Search

In this paper we propose a generic approach to reduce inter-individual variability of different physiological signals (HR, GSR and respiration) by automatically estimating normalization parameters (e.g. baseline and range). The proposed normalization procedure does not require a dedicated personal calibration during system setup. On the other hand, normalization parameters are estimated at system runtime from sedentary and low intensity activities of daily living (ADLs), such as lying and walking. When combined with activity-specific energy expenditure (EE) models, our normalization procedure improved EE estimation by 15 to 33% in a study group of 18 participants, compared to state of the art activity-specific EE models combining accelerometer and non-normalized physiological signals.

The thin skin of preterm babies is easily damaged by adhesive electrodes, tapes, chest drains and needle-marks. The scars caused could be disfiguring or disabling to 10% of preterm newborns. Capacitive sensors present an attractive option for pervasively monitoring neonatal ECG, and can be embedded in a support system or even a garment worn by the neonate. This could improve comfort and reduce pain aiding better recovery as well as avoiding the scars caused by adhesive electrodes. In this work, we investigate the use of an array of capacitive sensors unobtrusively embedded in a mattress and used in a clinical environment for 15 preterm neonates. We also describe the analysis framework including the fusion of information from all sensors to provide a more accurate ECG signal. We propose a channel selection strategy as well as a method using physiological information to obtain a reliable ECG signal. When sensor coverage is well attained, results for both instantaneous heart rate and ECG signal shape analysis are very encouraging. The study also provides several insights on important factors affecting the results. These include the effect of textile type, number of layers, interferences (e.g. people walking by), motion severity and interventions. Incorporating this knowledge in the design of a capacitive sensing system would be crucial in ensuring that these sensors provide a reliable ECG signal when embedded in a neonatal support system.

Fatigue is reported as the most common symptom by patients with multiple sclerosis (MS). The physiological and functional parameters related to fatigue in MS patients are currently not well established. A new wearable wireless body measurement system, named Fatigue Monitoring System (FAMOS), was developed to study fatigue in MS. It can continuously measure electrocardiogram, body-skin temperature, electromyogram and motions of feet. The goal of this study is to test the ability of distinguishing fatigued MS patients from healthy subjects by the use of FAMOS. This paper presents the realization of the measurement system including the design of both hardware and dedicated signal processing algorithms. Twenty-six participants including 17 MS patients with fatigue and 9 sex- and age-matched healthy controls were included in the study for continuous 24 h monitoring. The preliminary results show significant differences between fatigued MS patients and healthy controls. In conclusion, the FAMOS enables continuous data acquisition and estimation of multiple physiological and functional parameters. It provides a new, flexible and objective approach to study fatigue in MS, which can distinguish between fatigued MS patients and healthy controls. The usability and reliability of the FAMOS should however be further improved and validated through larger clinical trials.

Non-invasive fetal health monitoring during pregnancy is becoming increasingly important because of the increasing number of high-risk pregnancies. Despite recent advances in signal-processing technology, which have enabled fetal monitoring during pregnancy using abdominal electrocardiogram (ECG) recordings, ubiquitous fetal health monitoring is still unfeasible due to the computational complexity of noise-robust solutions. In this paper, an ECG R-peak detection algorithm for ambulatory R-peak detection is proposed, as part of a fetal ECG detection algorithm. The proposed algorithm is optimized to reduce computational complexity, without reducing the R-peak detection performance compared to the existing R-peak detection schemes. Validation of the algorithm is performed on three manually annotated datasets. With a detection error rate of 0.23%, 1.32% and 9.42% on the MIT/BIH Arrhythmia and in-house maternal and fetal databases, respectively, the detection rate of the proposed algorithm is comparable to the best state-of-the-art algorithms, at a reduced computational complexity.

Monitoring the fetal heart rate (fHR) and fetal electrocardiogram (fECG) during pregnancy is important to support medical decision making. Before labor, the fHR is usually monitored using Doppler ultrasound. This method is inaccurate and therefore of limited clinical value. During labor, the fHR can be monitored more accurately using an invasive electrode; this method also enables monitoring of the fECG. Antenatally, the fECG and fHR can also be monitored using electrodes on the maternal abdomen. The signal-to-noise ratio of these recordings is, however, low, the maternal electrocardiogram (mECG) being the main interference. Existing techniques to remove the mECG from these non-invasive recordings are insufficiently accurate or do not provide all spatial information of the fECG. In this paper a new technique for mECG removal in antenatal abdominal recordings is presented. This technique operates by the linear prediction of each separate wave in the mECG. Its performance in mECG removal and fHR detection is evaluated by comparison with spatial filtering, adaptive filtering, template subtraction and independent component analysis techniques. The new technique outperforms the other techniques in both mECG removal and fHR detection (by more than 3%).

