) with HR <100 bpm., Conclusion: HR assessment by auscultation was more accurate compared with cord palpation, but both may provide adequate clinical information to healthcare providers in terms of HR ranges. The clinical advantage of providing a stethoscope in low-resource settings remains to be established., Trial Registration: ClinicalTrials.gov: NCT03854435., (Copyright © 2020 Elsevier Inc. All rights reserved.)

As marine environments are influenced by global warming there is a need to thoroughly understand the relationship between physiological limits and temperature in fish. One quick screening method of a physiological thermal tipping point is the temperature at which maximum heart rate (ƒ Hmax ) can no longer scale predictably with warming and is referred to as the Arrhenius break temperature (T AB ). The use of this method has been successful for freshwater fish by using external electrodes to detect an electrocardiogram (ECG), however, the properties of this equipment pose challenges in salt water when evaluating marine fish. To overcome these challenges, this study aimed to explore the potential use of implantable heart rate loggers to quantify the T AB of Chrysoblephus laticeps, a marine Sparid, following the ECG method protocols where ƒ Hmax is monitored over an acute warming event and the T AB is subsequently identified using a piece-wise linear regression model. Of the nine experimental fish, only five (56%) returned accurate ƒ Hmax data. The T AB of successful trials was identified each time and ranged from 18.09 to 20.10 °C. This study therefore provides evidence that implantable heart rate loggers can estimate T AB of fish which can be applied to many marine species., Competing Interests: Declaration of competing interest None., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Background: The association of resting heart rate (RHR) and hypertension in adults is unclear. We aimed to perform a meta-analysis of cohort studies to clarify the association., Methods: We searched PubMed and Embase from their inception to November 3, 2017, for published articles. We used a random effects model to combine study-specific relative risks (RRs) and 95% confidence intervals (CIs). We used restricted cubic spline functions to assess the dose-response relationship., Results: Nine cohort articles (12 independent studies) with 79,399 individuals and more than 26,380 incident cases of hypertension were included. The summary RR for hypertension was 1.09 (95% CI: 1.06-1.13) with each 10 bpm increment in RHR. The cubic spline model suggested that when compared with 55.5 beats per minute, the risk of hypertension significantly increased with increasing levels of RHR (Pnonlinearity = 0.059)., Conclusion: We found a linear dose-response association between RHR and incident hypertension in adults.

On standard electrocardiogram (ECG) PQ interval is known to be moderately heart rate dependent, but no physiologic details of this dependency have been established. At the same time, PQ dynamics is a clear candidate for non-invasive assessment of atrial abnormalities including the risk of atrial fibrillation. We studied PQ heart rate dependency in 599 healthy subjects (aged 33.5 ± 9.3 years, 288 females) in whom drug-free day-time 12-lead ECG Holters were available. Of these, 752,517 ECG samples were selected (1256 ± 244 per subject) to measure PQ and QT intervals and P wave durations. For each measured ECG sample, 5-minute history of preceding cardiac cycles was also obtained. Although less rate dependent than the QT intervals (36 ± 19% of linear slopes), PQ intervals were found to be dependent on underlying cycle length in a highly curvilinear fashion with the dependency significantly more curved in females compared to males. The PQ interval also responded to the heart rate changes with a delay which was highly sex dependent (95% adaptation in females and males after 114.9 ± 81.1 vs 65.4 ± 64.3 seconds, respectively, p < 0.00001). P wave duration was even less rate dependent than the PQ interval (9 ± 10% of linear QT/RR slopes). Rate corrected P wave duration was marginally but significantly shorter in females than in males (106.8 ± 8.4 vs 110.2 ± 7.9 ms, p < 0.00001). In addition to establishing physiologic standards, the study suggests that the curvatures and adaptation delay of the PQ/cycle-length dependency should be included in future non-invasive studies of atrial depolarizations.

A mandibular advancement device (MAD) is recommended as an alternative therapy for obstructive sleep apnea (OSA), which effectively reduces the collapsibility of the upper airway during sleep by advancing the mandible. However, the effects of MAD therapy on cardiac autonomic modulation remain unclear. We evaluated the effects of MAD on nocturnal heart rate variability (HRV) in OSA. Anthropometric data, questionnaire results, and HRV parameters (evaluated using time domain and frequency domain methods) of 58 adult patients with OSA treated with MAD therapy were retrospectively reviewed. All patients underwent polysomnography at baseline and 3-month follow-up. The average normal-to-normal (NN) interval, standard deviation of the NN interval, low-frequency power in normalized units (LFnu), and high-frequency power in normalized units (HFnu) showed significant changes with MAD therapy. Based on the criteria for success (decrease in the apnea-hypopnea index by >50% and value <20/h), 34 and 24 patients were classified into the response and nonresponse groups, respectively. No differences in baseline characteristics were detected between groups, except for higher body mass index and lower minimal oxygen saturation in the nonresponse group. A subgroup analysis indicated that the average NN interval and HFnu significantly increased, and that Total power (TP), very low frequency, low frequency(LF), low frequency/high frequency and LFnu significantly decreased compared to baseline in the response group; however, no HRV changes were found in the nonresponse group. After adjusting for age, sex, and body mass index, the response group showed significant changes from baseline in TP and LF compared to the nonresponse group. Therefore, HRV may be useful for determining the efficacy of MAD therapy in OSA.

Background: Most deaths of comatose survivors of out-of-hospital sudden cardiac arrest result from withdrawal of life-sustaining treatment (WLST) decisions based on poor neurological prognostication and the family's intention. Thus, accurate prognostication is crucial to avoid premature WLST decisions. However, targeted temperature management (TTM) with sedation or neuromuscular blockade against shivering significantly affects early prognostication. In this study, we investigated whether heart rate variability (HRV) analysis could prognosticate poor neurological outcome in comatose patients undergoing hypothermic TTM., Methods: Between January 2015 and December 2017, adult patients with out-of-hospital sudden cardiac arrest, successfully resuscitated in the emergency department and admitted to the intensive care unit of the Niigata University in Japan, were prospectively included. All patients had an initial Glasgow Coma Scale motor score of 1 and received hypothermic TTM (at 34 °C). Twenty HRV-related variables (deceleration capacity; 4 time-, 3 geometric-, and 7 frequency-domain; and 5 complexity variables) were computed based on RR intervals between 0:00 and 8:00 am within 24 h after return of spontaneous circulation (ROSC). Based on Glasgow Outcome Scale (GOS) at 2 weeks after ROSC, patients were divided into good outcome (GOS 1-2) and poor outcome (GOS 3-5) groups., Results: Seventy-six patients were recruited and allocated to the good (n = 22) or poor (n = 54) outcome groups. Of the 20 HRV-related variables, ln very-low frequency (ln VLF) power, detrended fluctuation analysis (DFA) (α1), and multiscale entropy (MSE) index significantly differed between the groups (p = 0.001), with a statistically significant odds ratio (OR) by univariate logistic regression analysis (p = 0.001). Multivariate logistic regression analysis of the 3 variables identified ln VLF power and DFA (α1) as significant predictors for poor outcome (OR = 0.436, p = 0.006 and OR = 0.709, p = 0.024, respectively). The area under the receiver operating characteristic curve for ln VLF power and DFA (α1) in predicting poor outcome was 0.84 and 0.82, respectively. In addition, the minimum value of ln VLF power or DFA (α1) for the good outcome group predicted poor outcome with sensitivity = 61% and specificity = 100%., Conclusions: The present data indicate that HRV analysis could be useful for prognostication for comatose patients during hypothermic TTM.

