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Despite advances in understanding the cause of ischemic stroke, cryptogenic stroke remains a diagnostic and therapeutic challenge for clinicians. Approximately 15% to 40% of all ischemic strokes have no identifiable cause. CS is a diagnosis of exclusion after completing the standard stroke work-up. Further investigation needs to be tailored individually according to results of the clinical evaluation so appropriate secondary prevention strategies can be applied.

The QT interval represents the duration of ventricular depolarization and repolarization. It is measured from the beginning of the QRS complex to the end of the T wave. Prolongation of the QT interval may be congenital or acquired. This increases the risk of polymorphic ventricular tachycardia (i.e torsades de pointes) and cardiac arrest. To increase the awareness of this life-threatening phenomenon I outline an illustrative case in which acquired prolongation of the QT interval due to electrolyte derangement and administration of ciprofloxacin resulted in cardiac arrest due to torsade de pointes. Management of a patient with a long QT syndrome includes Immediate cessation of drugs that prolong the QT interval; cardiac monitoring, serial 12 lead ECGs and transthoracic echocardiography; measurement of serum electrolytes; intravenous potassium replacement; intravenous magnesium replacement; beta-blockade. Causes of acquired prolongation of the QT interval are common in critically ill patients. It is important to recognize this and consider screening with 12 lead ECG to reduce the risk of life-threatening ventricular arrhythmias.

So far, there has been no generally accepted diagnostic and therapeutic algorithm for patients with embolic stroke of undetermined source (ESUS). As recent clinical trials on secondary stroke prevention in ESUS did not support the use of oral anticoagulation and the concept of ESUS comprises heterogeneous subgroups of patients, including a wide age range, concomitant patent foramen ovale (PFO), variable cardiovascular risk factors as well as a variable probability for atrial fibrillation (AF), an individualized clinical approach is needed. In this context, we here present a case of recurrent stroke in a young patient with ESUS and PFO. During treatment according to our Catch-up-ESUS registry study, prolonged cardiac monitoring diagnosed AF, and PFO closure was omitted.

Purpose Continuous monitoring of patient movement is crucial to administering safe radiation therapy (RT). Conventional optical approaches often cannot be used when the patient's surface is blocked by immobilization devices. Millimeter waves (mmWaves) are capable of penetrating nonconductive objects. In this study, we investigated using mmWave technology to monitor patient surface displacements, as well as breathing and cardiac phases, through clothing and body fixtures. Methods A mmWave device was mounted inside the bore of a ring-based radiotherapy linear accelerator and pointed at a reflective surface on top of the couch. Measurements were obtained at displacements of 10, 7.5, 5.0, 2.5, and 1.0 mm at heights 100, 150, and 200 mm below isocenter. Submillimeter displacements were performed at a height of 200 mm. Additionally, millimeter and submillimeter displacements were measured with and without a gown and body mold placed between the surface and the sensor. The device was programmed to transmit chirp signals at 77-81 GHz. The subject's surface was detected by fast Fourier transform (FFT) of the reflected chirp signal within a rough range bin. Fine displacements within that range bin were calculated through phase extraction and phase demodulation. The displacement data were sent through two separate bandpass filters with passbands of 0.1-0.6 and 0.8-2.0 Hz to obtain the subject's breathing and cardiac waveforms, respectively. The breathing and cardiac measurements were compared to those of a Vernier Respiration Monitor Belt and an electrocardiogram (EKG), respectively, to assess validity. Results The device was able to detect millimeter and submillimeter displacements as small as 0.1 mm, as well as monitor displacement with an accuracy within 1 mm in the presence of an obstructive object. The device's breathing and cardiac waveforms exhibited a strong phase correlation between the respiration monitor belt (ρ = 0.9156) and EKG (ρ = 0.7895), respectively. Conclusions The mmWave device can monitor surface displacements with an accuracy better than 0.1 mm without obstructions and better than 1 mm with obstructions. It can also provide real-time monitoring of breathing and cardiac waveforms simultaneously with high correlation with traditional respiratory and cardiac monitoring devices. Overall, mmWave technology demonstrates potential for motion monitoring in the field of radiation oncology.