The analysis of intravascular indicator dynamics is important for cardiovascular diagnostics as well as for the assessment of tissue perfusion, aimed at the detection of ischemic regions or cancer hypervascularization. To this end, indicator dilution curves are measured after the intravenous injection of an indicator bolus and fitted by parametric models for the estimation of the hemodynamic parameters of interest. Based on heuristic reasoning, the dilution process is often modeled by a gamma variate. In this paper, we provide both a physical and stochastic interpretation of the gamma variate model. The accuracy of the model is compared with the local density random walk model, a known model based on physics principles. Dilution curves were measured by contrast ultrasonography both in vitro and in vivo (20 patients). Blood volume measurements were used to test the accuracy and clinical relevance of the estimated parameters. Both models provided accurate curve fits and volume estimates. In conclusion, the proposed interpretations of the gamma variate model describe physics aspects of the dilution process and lead to a better understanding of the observed parameters, increasing the value and credibility of the model, and possibly expanding its diagnostic applications.

Monitoring the uterine contraction provides important prognostic information during pregnancy and parturition. The existing methods employed in clinical practice impose a compromise between reliability and invasiveness. A promising technique for uterine contraction monitoring is electrohysterography (EHG). The EHG signal measures the electrical activity which triggers the contraction of the uterine muscle. In this paper, a non-invasive method for intrauterine pressure (IUP) estimation by EHG signal analysis is proposed. The EHG signal is regarded as a non-stationary signal whose frequency and amplitude characteristics are related to the IUP. After acquisition in a multi-channel configuration, the EHG signal is therefore analyzed in the time–frequency domain. A first estimation of the IUP is then derived by calculation of the unnormalized first statistical moment of the frequency spectrum. The estimation accuracy is finally increased by identification of a second-order polynomial model. The proposed method is compared to root mean squared analysis and optimal linear filtering and validated by simultaneous measurement of the IUP on nine women during labor. The results suggest that the proposed EHG signal analysis provides an accurate estimate of the IUP.

In this paper, we propose a new method for FECG detection in abdominal recordings. The method consists of a sequential analysis approach, in which the a priori information about the interference signals is used for the detection of the FECG. Our method is evaluated on a set of 20 abdominal recordings from pregnant women with different gestational ages. Its performance in terms of fetal heart rate (FHR) detection success is compared with that of independent component analysis (ICA). The results show that our sequential estimation method outperforms ICA with a FHR detection rate of 85% versus 60% of ICA. The superior performance of our method is especially evident in recordings with a low signal-to-noise ratio (SNR). This indicates that our method is more robust than ICA for FECG detection.

A customized filtering technique is introduced and compared with fast Fourier transformation (FFT) for analyzing heart rate variability (HRV) in neonates from short-term recordings. FFT is classically the most commonly used spectral technique to investigate cardiovascular fluctuations. FFT requires stability of the physiological signal within a 300 s time window that is usually analyzed in adults. Preterm infants, however, show characteristics of rapidly fluctuating heart rate and blood pressure due to an immature autonomic regulation, resulting in non-stationarity of these signals. Therefore neonatal studies use (half-overlapping or moving) windows of 64 s length within a recording time of 2-5 min. The proposed filtering technique performs a filtering operation in the frequency range of interest before calculating the spectrum, which allows it to perform an analysis of shorter periods of only 42 s. The frequency bands of interest are 0.04-0.15 Hz (low frequency, LF) and 0.4-1.5 Hz (high frequency, HF). Although conventional FFT analysis as well as the proposed alternative technique result in errors in the estimation of LF power, due to spectral leakage from the very low frequencies, FFT analysis is more sensitive to this effect. The response times show comparable behavior for both the techniques. Applying both the methods to heart rate data obtained from a neonate before and after atropine administration (inducing a wide range of HRV), shows a very significant correlation between the two methods in estimating LF and HF power. We conclude that a customized filtering technique might be beneficial for analyzing HRV in neonates because it reduces the necessary time window for signal stability.