Background: The high prevalence of physician burnout, particularly in emergency medicine, has garnered national attention in recent years. Objective means of measuring stress while at work can facilitate research into stress reduction interventions, and wearable photoplethysmography (PPG) technology has been proposed as a potential solution. However, the use of low-burden wearable biosensors to study training and clinical practice among emergency physicians (EP) remains untested., Objective: This pilot study aimed to (1) determine the feasibility of recording on-shift photoplethysmographic data from EP, (2) assess the quality of these data, and (3) calculate standard pulse rate variability (PRV) metrics from the acquired dataset and examine patterns in these variables over the course of an academic year., Methods: A total of 21 EP wore PPG biosensors on their wrists during clinical work in the emergency department during a 9-hour shift. Recordings were collected during the first quarter of the academic year, then again during the fourth quarter of the same year for comparison. The overall rate of usable data collection per time was computed. Standard pulse rate (PR) and PRV metrics from these two time points were calculated and entered into Student t tests., Results: More than 400 hours of data were entered into these analyses. Interpretable data were captured during 8.54% of the total recording time overall. In the fourth quarter of the academic year compared with the first quarter, there was no significant difference in median PR (75.8 vs 76.8; P=.57), mean R-R interval (0.81 vs 0.80; P=.32), SD of R-R interval (0.11 vs 0.11; P=.93), root mean square of successive difference of R-R interval (0.81 vs 0.80; P=.96), low-frequency power (3.5×103 vs 3.4×103; P=.79), high-frequency power (8.5×103 vs 8.3×103; P=.91), or low-frequency to high-frequency ratio (0.42 vs 0.41; P=.43), respectively. Power estimates for each of these tests exceeded .90. A secondary analysis of the resident-only subgroup similarly showed no significant differences over time, despite power estimates greater than .80., Conclusions: Although the use of PPG biosensors to record real-time physiological data from EP while providing clinical care seems operationally feasible, this study fails to support the notion that such an approach can efficiently provide reliable estimates of metrics of interest. No significant differences in PR or PRV metrics were found at the end of the year compared with the beginning. Although these methods may offer useful applications to other domains, it may currently have limited utility in the contexts of physician training and wellness., (©Gregory Andrew Peters, Matthew L Wong, Joshua W Joseph, Leon D Sanchez. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 02.10.2019.)

Background: Wrist-worn activity trackers are popular, and an increasing number of these devices are equipped with heart rate (HR) measurement capabilities. However, the validity of HR data obtained from such trackers has not been thoroughly assessed outside the laboratory setting., Objective: This study aimed to investigate the validity of HR measures of a high-cost consumer-based tracker (Polar A370) and a low-cost tracker (Tempo HR) in the laboratory and free-living settings., Methods: Participants underwent a laboratory-based cycling protocol while wearing the two trackers and the chest-strapped Polar H10, which acted as criterion. Participants also wore the devices throughout the waking hours of the following day during which they were required to conduct at least one 10-min bout of moderate-to-vigorous physical activity (MVPA) to ensure variability in the HR signal. We extracted 10-second values from all devices and time-matched HR data from the trackers with those from the Polar H10. We calculated intraclass correlation coefficients (ICCs), mean absolute errors, and mean absolute percentage errors (MAPEs) between the criterion and the trackers. We constructed decile plots that compared HR data from Tempo HR and Polar A370 with criterion measures across intensity deciles. We investigated how many HR data points within the MVPA zone (≥64% of maximum HR) were detected by the trackers., Results: Of the 57 people screened, 55 joined the study (mean age 30.5 [SD 9.8] years). Tempo HR showed moderate agreement and large errors (laboratory: ICC 0.51 and MAPE 13.00%; free-living: ICC 0.71 and MAPE 10.20%). Polar A370 showed moderate-to-strong agreement and small errors (laboratory: ICC 0.73 and MAPE 6.40%; free-living: ICC 0.83 and MAPE 7.10%). Decile plots indicated increasing differences between Tempo HR and the criterion as HRs increased. Such trend was less pronounced when considering the Polar A370 HR data. Tempo HR identified 62.13% (1872/3013) and 54.27% (5717/10,535) of all MVPA time points in the laboratory phase and free-living phase, respectively. Polar A370 detected 81.09% (2273/2803) and 83.55% (9323/11,158) of all MVPA time points in the laboratory phase and free-living phase, respectively., Conclusions: HR data from the examined wrist-worn trackers were reasonably accurate in both the settings, with the Polar A370 showing stronger agreement with the Polar H10 and smaller errors. Inaccuracies increased with increasing HRs; this was pronounced for Tempo HR., (©Andre Matthias Matthias Müller, Nan Xin Wang, Jiali Yao, Chuen Seng Tan, Ivan Cherh Chiet Low, Nicole Lim, Jeremy Tan, Agnes Tan, Falk Müller-Riemenschneider. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 02.10.2019.)

Objective: The goal of this month-long controlled study was to compare the efficacy of a second antidepressant (sAD) medication with a prescribed regimen of Plants vs. Zombies™ (PvZ), a casual videogame, in reducing treatment-resistant depression symptoms (TRDS) and improving heart rate variability (HRV). Materials and Methods: Approximately ∼8 weeks after beginning antidepressant therapy, participants returned to psychiatrists for evaluation and complained of TRDS. The psychiatrist gave them a choice of self-selecting a sAD medication or playing a prescribed regimen of PvZ as part of a research study. Those who agreed were referred to researchers who then screened them for major depression, the criteria for inclusion. PvZ was prescribed four times per week for 30-45 minutes over 4 weeks. Self-reported data were collected at four different times utilizing the Patient Health Questionnaire-9. HRV, an indicator of autonomic nervous system (ANS) functioning, was also recorded each time. Results: The sAD group's TRDS significantly improved. Remarkably, the PvZ group's TRDS improved significantly beyond the control group at all measurement times except for time 1 or baseline. In addition, a single 30-minute session of playing PvZ was significantly more effective in acutely reducing TRDS when compared with the sAD group that surfed the NIHM website on depression. Changes in HRV parameters indicated increased parasympathetic engagement and ANS balance in the PvZ group compared with the sAD group. Discussion: The findings illustrate the potential of PvZ as an acute and chronic intervention for reducing TRDS. Health care practitioners such as physicians and recreational therapists can consider recommending a regimen of prescribed PvZ play as a method to ameliorate symptoms of depression for those clients who self-select this option. Finally, a psychophysiological method for measuring the efficacy of videogames in reducing TRDS and a means to quantify ANS changes during gameplay are presented.