Objective The American Society of Clinical Oncology (ASCO) 2017 guidelines on cardiac monitoring during cancer treatments identified patients receiving thoracic radiation (TRT) ≥30 Gy (heart in field) at increased risk for developing radiation-induced heart disease (RIHD). ASCO encouraged clinicians to actively screen and monitor for baseline modifiable cardiac risk factors and therapy-induced cardiotoxicity in this high-risk population. Coronary artery calcium (CAC) is an independent risk factor for adverse cardiac events that can be mitigated with preventative medical therapy. It is unclear whether radiation oncologists (ROs) are aware of ASCO guidelines or the implications of CAC observed on computed tomographic scans. We report on practice patterns, perceptions, and experiences of cardiac monitoring for patients receiving definitive TRT, excluding breast patients. Materials and methods A 28-question survey was emailed to United States ROs 3 times from September 2018 to January 2019. Results There were 162 respondents from 42 states, 51% in academic practice. Most ROs (81%) were not aware of the ASCO guidelines. Only 24% agreed with the guidelines, only 27% believed symptomatic RIHD could manifest within 2 years of TRT, and 69% thought there was a lack of strong evidence for type and timing of cardiac monitoring tests. If CAC was evident on computed tomographic scans, 40% took no further action to inform the patient or referring doctor. Conclusions This survey highlights a critical gap in knowledge about cardiac monitoring and potentially life-saving opportunities for preventive cardiac medical management. Future studies focusing on timing and detection of RIHD may elucidate the utility of cardiac monitoring for TRT patients.

A 39-year-old pregnant patient with acute appendicitis was planned for emergency laparoscopic appendectomy in the second trimester of pregnancy. Preoperative two-dimensional transthoracic echocardiography revealed asymptomatic cor triatriatum sinisiter (CTS), dividing left atrium into two chambers. There was no associated cardiac anomaly, wall motion abnormality, or pulmonary hypertension. We report the case of a pregnant patient with CTS who uneventfully underwent laparoscopic appendectomy without invasive cardiac monitoring using total intravenous anesthesia.

Forcecardiography (FCG) is a novel technique that records the weak forces induced on the chest wall by cardio-respiratory activity, by using specific force sensors. FCG sensors feature a wide frequency band, which allows us to capture respiration, heart wall motion, heart valves opening and closing (similar to the Seismocardiogram, SCG) and heart sounds, all simultaneously from a single contact point on the chest. As a result, the raw FCG sensors signals exhibit a large component related to the respiratory activity, referred to as a Forcerespirogram (FRG), with a much smaller, superimposed component related to the cardiac activity (the actual FCG) that contains both infrasonic vibrations, referred to as LF-FCG and HF-FCG, and heart sounds. Although respiration can be readily monitored by extracting the very low-frequency component of the raw FCG signal (FRG), it has been observed that the respiratory activity also influences other FCG components, particularly causing amplitude modulations (AM). This preliminary study aimed to assess the consistency of the amplitude modulations of the LF-FCG and HF-FCG signals within the respiratory cycle. A retrospective analysis was performed on the FCG signals acquired in a previous study on six healthy subjects at rest, during quiet breathing. To this aim, the AM of LF-FCG and HF-FCG were first extracted via a linear envelope (LE) operation, consisting of rectification followed by low-pass filtering; then, the inspiratory peaks were located both in the LE of LF-FCG and HF-FCG, and in the reference respiratory signal (FRG). Finally, the inter-breath intervals were extracted from the obtained inspiratory peaks, and further analyzed via statistical analyses. The AM of HF-FCG exhibited higher consistency within the respiratory cycle, as compared to the LF-FCG. Indeed, the inspiratory peaks were recognized with a sensitivity and positive predictive value (PPV) in excess of 99% in the LE of HF-FCG, and with a sensitivity and PPV of 96.7% and 92.6%, respectively, in the LE of LF-FCG. In addition, the inter-breath intervals estimated from the HF-FCG scored a higher R2 value (0.95 vs. 0.86) and lower limits of agreement (± 0.710 s vs. ±1.34 s) as compared to LF-FCG, by considering those extracted from the FRG as the reference. The obtained results are consistent with those observed in previous studies on SCG. A possible explanation of these results was discussed. However, the preliminary results obtained in this study must be confirmed on a larger cohort of subjects and in different experimental conditions.

No relevant literature was identified regarding the comparative clinical effectiveness of cardiac monitoring devices for adolescents with eating disorders in inpatient settings. No evidence-based guidelines were identified regarding cardiac monitoring for adolescents with eating disorders in inpatient settings.

Monitoreo cardíaco; Grabadora de bucle; Palpitación Cardiac monitoring; Loop recorder; Palpitation Monitorització cardíaca; Gravadora de bucle; Palpitació Palpitations are one of the most common reasons for medical consultation. They tend to worry patients and can affect their quality of life. They are often a symptom associated with cardiac rhythm disorders, although there are other etiologies. For diagnosis, it is essential to be able to reliably correlate the symptoms with an electrocardiographic record allowing the identification or ruling out of a possible rhythm disorder. However, reaching a diagnosis is not always simple, given that they tend to be transitory symptoms and the patient is frequently asymptomatic at the time of assessment. In recent years, electrocardiographic monitoring systems have incorporated many technical improvements that solve several of the 24-h Holter monitor limitations. The objective of this review is to provide an update on the different monitoring methods currently available, remarking their indications and limitations, to help healthcare professionals to appropriately select and use them in the work-up of patients with palpitations.