Objective. Measurement of respiratory rate and effort is useful in various applications, such as the diagnosis of sleep apnea and early detection of patient deterioration in medical conditions, such as infections. A chest-worn accelerometer may be an easy and non-intrusive method, provided it is accurate and robust. We investigate the use of a novel method that can perform under realistic sleeping conditions such as variable sensor positions and body posture. Approach. Twenty subjects (aged 46–65 years) wore an accelerometer on the chest and a respiratory impedance plethysmography band as a reference. The subjects underwent an experimental protocol lasting approximately 90 min, under various postures and with different sensor positions. We used a novel, constrained, and recursive form of principal component analysis (PCA) to estimate the respiratory effort signal robustly. To obtain an estimate for the respiratory rate, first, multiple estimates were aggregated into a single frequency. Subsequently, a quality index was determined, such that unreliable estimates could be identified, and a trade-off could be made between coverage (percentage of time that the quality index is above a threshold) and limits of agreement. Main results. Results were determined over all recorded data, including changes in sensor position and posture. For respiratory effort, it was found that recursive and constrained computation of PCA reduced the estimation error significantly. For respiratory rate, a relation between coverage and limits of agreement was determined. If a minimum coverage of 80% was required, the limits of agreement could be kept below 1.45 breaths per minute. If the limits of agreement were constrained to 0.2 breaths per minute, a mean coverage of 5% was still attainable. Significance. We have shown that chest-worn accelerometery can be a robust and accurate method for measurement of respiratory features under realistic conditions.

Objective. Fetal heart rate (HR) monitoring is routinely used during pregnancy and labor to assess fetal well-being. The noninvasive fetal electrocardiogram (ECG), obtained by electrodes on the maternal abdomen, is a promising alternative to standard fetal monitoring. Subtraction of the maternal ECG from the abdominal measurements results in fetal ECG signals, in which the fetal HR can be determined typically through R-peak detection. However, the low signal-to-noise ratio and the nonstationary nature of the fetal ECG make R-peak detection a challenging task. Approach. We propose an alternative approach that instead of performing R-peak detection employs deep learning to directly determine the fetal HR from the extracted fetal ECG signals. We introduce a combination of dilated inception convolutional neural networks (CNN) with long short-term memory networks to capture both short-term and long-term temporal dynamics of the fetal HR. The robustness of the method is reinforced by a separate CNN-based classifier that estimates the reliability of the outcome. Main results. Our method achieved a positive percent agreement (within 10% of the actual fetal HR value) of 97.3% on a dataset recorded during labor and 99.6% on set-A of the 2013 Physionet/Computing in Cardiology Challenge exceeding top-performing state-of-the-art algorithms from the literature. Significance. The proposed method can potentially improve the accuracy and robustness of fetal HR extraction in clinical practice.

Objective: Respiratory activity is an essential parameter to monitor healthy and disordered sleep, and unobtrusive measurement methods have important clinical applications in diagnostics of sleep-related breathing disorders. We propose a respiratory activity surrogate extracted from wrist-worn reflective photoplethysmography validated on a heterogeneous dataset of 389 sleep recordings. Approach: The surrogate was extracted by interpolating the amplitude of the PPG pulses after evaluation of pulse morphological quality. Subsequent multistep post-processing was applied to remove parts of the surrogate with low quality and high motion levels. In addition to standard respiration rate performance, we evaluated the similarity between surrogate respiratory activity and reference inductance plethysmography of the thorax, using Spearman's correlations and spectral coherence, and assessed the influence of PPG signal quality, motion levels, sleep stages and obstructive sleep apnea. Main results: Prior to post-processing, the surrogate already had a strong similarity with the reference (correlation=0.54, coherence=0.81), and reached respiration rate estimation performance in line with the literature (estimation error=0.76±2.11 breaths/min). Detrimental effects of low PPG quality, high motion levels and sleep stage-dependent physiological phenomena were significantly mitigated by the proposed post-processing steps (correlation=0.62, coherence=0.88, estimation error=0.53±1.98 breaths/min). Significance: Wrist-worn PPG can be used to extract respiratory activity, thus allowing respiration monitoring in real-world sleep medicine applications using (consumer) wearable devices.