Objective: Obstructive sleep apnea (OSA) is associated with sympathetic overactivity. Intermittent hypoxemia and repetitive arousals are the proposed mechanisms; however, the main contributing factor for sympathetic overactivity remains to be determined. We therefore investigated the independent effect of apnea or arousal on sympathetic activity in a large cohort of OSA patients using heart rate variability analysis., Methods: Frequency domain heart rate variability parameters were measured and compared between 782 patients with OSA (198 mild, 259 moderate, 325 severe) and 119 non-OSA controls. Univariate analysis was performed to identify associations between clinical and polysomnographic variables and heart rate variability measurements. Multivariate analysis was further performed to determine the potential contributing factors for sympathetic overactivity., Results: Patients with OSA exhibited overall alterations in heart rate variability measures indicating sympathetic overactivity compared to controls, and this tendency was more pronounced in those with severe OSA. Univariate analysis showed that both apnea-hypopnea and arousal indices correlated with increased sympathetic activity and decreased parasympathetic activity. In multivariate analysis, arousal index was found to be more closely associated with sympathetic overactivity than apnea-hypopnea index., Conclusion: The present study showed that arousal may be a more potent contributing factor than apnea for sympathetic overactivity in OSA patients. Our results suggest that arousal index, rather than apnea-hypopnea index, could be considered as a surrogate marker for sympathetic overactivity in OSA. Further longitudinal studies should elucidate causal relationship between arousals and sympathetic overactivity in patients with OSA., (Copyright © 2019 Elsevier B.V. All rights reserved.)

In real world scenarios, the task of estimating heart rate (HR) using video plethysmography (VPG) methods is difficult because many factors could contaminate the pulse signal (i.e., a subjects' movement, illumination changes). This article presents the evaluation of a VPG system designed for continuous monitoring of the user's heart rate during typical human-computer interaction scenarios. The impact of human activities while working at the computer (i.e., reading and writing text, playing a game) on the accuracy of HR VPG measurements was examined. Three commonly used signal extraction methods were evaluated: green (G), green-red difference (GRD), blind source separation (ICA). A new method based on an excess green (ExG) image representation was proposed. Three algorithms for estimating pulse rate were used: power spectral density (PSD), autoregressive modeling (AR) and time domain analysis (TIME). In summary, depending on the scenario being studied, different combinations of signal extraction methods and the pulse estimation algorithm ensure optimal heart rate detection results. The best results were obtained for the ICA method: average RMSE = 6.1 bpm (beats per minute). The proposed ExG signal representation outperforms other methods except ICA ( RMSE = 11.2 bpm compared to 14.4 bpm for G and 13.0 bmp for GRD). ExG also is the best method in terms of proposed success rate metric ( sRate ).

Background: Heart rate variability (HRV) is reduced in cirrhosis and in conditions of systemic inflammation. Whether HRV is associated with cirrhosis decompensation and development of acute-on-chronic liver failure (ACLF) is unknown., Aims: To (a) validate wireless remote HRV monitoring in cirrhosis decompensation; (b) determine if severely reduced HRV is a surrogate for inflammation and progression of cirrhosis decompensation; (c) assess if measuring HRV determines prognosis in cirrhosis decompensation., Methods: One hundred and eleven patients at risk of cirrhosis decompensation at two clinical sites were monitored for HRV. Standard deviation of all normal beat-beat intervals (SDNN) reflecting HRV was assessed using remote monitoring (Isansys Lifetouch) and/or Holter ECG recording. Clinical outcomes and major prognostic scores were recorded during 90-day follow-up., Results: Reduced HRV denoted by lower baseline SDNN, correlated with severity of decompensation (median 14 (IQR 11-23) vs 33 (25-42); P < 0.001, decompensated patients vs stable outpatient cirrhosis). Furthermore, SDNN was significantly lower in patients developing ACLF compared to those with only decompensation (median 10 (IQR9-12) vs 16 (11-24); P = 0.02), and correlated inversely with MELD and Child-Pugh scores, and C-reactive protein (all P < 0.0001) and white cell count (P < 0.001). SDNN predicted disease progression on repeat measures and appeared an independent predictor of 90-day mortality (12 patients). An SDNN cut-off of 13.25 ms had a 98% negative predictive value., Conclusions: This study demonstrates that remote wireless HRV monitoring identifies cirrhosis patients at high risk of developing ACLF and death, and suggests such monitoring might guide the need for early intervention in such patients. Clinical Trial number: NIHR clinical research network CPMS ID 4949., (© 2019 John Wiley & Sons Ltd.)

Aim: Resuscitation of cardiac arrest involves invasive and traumatic interventions and places a large burden on limited EMS resources. Our aim was to identify prehospital cardiac arrests for which resuscitation is extremely unlikely to result in survival to hospital discharge., Methods: We performed a retrospective cohort analysis of all cardiac arrests in San Mateo County, California, for which paramedics were dispatched, from January 1, 2015 to December 31, 2018, using the Cardiac Arrest Registry to Enhance Survival (CARES) database. We described characteristics of patients, arrests, and EMS responses, and used recursive partitioning to develop decision rules to identify arrests unlikely to survive to hospital discharge, or to survive with good neurologic function., Results: From 2015-2018, 1750 patients received EMS dispatch for cardiac arrest in San Mateo County. We excluded 44 patients for whom resuscitation was terminated due to DNR directives. Median age was 69 years (IQR 57-81), 563 (33.0%) patients were female, 816 (47.8%) had witnessed arrests, 651 (38.2%) received bystander CPR, 421 (24.7%) had an initial shockable rhythm, and 1178 (69.1%) arrested at home. A simple rule (non-shockable initial rhythm, unwitnessed arrest, and age 80 or greater) excludes 223 (13.1%) arrests, of whom none survived to hospital discharge., Conclusion: A simple decision rule (non-shockable rhythm, unwitnessed arrest, age ≥ 80) identifies arrests for which resuscitation is futile. If validated, this rule could be applied by EMS policymakers to identify cardiac arrests for which the trauma and expense of resuscitation are extremely unlikely to result in survival., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Background: Automated detection of return of spontaneous circulation (ROSC) is still an unsolved problem during cardiac arrest. Current guidelines recommend the use of capnography, but most automatic methods are based on the analysis of the ECG and thoracic impedance (TI) signals. This study analysed the added value of EtCO 2 for discriminating pulsed (PR) and pulseless (PEA) rhythms and its potential to detect ROSC., Materials and Methods: A total of 426 out-of-hospital cardiac arrest cases, 117 with ROSC and 309 without ROSC, were analysed. First, EtCO 2 values were compared for ROSC and no ROSC cases. Second, 5098 artefact free 3-s long segments were automatically extracted and labelled as PR (3639) or PEA (1459) using the instant of ROSC annotated by the clinician on scene as gold standard. Machine learning classifiers were designed using features obtained from the ECG, TI and the EtCO 2 value. Third, the cases were retrospectively analysed using the classifier to discriminate cases with and without ROSC., Results: EtCO 2 values increased significantly from 41 mmHg 3-min before ROSC to 57 mmHg 1-min after ROSC, and EtCO 2 was significantly larger for PR than for PEA, 46 mmHg/20 mmHg (p < 0.05). Adding EtCO 2 to the machine learning models increased their area under the curve (AUC) by over 2 percentage points. The combination of ECG, TI and EtCO 2 had an AUC for the detection of pulse of 0.92. Finally, the retrospective analysis showed a sensitivity and specificity of 96.6% and 94.5% for the detection of ROSC and no-ROSC cases, respectively., Conclusion: Adding EtCO 2 improves the performance of automatic algorithms for pulse detection based on ECG and TI. These algorithms can be used to identify pulse on site, and to retrospectively identify cases with ROSC., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Several unobtrusive sensors have been tested in studies to capture physiological reactions to stress in workplace settings. Lab studies tend to focus on assessing sensors during a specific computer task, while in situ studies tend to offer a generalized view of sensors' efficacy for workplace stress monitoring, without discriminating different tasks. Given the variation in workplace computer activities, this study investigates the efficacy of unobtrusive sensors for stress measurement across a variety of tasks. We present a comparison of five physiological measurements obtained in a lab experiment, where participants completed six different computer tasks, while we measured their stress levels using a chest-band (ECG, respiration), a wristband (PPG and EDA), and an emerging thermal imaging method (perinasal perspiration). We found that thermal imaging can detect increased stress for most participants across all tasks, while wrist and chest sensors were less generalizable across tasks and participants. We summarize the costs and benefits of each sensor stream, and show how some computer use scenarios present usability and reliability challenges for stress monitoring with certain physiological sensors. We provide recommendations for researchers and system builders for measuring stress with physiological sensors during workplace computer use.