Background and Objectives: Prematurity of birth occurs before the 37th week of gestation and affects up to 10% of births worldwide. It is correlated with critical outcomes, therefore, constant monitoring in neonatal intensive care units or home environments is required. The aim of this work was to develop solutions for remote neonatal intensive supervision systems, which should assist medical diagnosis of premature infants and raise alarm at cardiac abnormalities, such as bradycardia. Additionally, the COVID-19 pandemic has put a worldwide stress upon the medical staff and the management of healthcare units. Materials and Methods: A traditional medical diagnosing scheme was set up, implemented with the aid of powerful mathematical operators. The algorithm was tailored to the infants’ personal ECG characteristics and was tested on real ECG data from the publicly available PhysioNet database “Preterm Infant Cardio-Respiratory Signals Database”. Different processing problems were solved: noise filtering, baseline drift removal, event detection and compression of medical data using the à trous wavelet transform. Results: In all 10 available clinical cases, the bradycardia events annotated by the physicians were correctly detected using the RR intervals. Compressing the ECG signals for remote transmission, we obtained compression ratios (CR) varying from 1.72 to 7.42, with the median CR value around 3. Conclusions: We noticed that a significant amount of noise can be added to a signal while monitoring using standard clinical sensors. We tried to offer solutions for these technical problems. Recent studies have shown that persons infected with the COVID-19 disease are frequently reported to develop cardiovascular symptoms and cardiac arrhythmias. An automatic surveillance system (both for neonates and adults) has a practical medical application. The proposed algorithm is personalized, no fixed reference value being applied, and the algorithm follows the neonate’s cardiac rhythm changes. The performance depends on the characteristics of the input ECG. The signal-to-noise ratio of the processed ECG was improved, with a value of up to 10 dB.

[This corrects the article DOI: 10.3389/fneur.2023.1103664.]., (Copyright © 2023 Chaisinanunkul, Khurshid, Buck, Rabinstein, Anderson, Hill, Fugate and Saver.)

Fiber Bragg grating sensing technology provides a new look to the healthcare monitoring system due to its spectral encoding capacity, dielectric property, sensitivity, inert, nontoxic, resistive to the electromagnetic environment, self-referencing, and low cost. This article presents a design and construction of an FBG sensor for the monitoring of cardiac vibrations. A sensor element is designed by depositing polydimethylsiloxane (PDMS) polymer on the FBG sensing element. The elastic and thermal property of the sensor element is also discussed in this article. The stress and strain distribution profile is addressed using finite element analysis (FEA). Further, the bonding of the FBG sensor element is discussed in this article with adequate design specifications. In addition to the FBG sensor design considerations, the real-time acquisition of the cardiac signal is also experimented with in this research work to validate the sensor performance. Finally, the architecture of a cardiac monitoring approach by utilizing the Internet of things (IoT) and machine learning (ML) is proposed in this article.

Aim Implantable loop recorders (ILRs) are now routinely implanted for long-term cardiac monitoring in the clinical setting. The aim of this study was to examine the real-world performance of these devices focusing on the management changes made in response to ILR-recorded data. Methods and results This was a single-centre, prospective observational study of consecutive patients undergoing ILR implantation. All patients who underwent implantation of a Medtronic Reveal LINQ device from September 2017 to June 2019 at Barts Heart Centre were included. Five hundred and one patients were included. Three hundred and two (60%) patients underwent ILR implantation for an indication of pre-syncope/syncope, 96 (19%) for palpitations, 72 (14%) for atrial fibrillation (AF) detection with a history of cryptogenic stroke, and 31 (6%) for high risk of serious cardiac arrhythmia. The primary outcome of this study was that an ILR-derived diagnosis altered management in 110 patients (22%). Secondary outcomes concerned subgroup analyses by indication: in patients who presented with syncope/pre-syncope, a change in management resulting from ILR data was positively associated with age [hazard ratio (HR) 1.04, 95% confidence interval 1.02–1.06; P Conclusion These results inform the utility of ILR in the clinical setting. Diagnoses provided by ILR that lead to changes in management are rare in patients under age 40, particularly following syncope, pre-syncope, or palpitations. In older patients, new diagnoses are frequently made and trigger important changes in treatment.

Purpose of Review HER2-targeted therapies have led to improved clinical outcomes in early and advanced breast cancer (BC). We review the long-term cardiotoxicity of HER2-targeted therapy in early and advanced BC, our current knowledge of cardiotoxicity of novel HER2-targeted therapies, and propose a cardiac monitoring (CM) strategy for this population. Recent Findings Long-term data from studies with HER2-targeted therapy in the adjuvant setting have failed to demonstrate an increase in cardiotoxicity over time, and rates of cardiotoxicity seen with novel HER2 agents remain low. Despite over a decade of experience with HER2-targeted therapy, CM in clinical practice is inconsistent in patients with early BC and almost non-existent in advanced BC. Summary Long-term follow-up of clinical trials with HER2-targeted agents in early and advanced BC has failed to demonstrate increased rates of cardiotoxicity over time, attesting to the long-term safety of this class of drugs for the majority of patients, although the long-term cardiac safety of newer HER2 agents in the non-clinical trial setting is largely unknown. We propose CM incorporating clinical history, cardiac imaging, and biomarkers.