Objective: Arterial pulse pressure variation (PPV) is widely used for predicting fluid responsiveness and supporting fluid management in the operating room and intensive care unit. Available PPV algorithms have been typically validated for fluid responsiveness during episodes of hemodynamic stability. Yet, little is known about the performance of PPV algorithms during surgery, where fast changes of the blood pressure may affect the robustness of the presented PPV value. This work provides a comprehensive understanding of how various existing algorithmic designs affect the robustness of the presented PPV value during surgery, and proposes additional processing for the pulse pressure signal before calculating PPV. Approach: We recorded arterial blood pressure waveforms from 23 patients undergoing major abdominal surgery. To evaluate the performance, we designed three clinically relevant metrics. Main results and Significance: The results show that all algorithms performed well during episodes of hemodynamic stability. Moreover, it is demonstrated that the proposed processing helps improve the robustness of PPV during the entire course of surgery.

Objective Non-invasive fetal electrocardiography has the potential to provide vital information for evaluating the health status of the fetus. However, the low signal-to-noise ratio of the fetal electrocardiogram (ECG) impedes the applicability of the method in clinical practice. Quality improvement of the fetal ECG is of great importance for providing accurate information to enable support in medical decision-making. In this paper we propose the use of artificial intelligence for the task of one-channel fetal ECG enhancement as a post-processing step after maternal ECG suppression. Approach We propose a deep fully convolutional encoder-decoder framework, learning end-to-end mappings from noise-contaminated fetal ECGs to clean ones. Symmetric skip-layer connections are used between corresponding convolutional and transposed convolutional layers to help recover the signal details. Main results Experiments on synthetic data show an average improvement of 7.5 dB in the signal-to-noise ratio (SNR) for input SNRs in the range of -15 to 15 dB. Application of the method with real signals and subsequent ECG interval analysis demonstrates a root mean square error of 9.9 and 14 ms for the PR and QT intervals, respectively, when compared with simultaneous scalp measurements. The proposed network can achieve substantial noise removal on both synthetic and real data. In cases of highly noise-contaminated signals some morphological features might be unreliably reconstructed. Significance The presented method has the advantage of preserving individual variations in pulse shape and beat-to-beat intervals. Moreover, no prior knowledge on the power spectra of the noise or the pulse locations is required.

Objective: To date, mainly obtrusive methods (e.g., adhesive electrodes in electroencephalography or electrocardiography) are necessary to determine the preterm infant sleep states. As any obtrusive measure should be avoided in preterm infants because of their immature skin development, we investigated the possibility of automated sleep staging using electrocardiograph signals from non-adhesive capacitive electrocardiography. Approach: Capacitive electrocardiography data from eight different patients with a mean gestational age of 30 ± 2.5 weeks are compared to manually annotated reference signals from classic adhesive electrodes. The sleep annotations were performed by two trained observers based on behavioral observations. Main results: Based on these annotations, classification performance of the preterm infant active and quiet sleep states, based on capacitive electrocardiography signals, showed a kappa value of 0.56 ± 0.20. Adding wake and caretaking into the classification, a performance of Kappa 0.44 ± 0.21 was achieved. In-between sleep state performance showed a classification performance of Kappa 0.36 ± 0.12. Lastly, a performance for all sleep states of Kappa 0.35 ± 0.17 was attained.Significance: capacitive electrocardiography signals can be utilized to classify the central preterm infant sleep states, active and quiet sleep. With further research based on our results, automated classification of sleep states can become an essential instrument in the future intensive neonatal care for continuous brain maturation monitoring. Especially, being able to use capacitive electrocardiography for continuous monitoring is a significant contributor to reducing disruption and harm for this extreme fragile patient group.