Background: Heart rate variability (HRV) has been emerging in neonatal medicine. It may help for the early diagnosis of pathology and estimation of autonomous maturation. There is a lack of standardization and automation in the selection of the sequences to analyze and some features have not been explored in this specific population. The main objective of this study was to analyze the impact of the time length of the sequences on the estimation of linear and non-linear HRV features, including horizontal visibility graphs (HVG)., Methods: HRV features were repeatedly measured with linear and non-linear methods on 2-, 5-, 10-minute sequences selected from the longest 15-min sequence and recorded on a weekly basis in 39 infants less than 31 weeks at birth. The associations between HRV measurements were analyzed through principal component analysis and k-means clustering. The effects of the time lengths on HRV measurements and post-menstrual age (PMA) were analyzed by linear mixed effect model for repeated measures., Results: The domains of analysis were concordant for their descriptive parameters of short (rMSSD, SD1 and HF) and long-term (SD, SD2 and LF) variability. α1 was correlated with the LF/HF and SD2/SD1. DC and AC were correlated with short-term variability estimates and significantly increased with GA and PMA. Shortening the windows of analysis increased the random measurement error for all the features and increased the bias for all but short term features and HVGs., Conclusion: The linear and non-linear measurements of HRV are correlated each other. Shortening the windows of analysis increased the random error for all the features and increased the bias for all but short term features and HVGs. Short-term HRV can be an index for evaluating the maturation in whatever sequence length., Competing Interests: The authors have declared that no competing interests exist.

Background and objectives: Autism Spectrum Disorder (ASD) is a complex neuro-developmental disorder and it has been suggested that symptoms of ASD are associated with neural networks that regulate the Autonomic Nervous System (ANS). However, the nature of autonomic atypicalities in ASDs remain largely unknown. Measures like Heart Rate Variability (HRV) and urinary Vanillylmandelic Acid (VMA) estimation are sensitive and non-invasive physiological and biochemical indicators of autonomic nervous activity. This study aimed to compare the physiological and biochemical autonomic indices in children with and without ASD. Materials and Methods: In this case-control study, 40 children with autism and 40 Typically Developing (TD) children were recruited. Measures of physiological autonomic index were assessed by the analysis of short term HRV, and the urinary levels of VMA estimation was used as a biochemical autonomic index. Results: Cardiac sympathetic activity assessed by Low Frequency (nu) of HRV was significantly higher in the ASD group in comparison with the TD group ( p = 0.006). On the contrary, both the High Frequency (abs) and (nu) of HRV were found to be significantly lower in autistic children ( p = 0.034 and p = 0.000) than controls. Autistic children also exhibited a significantly higher level ( p = 0.049) of VMA concentration compared to TD children. Conclusion: The study concludes that children with ASD exhibit lower cardio-vagal activity as measured by HRV and increased sympathetic activity as assessed by urinary VMA compared to that of TD children. The core autistic symptoms exhibited by children with ASD could be due to the differences in baseline arousal or stress which might be associated with autonomic dysfunction. Further studies are needed to examine the association of this autonomic dysregulation with ASD symptoms and comorbidities.

In this paper, we report the development of a portable energy-efficient interrogator (Perrogator) for wavelength-based optical sensors. The interrogator is based on a compact solution encompassing a white light source and the spectral convolution between the sensor and a tunable filter, which is acquired by a photodetector, where a microcontroller has two functions: (i) To control the filter tuning and to (ii) acquire the photodetector signal. Then, the data is sent to a single-board computer for further signal processing. Furthermore, the employed single-board computer has a Wi-Fi module, which can be used to send the sensors data to the cloud. The proposed approach resulted in an interrogator with a resolution as high as 3.82 pm (for 15.64 nm sweeping range) and maximum acquisition frequency of about 210 Hz (with lower resolution ~15.30 pm). Perrogator was compared with a commercial fiber Bragg grating (FBG) interrogator for strain measurements and good agreement between both devices was found (1.226 pm/µε for the commercial interrogator and 1.201 pm/µε for the proposed approach with root mean square error of 0.0144 and 0.0153, respectively), where the Perrogator has the additional advantages of lower cost, higher portability and lower energy consumption. In order to demonstrate such advantages in conjunction with the high acquisition frequency allowed us to demonstrate two wearable applications using the proposed interrogation device over FBG and Fabry-Perot interferometer (FPI) sensors. In the first application, an FBG-embedded smart textile for knee angle assessment was used to analyze the gait of a healthy person. Due to the capability of reconstructing the FBG spectra, it was possible to employ a technique based on the FBG wavelength shift and reflectivity to decouple the effects of the bending angle and axial strain on the FBG response. The measurement of the knee angle as well as the estimation of the angular and axial displacements on the grating that can be correlated to the variations of the knee center of rotation were performed. In the second application, a FPI was embedded in a chest band for simultaneous measurement of breath and heart rates, where good agreement (error below 5%) was found with the reference sensors in all analyzed cases.