Wearable cardiac sensors pave the way for advanced cardiac monitoring applications based on heart rate variability (HRV). In real-life settings, heart rate (HR) measurements are subject to motion artifacts that may lead to frequent data loss (missing samples in the HR signal), especially for commercial devices based on photoplethysmography (PPG). The current study had two main goals: (i) to provide a white-box quality index that estimates the amount of missing samples in any piece of HR signal, and (ii) to quantify the impact of data loss on feature extraction in a PPG-based HR signal. This was done by comparing real-life recordings from commercial sensors featuring both PPG (Empatica E4) and ECG (Zephyr BioHarness 3). After an outlier rejection process, our quality index was used to isolate portions of ECG-based HR signals that could be used as benchmark, to validate the output of Empatica E4 at the signal level and at the feature level. Our results showed high accuracy in estimating the mean HR (median error: 3.2%), poor accuracy for short-term HRV features (e.g., median error: 64% for high-frequency power), and mild accuracy for longer-term HRV features (e.g., median error: 25% for low-frequency power). These levels of errors could be reduced by using our quality index to identify time windows with few or no data loss (median errors: 0.0%, 27%, and 6.4% respectively, when no sample was missing). This quality index should be useful in future work to extract reliable cardiac features in real-life measurements, or to conduct a field validation study on wearable cardiac sensors.

Lyme disease is a vector-borne infection that can affect multiple different organ systems. Lyme carditis represents one of these sequelae and is defined by acute onset of high-grade atrioventricular block in the presence of laboratory-confirmed infection. Current guidelines recommend patients with Lyme carditis be admitted for close cardiac monitoring and intravenous antibiotics therapy. Our case illustrates an active duty male who was initially diagnosed with Lyme disease after initially reporting symptoms including headache, fever, eye pain, and rash, with subsequent development of exercise intolerance 6 weeks later. An electrocardiogram (ECG) obtained at that time was misinterpreted as first-degree heart block, and he was initiated on oral doxycycline therapy and referred to cardiology. On follow-up to cardiology clinic, the prior ECG was reviewed and interpreted as complete heart block. A repeat ECG showed resolution of the heart block, and exercise stress testing showed chronotropic competence. This case illustrates the resolution of complete heart block in Lyme carditis with oral doxycycline, suggesting this antibiotic as a possible alternative treatment agent.

Background Heart rate variability (HRV) has been investigated previously in autonomic nervous system-related clinical settings. In these settings, HRV is determined by the time-series heartbeat peak-to-peak intervals using electrocardiography (ECG). To reduce patient discomfort, we designed a Doppler radar-based autonomic nervous activity monitoring system (ANMS) that allows cardiopulmonary monitoring without using ECG electrodes or spirometry monitoring. Case summary Using our non-contact ANMS, we observed a bedridden 80-year-old female patient with terminal phase sepsis developed the daytime Cheyne-Stokes respiration (CSR) associated with the attenuation of the low frequency (LF) and high frequency (HF) of HRV components 20 days prior to her death. The patient developed a marked linear decrease in the LF and the HF of HRV components for over 3 days in a row. Furthermore, after the decrease both the LF and the HF showed low and linear values. Around the intersection of the two lines, the decreasing LF and HF lines and the constant LF and HF lines, the ANMS automatically detected the daytime CSR pathogenesis. The attenuation rate of HF (1340 ms2/day) was higher than that of LF (956 ms2/day). Heart rate increased by ∼10 b.p.m. during these 3 days. Discussion We detected CSR-associated LF and HF attenuation in a patient with terminal phase sepsis using our ANMS. The proposed system without lead appears promising for future applications in clinical settings, such as remote cardiac monitoring of patients with heart failure at home or in long-term acute care facilities.

An ultra-low power ECG processor ASIC (application specific integrated circuit) with R-wave detection and data compression is presented, which is designed for the long-term implantable cardiac monitoring (ICM) device for arrhythmia diagnosis. An adaptive derivative-based detection algorithm with low computation overhead for potential arrhythmia recording is proposed to detect arrhythmia with the occasional abnormal heart beats. In order to save as much as possible cardiac information with the limited memory size available in the ICM device, a hierarchical data buffer structure is proposed which saves 3 types of data, including the raw ECG data segments of 2 seconds, compressed ECG data segments of 45 seconds, and R-peak values and interval lengths of >2000 beat cycles. A modified swinging-door-trending (SDT) method is proposed for the ECG data compression. The ASIC has been implemented based on fully-customized near-threshold standard cells using the thick-gate transistors in 65-nm CMOS technology for low dynamic power consumption and leakage. The ASIC core occupies a die area of 1.77 mm2. The measured total power is 2.63 μW, which is among the ECG processors with the lowest core power consumption. It exhibits a relatively high positive precision rate ( P +) of 99.3% with a sensitivity of 98.2%, in contrast to the similar designs in literature with the same core power consumption level. Also, an ECG data compression ratio ( CR ) of up to 17.0 has been achieved, with a good trade-off between the compression efficiency and loss.