Heart failure is marked by frequent hospital admissions, often as a consequence of pulmonary congestion. Current gold standard techniques for thoracic fluid measurement require invasive heamodynamic access and therefore they are not suitable for continuous monitoring. Changes in thoracic impedance (TI) may enable non-invasive early detection of congestion and prevention of unplanned hospitalizations. However, the usefulness of TI to assess thoracic fluid status is limited by inter-subject variability and by the lack of reliable normalization methods. Indicator dilution methods allow absolute fluid volume estimation; cardiac magnetic resonance (CMR) has been recently proposed to apply indicator dilution methods in a minimally-invasive manner. In this study, we aim to compare bio-impedance spectroscopy (BIS) and CMR for the assessment of thoracic fluid status, and to determine their ability to detect fluid displacement induced by a leg compression procedure in healthy volunteers. A pressure gradient was applied across each subject's legs for 5 min (100-60 mmHg, distal to proximal). Each subject underwent a continuous TI-BIS measurement during the procedure, and repeated CMR-based indicator dilution measurements on a 1.5 T scanner at baseline, during compression, and after pressure release. The Cole-Cole and the local density random walk models were used for parameter extraction from TI-BIS and indicator dilution measurements, respectively. Intra-thoracic blood volume index (ITBI) derived from CMR, and extracellular fluid resistance (R E) from TI-BIS, were considered as thoracic fluid status measures. Eight healthy volunteers were included in this study. An increase in ITBI of 45.2 47.2 ml m-2 was observed after the leg inflation (13.1 15.1% w.r.t. baseline, p < 0.05), while a decrease of -0.84 0.39 Ω in R E (-1.7 0.9% w.r.t. baseline, p < 0.05) was observed. ITBV and R E normalized by body mass index were strongly inversely correlated (r = -0.93, p < 0.05). In conclusion, an acute fluid displacement to the thoracic circulation was induced in healthy volunteers. Significant changes were observed in the considered thoracic fluid measures derived from BIS and CMR. Good correlation was observed between the two measurement techniques. Further clinical studies will be necessary to prospectively evaluate the value of a combination of the two techniques for prediction of re-hospitalizations after admission for heart failure.

In this paper, we propose an algorithm that classifies whether a generated cardiac arrhythmia alarm is true or false. The large number of false alarms in intensive care is a severe issue. The noise peaks caused by alarms can be high and in a noisy environment nurses can experience stress and fatigue. In addition, patient safety is compromised because reaction time of the caregivers to true alarms is reduced.The data for the algorithm development consisted of records of electrocardiogram (ECG), arterial blood pressure, and photoplethysmogram signals in which an alarm for either asystole, extreme bradycardia, extreme tachycardia, ventricular fibrillation or flutter, or ventricular tachycardia occurs. First, heart beats are extracted from every signal. Next, the algorithm selects the most reliable signal pair from the available signals by comparing how well the detected beats match between different signals based on ${{\text{F}}_{1}}$ -score and selecting the best match. From the selected signal pair, arrhythmia specific features, such as heart rate features and signal purity index are computed for the alarm classification. The classification is performed with five separate Random Forest models. In addition, information on the local noise level of the selected ECG lead is added to the classification. The algorithm was trained and evaluated with the PhysioNet/Computing in Cardiology Challenge 2015 data set. In the test set the overall true positive rates were 93 and 95% and true negative rates 80 and 83%, respectively for events with no information and events with information after the alarm. The overall challenge scores were 77.39 and 81.58.

Objective: To investigate the effect of maternal hyperoxygenation on fetal heart rate (FHR) when applied for suspected fetal distress during the second stage of term labor. Approach: A single-center randomized controlled trial was conducted in a tertiary care hospital in The Netherlands. Participants were included during the second stage of labor in case of an intermediary or abnormal FHR pattern. Patients were randomized to receive either 100% oxygen at 10 l/min until delivery, or conventional care without additional oxygen. The primary outcome was the change in FHR pattern before and after the onset of the study, measured as the change in depth and duration of FHR decelerations. Secondary outcome measures were features based on phase-rectified signal averaging (PRSA), baseline assignability, and deceleration characteristics of the FHR pattern. Main results: Between March 2016 and April 2018, 117 women were included. The FHR pattern could be analyzed for 71 participants, the other 46 women delivered before the end of the post time-frame. A 2.3% reduction in depth and duration of FHR decelerations was found after maternal hyperoxygenation, compared to a 10% increase in the control group (p = 0.24). Maternal hyperoxygenation had a significantly positive effect on PRSA metrics, with a decrease in PRSA-acceleration capacity (p = 0.03) and PRSA-deceleration capacity (p = 0.02) in the intervention group compared to the control group. Significance: The difference in depth and duration of decelerations after the start of the study was not significantly different between both study groups. A statistically significant positive effect on PRSA-deceleration capacity and PRSA-acceleration capacity was found after maternal hyperoxygenation, which might be associated with a positive effect on neonatal outcome.