Aim: Obstructive sleep apnea (OSA) is associated with increased variability in nocturnal blood pressure (BP). Calcium channel blockers (CCB) are superior to other classes of antihypertensives in decreasing BP variability. We investigated whether OSA severity is associated with nocturnal BP variability in older hypertensive patients treated with CCB., Methods: We measured home systolic and diastolic BP and pulse rate (PR) automatically during sleep at an interval of an hour once a week using an electronic sphygmomanometer in 29 hypertensive patients (aged ≥65 years) receiving CCB. We calculated the coefficient of variation (CV) from four consecutive measurements. All patients underwent a home-based portable sleep study., Results: We found no difference in PR, BP or CV of BP between the patients with no-to-mild OSA and with moderate-to-severe OSA, categorized by the respiratory disturbance index (RDI) and 3% oxygen desaturation index (ODI). The CV of PR in patients with moderate-to-severe OSA was higher than the patients with no-to-mild OSA categorized by 3% ODI (P = 0.01). Body mass index was correlated with RDI and 3% ODI (r = 0.56 and 0.43, respectively). The CV of BP did not correlate to RDI or 3% ODI. The CV of PR was positively correlated both with RDI and with 3% ODI (r = 0.41 and 0.42, respectively)., Conclusions: The severity of OSA was associated with PR variability, but not with BP variability, in older patients receiving CCB. Our results suggest the need for future studies to determine whether CCB can suppress the influence of OSA on BP fluctuation during sleep. Geriatr Gerontol Int 2019; 19: 604-610., (© 2019 Japan Geriatrics Society.)

Background: Moderate-vigorous physical activity (MVPA) offers extensive health benefits but is neglected by many. As a result, a wide body of research investigating physical activity behavior change has been conducted. As many of these studies transition from paper-based methods of MVPA data collection to fitness trackers, a series of challenges arise in extracting insights from these new data., Objective: The objective of this research was to develop a framework for preprocessing and extracting MVPA trends from wearable fitness tracker data to support MVPA behavior change studies., Methods: Using heart rate data collected from fitness trackers, we propose Physical Activity Trend eXtraction (PATX), a framework that imputes missing data, recalculates personalized target heart zones, and extracts MVPA trends. We tested our framework on a dataset of 123 college study participants observed across 2 academic years (18 months) using Fitbit Charge HRs. To demonstrate the value of our frameworks' output in supporting MVPA behavior change studies, we applied it to 2 case studies., Results: Among the 123 participants analyzed, PATX labeled 41 participants as experiencing a significant increase in MVPA and 44 participants who experienced a significant decrease in MVPA, with significance defined as P<.05. Our first case study was consistent with previous works investigating the associations between MVPA and mental health. Whereas the second, exploring how individuals perceive their own levels of MVPA relative to their friends, led to a novel observation that individuals were less likely to notice changes in their own MVPA when close ties in their social network mimicked their changes., Conclusions: By providing meaningful and flexible outputs, PATX alleviates data concerns common with fitness trackers to support MVPA behavior change studies as they shift to more objective assessments of MVPA., (©Louis Faust, Cheng Wang, David Hachen, Omar Lizardo, Nitesh V Chawla. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 12.03.2019.)

Background: Wrist-worn smart watches and fitness monitors (ie, wearables) have become widely adopted by consumers and are gaining increased attention from researchers for their potential contribution to naturalistic digital measurement of health in a scalable, mobile, and unobtrusive way. Various studies have examined the accuracy of these devices in controlled laboratory settings (eg, treadmill and stationary bike); however, no studies have investigated the heart rate accuracy of wearables during a continuous and ecologically valid 24-hour period of actual consumer device use conditions., Objective: The aim of this study was to determine the heart rate accuracy of 2 popular wearable devices, the Apple Watch 3 and Fitbit Charge 2, as compared with the gold standard reference method, an ambulatory electrocardiogram (ECG), during consumer device use conditions in an individual. Data were collected across 5 daily conditions, including sitting, walking, running, activities of daily living (ADL; eg, chores, brushing teeth), and sleeping., Methods: One participant, (first author; 29-year-old Caucasian male) completed a 24-hour ecologically valid protocol by wearing 2 popular wrist wearable devices (Apple Watch 3 and Fitbit Charge 2). In addition, an ambulatory ECG (Vrije Universiteit Ambulatory Monitoring System) was used as the gold standard reference method, which resulted in the collection of 102,740 individual heartbeats. A single-subject design was used to keep all variables constant except for wearable devices while providing a rapid response design to provide initial assessment of wearable accuracy for allowing the research cycle to keep pace with technological advancements. Accuracy of these devices compared with the gold standard ECG was assessed using mean error, mean absolute error, and mean absolute percent error. These data were supplemented with Bland-Altman analyses and concordance class correlation to assess agreement between devices., Results: The Apple Watch 3 and Fitbit Charge 2 were generally highly accurate across the 24-hour condition. Specifically, the Apple Watch 3 had a mean difference of -1.80 beats per minute (bpm), a mean absolute error percent of 5.86%, and a mean agreement of 95% when compared with the ECG across 24 hours. The Fitbit Charge 2 had a mean difference of -3.47 bpm, a mean absolute error of 5.96%, and a mean agreement of 91% when compared with the ECG across 24 hours. These findings varied by condition., Conclusions: The Apple Watch 3 and the Fitbit Charge 2 provided acceptable heart rate accuracy (<±10%) across the 24 hour and during each activity, except for the Apple Watch 3 during the daily activities condition. Overall, these findings provide preliminary support that these devices appear to be useful for implementing ambulatory measurement of cardiac activity in research studies, especially those where the specific advantages of these methods (eg, scalability, low participant burden) are particularly suited to the population or research question., (©Benjamin W Nelson, Nicholas B Allen. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 11.03.2019.)

The purpose of this study was to measure emotional contagion, determine its direction, and compare the intensity between positive and negative contagion using the synchronization of heart rhythm pattern (HRP). A total of 64 undergraduate students (32 women and 32 men) participated in the experiment, and were randomly categorized as either leaders or followers. Followers were required to imitate the facial expression (happy and sad) of the leader (emotional contagion) or of a facial image (emotional non-contagion). We found that emotional contagion significantly increased the correlation coefficient between leaders and followers' HRP for both positive and negative emotions, but emotional non-contagion did not. There was no significant difference in leaders' HRP before and after contagion, while followers' HRP changed significantly. During emotional contagion, the correlation coefficient for negative emotion was significantly higher than for positive emotion. The proposed method could measure low or high emotional contagion and determine its direction quantitatively. In our application study, a sales manager (leader) transmitted a positive emotion to a sales employee (follower), and the groups are organized as HEC or LEC (high or low emotional contagion) groups by evaluating the intensity of emotional contagion based on HRP synchrony between them. HEC group's performance was enhanced compared to the LEC group., (Copyright © 2018. Published by Elsevier Inc.)