Videoplethysmography (VPG) is an emerging technology that uses a video camera to capture subtle skin color variations caused by blood volume flow. VPG offers a seamless, non invasive long term cardiac monitoring solution which is necessary to capture a-symptomatic heart conditions such as early stages of Atrial Fibrillation (AF). In this work we investigate the ability of VPG based on high end Basler RGB camera to identify AF in a controlled hospital setting. We conduct a clinical study and explore various modern Machine Learning (ML) algorithms and feature extraction methods to provide classification among three groups: healthy subjects, subjects with AF before and after they undergo direct current cardioversion. Our results reveal that when features represent statistical, dimensional and time-frequency properties of the underlying VPG signal, all non-linear and ensemble classifiers under test achieve close to perfect performance in both 2- and 3-class classification. Among them, random forest and extreme gradient boosting classifier consistently attain the highest accuracy, sensitivity and specificity of almost 100%. These results suggest that the VPG technology relying on a high quality camera combined with intelligent ML classifiers and smart feature selection can be used to facilitate diagnosis of heart conditions within a controlled hospital environment and has a great potential to go beyond.

Monitoring of the cardiovascular system is vital to detect cardiac arrhythmia which is a leading cause of death in the United States. The most popular cardiac monitoring modalities are Electrocardiography (ECG) and Photoplethysmography (PPG). Utilization of the critical features extracted from these signals allows an automated pipeline for early detection. However, recorded data is often incomplete due to equipment sampling issues or loss of contact. Completion of missing values through imputation permits these datasets to be examined for arrhythmia through machine learning. Because a comprehensive investigation of prominent imputation algorithms has not been conducted within this context before, we explored several algorithms to accurately predict the missing feature values extracted from both modalities. Our workflow consists of preprocessing, feature extraction, and imputation algorithms to estimate these clinically important values. We collected 2700 minutes of ECG and PPG data from 36 patients within the PhysioNet MIMIC-III database. Approximately 150 features were extracted for both ECG and PPG prior to the imputation. We compared seven imputation algorithms (Zero, Mean, Hot Deck, Linear Regression, KNN, Random Forest and Neural Networks) using MSE and R2 as the evaluation metrics. KNN, Random Forest and Linear Regression gave the highest R2 values of 0.904, 0.908, 0.898 respectively, for a subset of the features. Overall, KNN was the most consistent in providing the highest R2 values and the lowest MSE values across all the features tested. Our investigation provides an expanded preliminary platform to conduct real-time arrhythmia detection in hospitals or remotely via wearable devices.

Objectives: This is a protocol for a Cochrane Review (intervention). The objectives are as follows:. To evaluate the efficacy of wearable smart-devices for the detection of atrial fibrillation.

Chronic exposure to stressful environments can negatively impact cats' health and welfare, affecting behavioral, autonomic, endocrine, and immune function, as with cats in shelters. Low-stress handling practices likely improve shelter cat welfare, but data supporting improved outcomes remain limited. Cardiac activity, particularly heart rate variability (HRV), is an indicator of stress and emotional state in humans and non-human animals, tracking important body functions associated with stress responsiveness, environmental adaptability, mental, and physical health. HRV studies in cats are limited, involving mainly anesthetized or restrained cats. This pilot study tested the feasibility of obtaining HRV data from unrestrained cats, using a commercially available cardiac monitoring system (Polar H10 with chest strap), compared with data from a traditional ambulatory electrocardiogram. Simultaneous data for the two systems were obtained for five adult cats. Overall, the Polar H10 monitor assessments of HRV were lower than the true HRV assessment by ambulatory ECG, except for SDNN. Correlation between the two systems was weak. Possible reasons for the lack of agreement between the two methods are discussed. At this time, our results do not support the use of Polar H10 heart rate monitors for studies of HRV in cats.