OBJECTIVE: Atrial fibrillation (AF) is the most commonly experienced arrhythmia and it increases the risk of stroke and heart failure. The challenge in detecting the presence of AF is the occasional and asymptomatic manifestation of the condition. Long-term monitoring can increase the sensitivity of detecting intermittent AF episodes, however it is either cumbersome or invasive and costly with electrocardiography (ECG). Photoplethysmography (PPG) is an unobtrusive measuring modality enabling heart rate monitoring, and promising results have been presented in detecting AF. However, there is still limited knowledge about the applicability of the PPG solutions in free-living conditions. The aim of this study was to compare the inter-beat interval derived features for AF detection between ECG and wrist-worn PPG in daily life.APPROACH: The data consisted of 24 h ECG, PPG, and accelerometer measurements from 27 patients (eight AF, 19 non-AF). In total, seven features (Shannon entropy, root mean square of successive differences (RMSSD), normalized RMSSD, pNN40, pNN70, sample entropy, and coefficient of sample entropy (CosEn)) were compared. Body movement was measured with the accelerometer and used with three different thresholds to exclude PPG segments affected by movement.MAIN RESULTS: CosEn resulted as the best performing feature from ECG with Cohens kappa 0.95. When the strictest movement threshold was applied, the same performance was obtained with PPG (kappa = 0.96). In addition, pNN40 and pNN70 reached similar results with the same threshold (kappa = 0.95 and 0.94), but were more robust with respect to movement artefacts. The coverage of PPG was 24.0%-57.6% depending on the movement threshold compared to 92.1% of ECG.SIGNIFICANCE: The inter-beat interval features derived from PPG are equivalent to the ones from ECG for AF detection. Movement artefacts substantially worsen PPG-based AF monitoring in free-living conditions, therefore monitoring coverage needs to be carefully selected. Wrist-worn PPG still provides a promising technology for long-term AF monitoring.

Objective: To compare conditional random fields (CRF), hidden Markov models (HMMs) and Bayesian linear discriminants (LDs) for cardiorespiratory sleep stage classification on a five-class sleep staging task (wake/N1/N2/N3/REM), to explore the benefits of incorporating time information in the classification and to evaluate the feasibility of sleep staging on obstructive sleep apnea (OSA) patients. Approach: The classifiers with and without time information were evaluated with 10-fold cross-validation on five-, four- (wake/N1 + N2/N3/REM) and three-class (wake/NREM/REM) classification tasks using a data set comprising 443 night-time polysomnography (PSG) recordings of 231 participants (180 healthy participants, 100 of which had a 'regular' sleep architecture, and 51 participants previously diagnosed with OSA). Main results: CRF with time information (CRFt) outperforms all other classifiers on all tasks, achieving a median accuracy and Cohen's κ for all participants of 62.8% and 0.44 for five classes, 68.8% and 0.47 for four classes, and 77.6% and 0.55 for three classes. An advantage was found in training classifiers, specifically for 'regular' and 'OSA' participants, achieving an improvement in classification performance for these groups. For 'regular' participants, CRFt achieved a median accuracy and Cohen's κ of 67.0% and 0.51, 70.8% and 0.53 and 81.3% and 0.62 for five-, four- and three-classes respectively, and for 'OSA' patients, of 59.9% and 0.40, 69.7% and 0.45, and 75.8% and 0.51 for five-, four- and three-classes respectively. Significance: The results suggest that CRFt is not only better at learning and predicting more complex and irregular sleep architectures, but that it also performs reasonably well in five-class classification - the standard for sleep scoring used in clinical PSG. Additionally, and albeit with a decrease in performance when compared with healthy participants, sleep stage classification in OSA patients using cardiorespiratory features and CRFt seems feasible with reasonable accuracy.