Traditional sensory tests rely on conscious and self-reported responses from participants. The integration of non-invasive biometric techniques, such as heart rate, body temperature, brainwaves and facial expressions can gather more information from consumers while tasting a product. The main objectives of this study were i) to assess significant differences between beers for all conscious and unconscious responses, ii) to find significant correlations among the different variables from the conscious and unconscious responses and iii) to develop a model to classify beers according to liking using only the unconscious responses. For this study, an integrated camera system with video and infrared thermal imagery (IRTI), coupled with a novel computer application was used. Videos and IRTI were automatically obtained while tasting nine beers to extract biometrics (heart rate, temperature and facial expressions) using computer vision analysis. Additionally, an EEG mobile headset was used to obtain brainwave signals during beer consumption. Consumers assessed foam, color, aroma, mouthfeel, taste, flavor and overall acceptability of beers using a 9-point hedonic scale with results showing a higher acceptability for beers with higher foamability and lower bitterness. i) There were non-significant differences among beers for the emotional and physiological responses, however, significant differences were found for the cognitive and self-reported responses. ii) Results from principal component analysis explained 65% of total data variability and, along with the covariance matrix (p < 0.05), showed that there are correlations between the sensory responses of participants and the biometric data obtained. There was a negative correlation between body temperature and liking of foam height and stability, and a positive correlation between theta signals and bitterness. iii) Artificial neural networks were used to develop three models with high accuracy to classify beers according to level of liking (low and high) of three sensory descriptors: carbonation mouthfeel (82%), flavor (82%) and overall liking (81%). The integration of both sensory and biometric responses for consumer acceptance tests showed to be a reliable tool to be applied to beer tasting to obtain more information from consumers physiology, behavior and cognitive responses., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Background: In 2015, an estimated 303 000 women died in pregnancy and childbirth. Obstetric haemorrhage, sepsis, and hypertensive disorders of pregnancy account for more than 50% of maternal deaths worldwide. There are effective treatments for these pregnancy complications, but they require early detection by measurement of vital signs and timely administration to save lives. The primary aim of this trial was to determine whether implementation of the CRADLE Vital Sign Alert and an education package into community and facility maternity care in low-resource settings could reduce a composite of all-cause maternal mortality or major morbidity (eclampsia and hysterectomy)., Methods: We did a pragmatic, stepped-wedge, cluster-randomised controlled trial in ten clusters across Africa, India, and Haiti, introducing the device into routine maternity care. Each cluster contained at least one secondary or tertiary hospital and their main referral facilities. Clusters crossed over from existing routine care to the CRADLE intervention in one of nine steps at 2-monthly intervals, with CRADLE devices replacing existing equipment at the randomly allocated timepoint. A computer-generated randomly allocated sequence determined the order in which the clusters received the intervention. Because of the nature of the intervention, this trial was not masked. Data were gathered monthly, with 20 time periods of 1 month. The primary composite outcome was at least one of eclampsia, emergency hysterectomy, and maternal death. This study is registered with the ISRCTN registry, number ISRCTN41244132., Findings: Between April 1, 2016, and Nov 30, 2017, among 536 223 deliveries, the primary outcome occurred in 4067 women, with 998 maternal deaths, 2692 eclampsia cases, and 681 hysterectomies. There was an 8% decrease in the primary outcome from 79·4 per 10 000 deliveries pre-intervention to 72·8 per 10 000 deliveries post-intervention (odds ratio [OR] 0·92, 95% CI 0·86-0·97; p=0·0056). After planned adjustments for variation in event rates between and within clusters over time, the unexpected degree of variability meant we were unable to judge the benefit or harms of the intervention (OR 1·22, 95% CI 0·73-2·06; p=0·45)., Interpretation: There was an absolute 8% reduction in primary outcome during the trial, with no change in resources or staffing, but this reduction could not be directly attributed to the intervention due to variability. We encountered unanticipated methodological challenges with this trial design, which can provide valuable learning for future research and inform the trial design of future international stepped-wedge trials., Funding: Newton Fund Global Research Programme: UK Medical Research Council; Department of Biotechnology, Ministry of Science & Technology, Government of India; and UK Department of International Development., (Copyright © 2019 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Background: Mobile phone apps capable of monitoring arrhythmias and heart rate (HR) are increasingly used for screening, diagnosis, and monitoring of HR and rhythm disorders such as atrial fibrillation (AF). These apps involve either the use of (1) photoplethysmographic recording or (2) a handheld external electrocardiographic recording device attached to the mobile phone or wristband., Objective: This review seeks to explore the current state of mobile phone apps in cardiac rhythmology while highlighting shortcomings for further research., Methods: We conducted a narrative review of the use of mobile phone devices by searching PubMed and EMBASE from their inception to October 2018. Potentially relevant papers were then compared against a checklist for relevance and reviewed independently for inclusion, with focus on 4 allocated topics of (1) mobile phone monitoring, (2) AF, (3) HR, and (4) HR variability (HRV)., Results: The findings of this narrative review suggest that there is a role for mobile phone apps in the diagnosis, monitoring, and screening for arrhythmias and HR. Photoplethysmography and handheld electrocardiograph recorders are the 2 main techniques adopted in monitoring HR, HRV, and AF., Conclusions: A number of studies have demonstrated high accuracy of a number of different mobile devices for the detection of AF. However, further studies are warranted to validate their use for large scale AF screening., (©Ka Hou Christien Li, Francesca Anne White, Timothy Tipoe, Tong Liu, Martin CS Wong, Aaron Jesuthasan, Adrian Baranchuk, Gary Tse, Bryan P Yan. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 15.02.2019.)

Radial pulse spectrum has been shown to correlate with coronary stenosis in patients with type 2 diabetes mellitus (T2DM). In academia, it has not been demonstrated that the radial artery pulse spectrum is an independent risk factor for major adverse cardiovascular events (MACE), including myocardial infarction, stroke, and all-cause mortality. The primary objective of this study is to assess the risk of MACE, in patients with T2DM and to determine if an increase in MACE would be associated with a first harmonic (C1) increase in the radial artery pulse. 1972 consecutive patients with T2DM were enrolled. All subjects received measurements of radial pulse waves at baseline. Harmonic analysis of radial pressure wave was performed. The hazard ratios for MACE and its 95% confident interval were estimated using Cox proportional hazard model. The follow-up period lasted for one year. MACE was detected in 232 (11.8%) of those with T2DM. The log-rank test demonstrated that the cumulative incidence of patients with C1 above 0.96 was greater than those with C1 bellow 0.96. Comparing the patients with C1 smaller than first quartile to the patients with C1 greater than third quartile, higher C1 increased the cardiovascular risks as follows: MACE (Hazard ratio,1.93; 95% CI,1.31-2.86), stroke (Hazard ratio, 1.61; 95% CI, 0.90-2.90), myocardial infarction (Hazard ratio, 2.23; 95% CI, 1.33-3.74). The risk for the composite MACE increased continuously as C1 increased (P < 0.001 for trend). The hazard ratio and trend for all-cause mortality were not significant. Increased C1 resulted in increased risk for nonfatal stroke, and nonfatal myocardial infarction among patients with T2DM. Our results indicate that the degree of C1 is a risk factor for nonfatal MACE. Therefore, the radial pulse spectrum could identify patients with T2DM at high risk of nonfatal MACE., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Zebrafish (Danio rerio) is a powerful animal model used in many areas of genetics and disease research. Despite its advantages for cardiac research, the heartbeat pattern of zebrafish larvae under different stress conditions is not well documented quantitatively. Several effective automated heartbeat detection methods have been developed to reduce the workload for larva heartbeat analysis. However, most require complex experimental setups and necessitate direct observation of the larva heart. In this paper, we propose the Zebrafish Heart Rate Automatic Method (Z-HRAM), which detects and tracks the heartbeats of immobilized, ventrally positioned zebrafish larvae without direct larva heart observation. Z-HRAM tracks localized larva body deformation that is highly correlated with heart movement. Multiresolution dense optical flow-based motion tracking and principal component analysis are used to identify heartbeats. Here, we present results of Z-HRAM on estimating heart rate from video recordings of seizure-induced larvae, which were of low resolution (1024 × 760) and low frame rate (3 to 4 fps). Heartbeats detected from Z-HRAM were shown to correlate reliably with those determined through corresponding electrocardiogram and manual video inspection. We conclude that Z-HRAM is a robust, computationally efficient, and easily applicable tool for studying larva cardiac function in general laboratory conditions. Graphical abstract Flowchart of the automatic zebrafish heartbeat detection.