This article first studied the morphological characteristics of the EEG for intensive cardiac care; that is, based on the analysis of the mechanism of disease diagnosis and treatment, a signal processing and machine learning model was constructed. Then, the methods of signal preprocessing, signal feature extraction, new neural network model structure, training mechanism, optimization algorithm, and efficiency are studied, and experimental verification is carried out for public data sets and clinical big data. Then, the principle of intensive cardiac monitoring, the mechanism of disease diagnosis, the types of arrhythmia, and the characteristics of the typical signal are studied, and the rhythm performance, individual variability, and neurophysiological basis of electrical signals in intensive cardiac monitoring are researched. Finally, the automatic signal recognition technology is studied. In order to improve the training speed and generalization ability, a multiclassification model based on Least Squares Twin Support Vector Machine (LS-TWIN-SVM) is proposed. The computational complexity of the classification model algorithm is compared, and intelligence is adopted. The optimization algorithm selects the parameters of the classifier and uses the EEG signal to simulate the model. Support Vector Machines and their improved algorithms have achieved the ultimum in shallow neural networks and have achieved good results in the classification and recognition of bioelectric signals. The LS-TWIN-SVM algorithm proposed in this paper has achieved good results in the classification and recognition of bioelectric signals. It can perform bioinformatics processing on intensive cardiac care EEG signals, systematically biometric information, diagnose diseases, the real-time detection, auxiliary diagnosis, and rehabilitation of patients.

Metoprolol overdosing can lead to bradycardia, hypotension and chances of cardiovascular collapse. Here we are going through a case study of 36 year old male patient who consumed 15 metoprolol tablet.He consumed metoprolol tablets under the influence of alcohol.He was brought to the emergency department of a hospital.He was provided with gastric lavage followed by further antidote therapy. Antidote used in glucagon. Glucagon is traditionally considered a first line antidote for beta-blocker overdose. No clear guidelines are present for treatment of metoprolol overdose. Cardiac monitoring and Random blood sugar monitoring was done. As patient attempted suicide, he was provided with the psychiatrist Counseling, nursing care. The Patient was treated for 72 hours. Keywords: Metoprolol, Glucagon, Overdose, Gastric lavage.

Cardiac arrhythmias have been reported in the setting of coronavirus disease 2019 (COVID-19) infection. To date various tachyarrhythmias have been noted. This case report describes an atypical clinical hospitalization course of high-degree atrioventricular (AV) block in the setting of COVID-19 infection which required no intervention. This case highlights the importance of an initial electrocardiogram (ECG) upon presentation and consideration for constant cardiac monitoring in select patients.

Due to the rising number of heart patients and the apparent need for more robust electrocardiogram (ECG) monitoring of these patients, hospitals are increasingly investing in typical cloud technology or centralized hospital server based remote ECG monitoring systems. However, the deployment these systems in rural communities is limited due to the high cost factor. To counter this challenge, in this paper, we focus on the design and implementation of a low cost real time wireless ambulatory ECG monitoring system. The detected ECG signals are first filtered and amplified and then digitally converted by a microcontroller. The digitized ECG signals are then sent over a ZigBee wireless link to a gateway personal computer (PC) at patient’s premises. The received ECG data from the ZigBee connection is displayed in real time via the National Instruments (NI) Laboratory Virtual Instrument Engineering Workbench (LabVIEW) user interface on the PC for instant personalized evaluation of the ECG data. The ECG data can be saved on the PC and sent via email to a remote cardiologist or a clinician. Additionally, the gateway PC at patient’s end acts as web server for sharing patient’s data over the Internet. The remote off-site physician (medical staff in a hospital) can use a web browser on a PC, laptop or a mobile phone with Internet connection to access patient’s real time ECG trace for monitoring, expert review and diagnosis. It is shown that the system prototype allows users to acquire reliable ECG signals effectively and simply. The proposed ambulatory ECG system offers an alternative low cost deployment strategy and is especially suited for remote cardiac monitoring of patients in rural communities.

This paper presents a novel ECG classification algorithm for inclusion as part of real-time cardiac monitoring systems in ultra low-power wearable devices. The proposed solution is based on spiking neural networks which are the third generation of neural networks. In specific, we employ spike-timing dependent plasticity (STDP), and reward-modulated STDP (R-STDP), in which the model weights are trained according to the timings of spike signals, and reward or punishment signals. Experiments show that the proposed solution is suitable for real-time operation, achieves comparable accuracy with respect to previous methods, and more importantly, its energy consumption in real-time classification of ECG signals is significantly smaller. In specific, energy consumption is 1.78 $\boldsymbol {\mu }$ J per beat, which is 2 to 9 orders of magnitude smaller than previous neural network based ECG classification methods.

Session V of the Colloquium was chaired by Professors Teresa Lopez-Fernandez of Spain and Grzegorz Opolski of Poland. The 3 speakers addressed cardio-oncology issues as they relate to both clinical studies and real life situations. Professor Susan Dent discussed cardio-oncology networks for patients, emphasizing the importance of establishing a framework where the expertise of the cardiology consultant can supplement and reinforce the goals of optimal cancer therapy. Professor Thomas Suter moved the discussion further, sharing his insight into cardiac monitoring in clinical trials, emphasizing the lack of uniform criteria and lack of consensus regarding reversibility of cardiac events and long-term implications of modest declines in systolic function frequently found in clinical trials for which long-term follow-up may not be a component of the trial. Professor Giorgio Minotti added important considerations to the discussion of clinical trials. He emphasized that the usual reporting of cardiac systolic function omits important diastolic dysfunction data generated but often ignored during the routine cardiac exams. The inclusion of cardiac biomarker changes would also help to broaden the perspective of cardiac effects and events seen in patients enrolled in clinical trials.