During labor, uterine contractions can cause temporary oxygen deficiency for the fetus. In case of severe and prolonged oxygen deficiency this can lead to asphyxia. The currently used technique for detection of asphyxia, cardiotocography (CTG), suffers from a low specificity. Recent studies suggest that analysis of fetal heart rate variability (HRV) in addition to CTG can provide information on fetal distress. However, interpretation of fetal HRV during labor is difficult due to the influence of uterine contractions on fetal HRV. The aim of this study is therefore to investigate whether HRV features differ during contraction and rest periods, and whether these differences can improve the detection of asphyxia. To this end, a case-control study was performed, using 14 cases with asphyxia that were matched with 14 healthy fetuses. We did not find significant differences for individual HRV features when calculated over the fetal heart rate without separating contractions and rest periods (p > 0.30 for all HRV features). Separating contractions from rest periods did result in a significant difference. In particular the ratio between HRV features calculated during and outside contractions can improve discrimination between fetuses with and without asphyxia (p

Physiological measurement 37(1), 67-82 (2016). doi:10.1088/0967-3334/37/1/67, Published by IOP Publ., Bristol

In this work, a model to estimate systolic blood pressure (SBP) using photoplethysmography (PPG) and electrocardiography (ECG) is proposed. Data from 19 subjects doing a 40 min exercise was analyzed. Reference SBP was measured at the finger based on the volume-clamp principle. PPG signals were measured at the finger and forehead. After an initialization process for each subject at rest, the model estimated SBP every 30 s for the whole period of exercise. In order to build this model, 18 features were extracted from PPG signals by means of its waveform, first derivative, second derivative, and frequency spectrum. In addition, pulse arrival time (PAT) was derived as a feature from the combination of PPG and ECG. After evaluating four regression models, we chose multiple linear regression (MLR) to combine all derived features to estimate SBP. The contribution of each feature was quantified using its normalized weight in the MLR. To evaluate the performance of the model, we used a leave-one-subject-out cross validation. With the aim of exploring the potential of the model, we investigated the influences of the inclusion of PAT, regression models, measurement sites (finger and forehead), and posture change (lying, sitting, and standing). The results show that the inclusion of PAT reduced the standard deviation (SD) of the difference from 14.07 to 13.52 mmHg. There was no significant difference in the estimation performance between the model using finger- and forehead-derived PPG signals. Separate models are necessary for different postures. The optimized model using finger-derived PPG signals during physical exercise had a performance with a mean difference of 0.43 mmHg, an SD of difference of 13.52 mmHg, and median correlation coefficients of 0.86. Furthermore, we identified two groups of features that contributed more to SBP estimation compared to other features. One group consists of our proposed features depicting beat morphology. The other comprises existing features depicting the dicrotic notch. The present work demonstrates promising results of the SBP estimation model during physical exercise.

Patient monitoring generates a large number of alarms, the vast majority of which are false. Excessive non-actionable medical alarms lead to alarm fatigue, a well-recognized patient safety issue. While multiple approaches to reduce alarm fatigue have been explored, patterns in alarming and inter-alarm relationships, as they manifest in the clinical workspace, are largely a black-box and hamper research efforts towards reducing alarms. The aim of this study is to detect opportunities to safely reduce alarm pressure, by developing techniques to identify, capture and visualize patterns in alarms.Nearly 500 000 critical medical alarms were acquired from a neonatal intensive care unit over a 20 month period. Heuristic techniques were developed to extract the inter-alarm relationships. These included identifying the presence of alarm clusters, patterns of transition from one alarm category to another, temporal associations amongst alarms and determination of prevalent sequences in which alarms manifest.Desaturation, bradycardia and apnea constituted 86% of all alarms and demonstrated distinctive periodic increases in the number of alarms that were synchronized with nursing care and enteral feeding. By inhibiting further alarms of a category for a short duration of time (30 s/60 s), non-actionable physiological alarms could be reduced by 20%. The patterns of transition from one alarm category to another and the time duration between such transitions revealed the presence of close temporal associations and multiparametric derangement. Examination of the prevalent alarm sequences reveals that while many sequences comprised of multiple alarms, nearly 65% of the sequences were isolated instances of alarms and are potentially irreducible.Patterns in alarming, as they manifest in the clinical workspace were identified and visualized. This information can be exploited to investigate strategies for reducing alarms.