Objective: Non-contact physiological measurement is a growing research area that allows capturing vital signs such as heart rate (HR) and breathing rate (BR) comfortably and unobtrusively with remote devices. However, most of the approaches work only in bright environments in which subtle photoplethysmographic and ballistocardiographic signals can be easily analyzed and/or require expensive and custom hardware to perform the measurements., Approach: This work introduces a low-cost method to measure subtle motions associated with the carotid pulse and breathing movement from the neck using near-infrared (NIR) video imaging. A skin reflection model of the neck was established to provide a theoretical foundation for the method. In particular, the method relies on template matching for neck detection, principal component analysis for feature extraction, and hidden Markov models for data smoothing., Main Results: We compared the estimated HR and BR measures with ones provided by an FDA-cleared device in a 12-participant laboratory study: the estimates achieved a mean absolute error of 0.36 beats per minute and 0.24 breaths per minute under both bright and dark lighting., Significance: This work advances the possibilities of non-contact physiological measurement in real-life conditions in which environmental illumination is limited and in which the face of the person is not readily available or needs to be protected. Due to the increasing availability of NIR imaging devices, the described methods are readily scalable.

Objective: This work aims to validate a set of data processing methods for variability metrics, which hold promise as potential indicators for autonomic function, prediction of adverse cardiovascular outcomes, psychophysiological status, and general wellness. Although the investigation of heart rate variability (HRV) has been prevalent for several decades, the methods used for preprocessing, windowing, and choosing appropriate parameters lacks consensus among academic and clinical investigators. Moreover, many of the important steps are omitted from publications, preventing reproducibility., Approach: To address this, we have compiled a comprehensive and open-source modular toolbox for calculating HRV metrics and other related variability indices, on both raw cardiovascular time series and RR intervals. The software, known as the PhysioNet Cardiovascular Signal Toolbox, is implemented in the MATLAB programming language, with standard (open) input and output formats, and requires no external libraries. The functioning of our software is compared with other widely used and referenced HRV toolboxes to identify important differences., Main Results: Our findings demonstrate how modest differences in the approach to HRV analysis can lead to divergent results, a factor that might have contributed to the lack of repeatability of studies and clinical applicability of HRV metrics., Significance: Existing HRV toolboxes do not include standardized preprocessing, signal quality indices (for noisy segment removal), and abnormal rhythm detection and are therefore likely to lead to significant errors in the presence of moderate to high noise or arrhythmias. We therefore describe the inclusion of validated tools to address these issues. We also make recommendations for default values and testing/reporting.

Purpose: To find the shortest, acceptable stabilization period before recording resting, supine ultra-short-term Ln RMSSD and heart rate (HR)., Method: Thirty endurance-trained male athletes (age 24.1 ± 2.3 years, maximal oxygen consumption (VO2max) 64.1 ± 6.6 ml·kg-1·min-1) and 30 male students (age 23.3 ± 1.8 years, VO2max 52.8 ± 5.1 ml·kg-1·min-1) were recruited. Upon awaking at home, resting, supine RR intervals were measured continuously for 10 min using a Polar V800 HR monitor. Ultra-short-term Ln RMSSD and HR values were calculated from 1-min RR interval segments after stabilization periods from 0 to 4 min in 0.5 min increments and were compared with reference values calculated from 5-min segment after 5-min stabilization. Systematic bias and intraclass correlation coefficients (ICC) including 90% confidence intervals (CI) were calculated and magnitude based inference was conducted., Results: The stabilization periods of up to 30 s for athletes and up to 60 s for students showed positive (possibly to most likely) biases for ultra-short-term Ln RMSSD compared with reference values. Stabilization periods of 60 s for athletes and 90 s for students showed trivial biases and ICCs were 0.84; 90% CI 0.72 to 0.91, and 0.88; 0.79 to 0.94, respectively. For HR, biases were trivial and ICCs were 0.93; 0.88 to 0.96, and 0.93; 0.88 to 0.96, respectively., Conclusion: The shortest stabilization period required to stabilize Ln RMSSD and HR was set at 60 s for endurance-trained athletes and 90 s for university students., Competing Interests: The authors have declared that no competing interests exist.

Objective: This study participated in the 2017 PhysioNet/CinC Challenge dedicated to the classification of atrial fibrillation (AF), normal sinus rhythm (Normal), other arrhythmia (Other) and strong noise, using single-lead electrocardiogram (ECG) recordings with a duration  <60 s. The aim is to apply a linear threshold-based strategy for arrhythmia classification, ranking the most powerful time domain ECG features that could be easily reproduced on any platform., Approach: An algorithm for time domain ECG analysis was designed to extract 44 features with focus on the following: noise detection; heart rate variability (HRV) analysis; beat morphology analysis and delineation of P-, QRS-, and T-waves in the robust average beat; detection of atrial activity by the presence of P-waves in the average beat and atrial fibrillatory waves (f-waves) during TQ intervals. A linear discriminant analysis (LDA) classifier was optimized on the Challenge training set (8528 ECGs) by stepwise selection of a nonredundant feature set until maximization of the Challenge F1 score. Heart rate (HR) was an independent factor for the LDA classifier design, particular to bradycardia (HR  ⩽  50 bpm), normal rhythm (HR  =  50-100 bpm), tachycardia (HR  ⩾  100 bpm)., Main Results: The algorithm obtained official Challenge F1 scores of 0.80 (Overall), 0.90 (Normal), 0.81 (AF), 0.70 (Other), and 0.54 (Noise) on the hidden Challenge test set (3658 ECGs). This is equivalent to a true positive rate (TPR)  =  90.1% (Normal), 81.5% (AF), 67.7% (Other), and 69.5% (Noise), and a false positive rate (FPR)  =  13.6% (Normal), 2.3% (AF), 7.7% (Other), and 1.5% (Noise)., Significance: The top five features, which together contributed to about 94% of the maximal F1 score were ranked: (1) proportion of RR intervals differing by  >50 ms from the preceding RR interval; (2) Poincaré plot geometry estimated by the ratio of the minor-to-major semi-axes of the fitted ellipse; (3) P-wave presence in the average beat; (4) mean percentage of the RR interval first differences; and (5) mean correlation of all beats against the average beat. The global rank of feature extraction methods highlighted that HRV alone was able to provide 92.5% of the maximal F1 score (0.74 versus 0.8). The added value of more complex ECG morphology analysis was less significant for Normal, AF, and Other rhythms (+0.02 to 0.08 points) than for Noise (+0.19 points); however, these were indispensable for wearable ECG recording devices with frequent artefact disturbance.