BackgroundFingolimod (Gilenya®) is approved for relapsing forms of multiple sclerosis in the USA. Owing to transient heart-rate effects when initiating fingolimod, eligible patients undergo precautionary baseline assessment and first-dose observation (FDO) for ≥6 h. Prior to 2014, FDO was undertaken only in clinics. As the FDO period is short, and fingolimod has accumulated evidence of a positive benefit:risk ratio, an in-home treatment-initiation program, Gilenya@Home, was developed to offer a convenient alternative.MethodsCardiac parameters and adverse events (AEs) were recorded by healthcare professionals performing fingolimod FDOs in the US Gilenya@Home program or in US Gilenya Assessment Network clinics. Anonymized data were collated retrospectively from the first 34 months in the home setting and from 78 months in clinics; data are reported descriptively. Satisfaction with Gilenya@Home was rated by patients using a 7-item questionnaire that considered aspects such as ease of scheduling, courtesy, and competency.ResultsData were captured as part of standard care from 5573 patients initiating fingolimod in-home (October 2014 to July 2017) and from 15,025 patients initiating in-clinic (July 2010 to December 2016). In the Gilenya@Home questionnaire, 91.7% of 1848 respondents rated their overall satisfaction as “very good,” and 7.6% rated their satisfaction as “good.” AEs were reported for 30.7 and 32.6% of in-home and in-clinic patients, respectively. In total, 557 in-home (10.0%) and 398 in-clinic (2.6%) patients were monitored for > 6 h; 15 (0.3%) in-home and 129 (0.9%) in-clinic patients were transferred to an emergency room for overnight monitoring. The mean (standard deviation) heart rate (HR; bpm) pre-FDO was 74.8 (12.2) in-home and 74.2 (11.3) in-clinic; reduction in HR at 6 h postdose was 10.6 (12.0) and 6.3 (9.6), respectively. New-onset first-degree atrioventricular block was experienced by 132 (2.4%) in-home and 74 (0.5%) in-clinic patients, and Wenckebach (Mobitz type I) second-degree atrioventricular block by four (0.07%) and nine (0.1%) patients, with no cases of third-degree atrioventricular block.ConclusionsA substantial number of patients have initiated fingolimod at home, reporting very high levels of satisfaction. Gilenya@Home was as rigorous as the clinic setting in detecting cardiovascular events. Overall, FDO safety outcomes were similar with Gilenya@Home and in-clinic.

Electrocardiography (ECG) is a mandatory standard for monitoring electrical activity of the heart in many clinical settings such as intensive care and emergency units. However, in situations wherein the skin is damaged, such as acute burn injuries, it is impossible to efficiently attach electrodes to the skin. In this study, we developed a non-contact cardiac monitoring system using a 24-GHz medical radar for directly measuring the beat-to-beat heart mechanical activity at a distance from a subject. The heart rate variability (HRV) was analysed using an autoregressive model (AR) from the measured beat-to-beat intervals during a head-up tilt test. To investigate the feasibility of the proposed system, we compared medical radar and ECG recording by using Lin's correlation coefficient and Bland-Altman analysis, which showed a negligible mean difference from the substantial agreement of Lin's correlation coefficient of 0.9 between the radar and ECG. The non-contact radar clearly monitored dynamic changes in HRV indices induced by the head-up tilt test. This type of non-contact HRV-sensing technique as an alternative approach has significant potential for advancing personal healthcare in both clinical and out-of-hospital settings.

Background and Purpose Current guidelines do not provide firm directions on atrial fibrillation (AF) screening after ischemic stroke (IS). We sought to investigate the association of implantable cardiac monitoring (ICM) duration with the yield of AF detection in IS patients. Methods We included studies reporting AF detection rates by ICM in IS patients with negative initial AF screening. We excluded studies reporting prolonged cardiac monitoring with devices other than ICM, not providing AF detection rates or monitoring duration, and reporting overlapping data for the same population. The random-effects model was used for all pooled estimates and meta-regression analyses. Results We included 28 studies (4,531 patients, mean age 65 years). In meta-regression analyses, the proportion of AF detection by ICM was independently associated with monitoring duration (coefficient=0.015; 95% confidence interval [CI], 0.005 to 0.024) and mean patient age (coefficient=0.009; 95% CI, 0.003 to 0.015). No associations were detected with other patient characteristics, including IS subtype (cryptogenic vs. embolic stroke of undetermined source) or time from IS onset to CM implantation. In subgroup analyses, significant differences (P12 and ≤24 months: 26% [95% CI, 22% to 31%]; >24 months: 34% [95% CI, 29% to 39%]). Conclusions Extended duration of ICM monitoring and increased patient age are factors that substantially increase AF detection in IS patients with initial negative AF screening.