International audience; In this paper we propose an efficient method for denoising and extracting fiducial point (FP) of ECG signals. The method is based on a nonlinear dynamic model which uses Gaussian functions to model ECG waveforms. For estimating the model parameters, we use an extended Kalman filter (EKF). In this framework called EKF25, all the parameters of Gaussian functions as well as the ECG waveforms (P-wave, QRS complex and T-wave) in the ECG dynamical model, are considered as state variables. In this paper, the dynamic time warping method is used to estimate the nonlinear ECG phase observation. We compare this new approach with linear phase observation models. Using linear and nonlinear EKF25 for ECG denoising and nonlinear EKF25 for fiducial point extraction and ECG interval analysis are the main contributions of this paper. Performance comparison with other EKF-based techniques shows that the proposed method results in higher output SNR with an average SNR improvement of 12 dB for an input SNR of-8 dB. To evaluate the FP extraction performance, we compare the proposed method with a method based on partially collapsed Gibbs sampler and an established EKF-based method. The mean absolute error and the root mean square error of all FPs, across all databases are 14 msec and 22 msec, respectively, for our proposed method, with an advantage when using a nonlinear phase observation. These errors are significantly smaller than errors obtained with other methods. For ECG interval analysis, with an absolute mean error and a root mean square error of about 22 msec and 29 msec, the proposed method achieves better accuracy and smaller variability with respect to other methods. Keywords: Electrocardiogram (ECG), Extended Kalman Filter (EKF), Dynamic Time Warping (DTW), Fiducial Point Extraction, Denoising.

Motion artifacts (MA) have long been a problem in biopotential measurements. Adaptive filtering is widely used for optimal noise removal in many biomedical applications. However, the existing adaptive filtering methods involve the use of additional sensors, limiting the applicability of adaptive filtering for MA reduction. In the present study, a novel adaptive filtering method without need for additional sensors is proposed. In biopotential measurements, movement of the electrodes and their leads may cause variations not only in the skin and half-cell potential (motion artifacts), but also in the electrode-skin impedance. Such impedance variations may also cause power-line interference modulation (PLIM), resulting in additional spectral components around the power-line interference (PLI) in the frequency domain. Demodulation of the PLI may reflect the movement-induced electrode-skin impedance variation, and can therefore represent a reference signal for the adaptive filter. Preliminary validation on ECG measurements with seven volunteers showed a high correlation coefficient (R = 0.97) between MA and PLIM, and excellent MA removal by the proposed adaptive filter, possibly leading to improved analysis of biopotential signals.

Physiological measurement 36(10), 2027-2040 (2015). doi:10.1088/0967-3334/36/10/2027, Published by IOP Publ., Bristol

Weaning from mechanical ventilation is still one of the most challenging problems in intensive care. Unnecessary delays in discontinuation and weaning trials that are undertaken too early are both undesirable. This study investigated the contribution of spectral signals of heart rate variability (HRV) and respiratory flow, and their coherence to classifying patients on weaning process from mechanical ventilation. A total of 121 candidates for weaning, undergoing spontaneous breathing tests, were analyzed: 73 were successfully weaned (GSucc), 33 failed to maintain spontaneous breathing so were reconnected (GFail), and 15 were extubated after the test but reintubated within 48 h (GRein). The power spectral density and magnitude squared coherence (MSC) of HRV and respiratory flow signals were estimated. Dimensionality reduction was performed using principal component analysis (PCA) and sequential floating feature selection. The patients were classified using a fuzzy K-nearest neighbour method. PCA of the MSC gave the best classification with the highest accuracy of 92% classifying GSucc versus GFail patients, and 86% classifying GSucc versus GRein patients. PCA of the respiratory flow signal gave the best classification between GFail and GRein patients (79% accuracy). These classifiers showed a good balance between sensitivity and specificity. Besides, the spectral coherence between HRV and the respiratory flow signal, in patients on weaning trial process, can contribute to the extubation decision.

In order to obtain power spectral information on the fetal heart rate in stages of pregnancy earlier than labor an algorithm has been developed to calculate the fetal heart rate on a beat-to-beat basis from Doppler ultrasound cardiotocographic signals. The algorithm was evaluated by comparing the calculated fetal heart rate with the heart rate determined from direct ECG signals measured with a scalp electrode. Heart rates were compared both in time and frequency domain. In the time domain the results achieved by both methods correlate well (correlation coefficient = 0.977 (p