Participation in organized sports makes a significant contribution to youth development, but places youth at a higher risk for sustaining a concussion. To date, return-to-activity decision-making has been anchored in the monitoring of self-reported concussion symptoms and neurocognitive testing. However, multi-modal assessments that corroborate objective physiological measures with traditional subjective symptom reporting are needed and can be valuable. Heart rate variability (HRV) is a non-invasive physiological indicator of the autonomic nervous system, capturing the reciprocal interplay between the sympathetic and parasympathetic nervous systems. There is a dearth of literature exploring the effect of concussion on HRV in youth athletes, and developmental differences preclude the application of adult findings to a pediatric population. Further, the current state of HRV methodology has primarily included short-term (5-15 min) recordings, by using resting state or short-term physical exertion testing to elucidate changes following concussion. The novelty in utilizing a 24 h recording methodology is that it has the potential to capture natural variation in autonomic function, directly related to the activities a youth athlete performs on a regular basis. Within a prospective, longitudinal research setting, this novel approach to quantifying autonomic function can provide important information regarding the recovery trajectory, alongside traditional self-report symptom measures. Our objectives regarding a 24 h recording methodology were to (1) evaluate the physiological effects of a concussion in youth athletes, and (2) describe the trajectory of physiological change, while considering the resolution of self-reported post-concussion symptoms. To achieve these objectives, non-invasive sensor technology was implemented. The raw beat-to-beat time intervals captured can be transformed to derive time domain and frequency domain measures, which reflect an individual's ability to adapt and be flexible to their ever-changing environment. By using non-invasive heart rate technology, autonomic function can be quantified outside of a traditional controlled research setting.

Objective: Use of wearable ECG devices for arrhythmia screening is limited due to poor signal quality, small number of leads and short records, leading to incorrect recognition of pathological events. This paper introduces a novel approach to classification (normal/'N', atrial fibrillation/'A', other/'O', and noisy/'P') of short single-lead ECGs recorded by wearable devices., Approach: Various rhythm and morphology features are derived from the separate beats ('local' features) as well as the entire ECGs ('global' features) to represent short-term events and general trends respectively. Various types of atrial and ventricular activity, heart beats and, finally, ECG records are then recognised by a multi-level approach combining a support vector machine (SVM), decision tree and threshold-based rules., Main Results: The proposed features are suitable for the recognition of 'A'. The method is robust due to the noise estimation involved. A combination of radial and linear SVMs ensures both high predictive performance and effective generalisation. Cost-sensitive learning, genetic algorithm feature selection and thresholding improve overall performance. The generalisation ability and reliability of this approach are high, as verified by cross-validation on a training set and by blind testing, with only a slight decrease of overall F1-measure, from 0.84 on training to 0.81 on the tested dataset. 'O' recognition seems to be the most difficult (test F1-measures: 0.90/'N', 0.81/'A' and 0.72/'O') due to high inter-patient variability and similarity with 'N'., Significance: These study results contribute to multidisciplinary areas, focusing on creation of robust and reliable cardiac monitoring systems in order to improve diagnosis, reduce unnecessary time-consuming expert ECG scoring and, consequently, ensure timely and effective treatment.

Objective: Cuffless blood-pressure (BP) estimation has attracted widespread interest owing to its potential for long-term, non-invasive BP monitoring. But it is still impractical in clinical settings, mainly owing to ongoing challenges with respect to accuracy in hypertensive patients. To better estimate the BP, the current study proposes a new cuffless estimation method that includes a sympathetic tone, which has been reported with a varied pattern in hypertensive patients., Approach: First, the heart-rate variability of all subjects is investigated, and a new parameter, the heart-rate power spectrum ratio (HPSR), is proposed to indicate BP dynamics under sympathetic regulation. Then, a new BP estimation model is constructed by integrating HPSP with the pulse transit time (PTT) and photoplethysmography intensity ratio. The estimation accuracy is further evaluated by making comparisons with the standard sphygmomanometer BP on 60 subjects (29 hypertensive and 31 normotensive)., Main Results: A significant increase in HPSR was observed in hypertensive patients compared to normotensive subjects. Of the 60 subjects, the estimation accuracy was 0.73  ±  10.04 mmHg for systolic BP (SBP) and 0.90  ±  7.10 mmHg for diastolic BP (DBP) in hypertensive patients, which is comparable to 0.54  ±  7.52 mmHg for SBP and 0.82  ±  6.20 mmHg for DBP in normotensive subjects. Furthermore, the proposed method overperformed the traditional PTT-based algorithm by reducing the 3 mmHg error in the standard deviation in hypertensive patients., Significance: The results of the current study demonstrate that the inclusion of factors associated with autonomic activities in the BP estimation model would improve the BP estimation accuracy, especially for hypertensive subjects.

We discovered that impulse-radio ultra-wideband (IR-UWB) radar could recognize cardiac motions in a non-contact fashion. Therefore, we measured the heart rate (HR) and rhythms using an IR-UWB radar sensor and evaluated the validity and reliability of the measurements in comparison to electrocardiography. The heart beats were measured in 6 healthy volunteers (18 samples) with normal sinus rhythm (NSR) and 16 patients (36 samples) with atrial fibrillation (AF) using both an IR-UWB radar sensor and electrocardiography simultaneously. The participants hold their breath for 20 seconds during the data acquisition. In subjects with NSR, there was excellent agreement of HR (intraclass correlation coefficient [ICC] 0.856), average R-R interval (ICC 0.997) and individual R-R intervals between the two methods (ICC 0.803). In subjects with AF, HR (ICC 0.871) and average R-R interval (ICC 0.925) from the radar sensor also agreed well with those from electrocardiography, though there was a small disagreement in the individual R-R intervals between the two methods (ICC 0.697). The rhythms computed by the signal-processing algorithm showed good agreement between the two methods (Cohen's Kappa 0.922). The IR-UWB radar sensor is precise and accurate for assessing HR and rhythms in a non-contact fashion.