Background Strokes due to cardio embolism are generally more severe and prone to early and long term recurrence. All forms of anticoagulant therapy are associated with a two thirds risk reduction in acute ischaemic stroke associated with atrial fibrillation. Establishing a diagnosis of paroxysmal atrial fibrillation can be a challenging one. Our study will attempt to answer the question as to what duration of extended cardiac monitoring is required when screening for atrial fibrillation in cryptogenic stroke. Methods We examined the time of atrial fibrillation initial diagnosis in 50 patients with an original working diagnosis of cryptogenic stroke but later have atrial fibrillation confirmed on extended monitoring. All patients wore the external loop recorder from 1 day to 3 weeks’ duration. On return of the device, data from the external loop recorder was downloaded, and reviewed by a consultant cardiologist. The total duration of monitoring using the external loop recorder was recorded. The time to first episode of algorithm detected atrial fibrillation was recorded. Results The mean duration of extended cardiac monitoring using the external loop recorder was 120.1days By seven days, atrial fibrillation had been detected in 94% of patients. By 72 hours, atrial fibrillation had been detected in 78% of patients. The mean age of participants was 58.3 years. 54.4% were female. The mean time to detection of atrial fibrillation was 52.9 hours. The median time to detection of atrial fibrillation was 34.2 hours. Conclusion External loop recorders present an opportunity for detection of paroxysmal atrial fibrillation in high risk individuals. Our study shows the feasibility and accuracy of a screening duration of seven days in the high risk post ischaemic stroke population. A one week screening timeframe allows us to screen twice as many patients for paroxysmal atrial fibrillation. Successful screening for atrial fibrillation in this high risk group allows the commencement of acceptable and safe anticoagulant therapy.

Wearable digital devices offer potential advantages over traditional methods for the collection of health-related information, including continuous collection of dense data while study subjects are ambulatory or in remote settings. We assessed the utility of collecting continuous actigraphy and cardiac monitoring by deploying two US Food and Drug Administration (FDA) 510(k)-cleared devices in a phase I clinical trial of a novel compound, which included the use of an amphetamine challenge. The Phillips Actiwatch Spectrum Pro (Actiwatch) was used to assess mobility and sleep. The Preventice BodyGuardian (BodyGuardian) was used for monitoring heart rate (HR) and respiratory rate (RR), via single-lead electrocardiogram (ECG) recordings, together with physical activity. We measured data collection rates, compared device readouts with conventional measures, and monitored changes in HR measures during the amphetamine challenge. Completeness of data collection was good for the Actiwatch (96%) and lower for the BodyGuardian (80%). A good correlation was observed between device and in-clinic measures for HR (r = 0.99; P < 0.001), but was poor for RR (r = 0.39; P = 0.004). Manual reviews of selected ECG strips corresponding to HR measures below, within, and above the normal range were consistent with BodyGuardian measurements. The BodyGuardian device detected clear HR responses after amphetamine administration while subjects were physically active, whereas conventional measures collected at predefined timepoints while subjects were resting and supine did not. Wearable digital technology shows promise for monitoring human subjects for physiologic changes and pharmacologic responses, although fit-for-purpose evaluation and validation continues to be important prior to the wider deployment of these devices.

Aims Monitoring of patients after ablation had wide variations in the ESC-EHRA atrial fibrillation ablation long-term (AFA-LT) registry. We aimed to compare four different monitoring strategies after catheter AF ablation. Methods and results The ESC-EHRA AFA-LT registry included 3593 patients who underwent ablation. Arrhythmia monitoring during follow-up was performed by 12-lead electrocardiogram (ECG), Holter ECG, trans-telephonic ECG monitoring (TTMON), or an implanted cardiac monitoring (ICM) system. Patients were selected to a given monitoring group according to the most extensive ECG tool used in each of them. Comparison of the probability of freedom from recurrences was performed by censored log-rank test and presented by Kaplan–Meier curves. The rhythm monitoring methods were used among 2658 patients: ECG (N = 578), Holter ECG (N = 1874), TTMON (N = 101), and ICM (N = 105). A total of 767 of 2658 patients (28.9%) had AF recurrences during follow-up. Censored log-rank test discovered a lower probability of freedom from relapses, which was detected with ICM compared to TTMON, ECG, and Holter ECG (P Conclusion Comparing all main electrocardiographic monitoring methods in a large patient sample, our results suggest that post-ablation recurrences of AF are significantly underreported by TTMON, ECG, and Holter ECG. The ICM estimates AF ablation recurrences most reliably and should be a preferred mode of monitoring for trials evaluating novel AF ablation techniques.