Heart Rate Variability, Deceleration Capacity of Heart Rate, and Death: A Veteran Twins Study.

BACKGROUND: Autonomic function can be measured noninvasively using heart rate variability (HRV), which indexes overall sympathovagal balance. Deceleration capacity (DC) of heart rate is a more specific metric of vagal modulation. Higher values of these measures have been associated with reduced mortality risk primarily in patients with cardiovascular disease, but their significance in community samples is less clear. METHODS AND RESULTS: This prospective twin study followed 501 members from the VET (Vietnam Era Twin) registry. At baseline, frequency domain HRV and DC were measured from 24-hour Holter ECGs. During an average 12-year follow-up, all-cause death was assessed via the National Death Index. Multivariable Cox frailty models with random effect for twin pair were used to examine the hazard ratios of death per 1-SD increase in log-transformed autonomic metrics. Both in the overall sample and comparing twins within pairs, higher values of low-frequency HRV and DC were significantly associated with lower hazards of all-cause death. In within-pair analysis, after adjusting for baseline factors, there was a 22% and 27% lower hazard of death per 1-SD increment in low-frequency HRV and DC, respectively. Higher low-frequency HRV and DC, measured during both daytime and nighttime, were associated with decreased hazard of death, but daytime measures showed numerically stronger associations. Results did not substantially vary by zygosity. CONCLUSIONS: Autonomic inflexibility, and especially vagal withdrawal, are important mechanistic pathways of general mortality risk, independent of familial and genetic factors.

Real-Time Detection of Sleep Arousals with a Head-Mounted Accelerometer.

Wearable electroencephalography (EEG) enables real-time interactions with the sleeping brain in real-life settings. An important parameter to monitor during these interactions are sleep arousals, i.e. temporary increases in EEG frequency, that compose sleep dynamics, but are challenging to detect without delay. We describe the development of an EEG- and accelerometer(ACC)-based sensing approach to detect arousals in real-time. We investigated the ability of these sensing modalities to timely and accurately detect arousals. When evaluated on 6 nights of mobile recordings, ACC had a median real-time delay of 2 s and was therefore better suited for an early detection of arousals than EEG (4.7 s). The detection performance was independent of sleep stages, but worked better on longer arousals. Our results demonstrate that a head-mounted ACC might be a cost-effective and easy-to-integrate solution for arousal detection where short delays are important or EEG signals are not available.

Association of Autonomic Activation with traumatic reminder challenges in posttraumatic stress disorder: A co-twin control study.

Post-traumatic stress disorder (PTSD) has been associated with cardiovascular disease (CVD), but the mechanisms remain unclear. Autonomic dysfunction, associated with higher CVD risk, may be triggered by acute PTSD symptoms. We hypothesized that a laboratory-based trauma reminder challenge, which induces acute PTSD symptoms, provokes autonomic dysfunction in a cohort of veteran twins. We investigated PTSD-associated real-time physiologic changes with a simulation of traumatic experiences in which the twins listened to audio recordings of a one-minute neutral script followed by a one-minute trauma script. We examined two heart rate variability metrics: deceleration capacity (DC) and logarithmic low frequency (log-LF) power from beat-to-beat intervals extracted from ambulatory electrocardiograms. We assessed longitudinal PTSD status with a structured clinical interview and the severity with the PTSD Symptoms Scale. We used linear mixed-effects models to examine twin dyads and account for cardiovascular and behavioral risk factors. We examined 238 male Veteran twins (age 68 ± 3 years old, 4% black). PTSD status and acute PTSD symptom severity were not associated with DC or log-LF measured during the neutral session, but were significantly associated with lower DC and log-LF during the traumatic script listening session. Long-standing PTSD was associated with a 0.38 (95% confidence interval, -0.83,-0.08) and 0.79 (-1.30,-0.29) standardized unit lower DC and log-LF, respectively, compared to no history of PTSD. Traumatic reminders in patients with PTSD lead to real-time autonomic dysregulation and suggest a potential causal mechanism for increased CVD risk, based on the well-known relationships between autonomic dysfunction and CVD mortality.

Label noise and self-learning label correction in cardiac abnormalities classification.

Objective. Learning to classify cardiac abnormalities requires large and high-quality labeled datasets, which is a challenge in medical applications. Small datasets from various sources are often aggregated to meet this requirement, resulting in a final dataset prone to label noise due to inter- and intra-observer variability and different expertise. It is well known that label noise can affect the performance and generalizability of the trained models. In this work, we explore the impact of label noise and self-learning label correction on the classification of cardiac abnormalities on large heterogeneous datasets of electrocardiogram (ECG) signals.Approach.A state-of-the-art self-learning multi-class label correction method for image classification is adapted to learn a multi-label classifier for electrocardiogram signals. We evaluated our performance using 5-fold cross-validation on the publicly available PhysioNet/Computing in Cardiology (CinC) 2021 Challenge data, with full and reduced sets of leads. Due to the unknown label noise in the testing set, we tested our approach on the MNIST dataset. We investigated the performance under different levels of structured label noise for both datasets.Main results.Under high levels of noise, the cross-validation results of self-learning label correction show an improvement of approximately 3% in the challenge score for the PhysioNet/CinC 2021 Challenge dataset and an improvement in accuracy of 5% and reduction of the expected calibration error of 0.03 for the MNIST dataset. We demonstrate that self-learning label correction can be used to effectively deal with the presence of unknown label noise, also when using a reduced number of ECG leads.

The temporal relationships between sleep disturbance and autonomic dysregulation: A co-twin control study.

INTRODUCTION: Sleep disturbance is associated with autonomic dysregulation, but the temporal directionality of this relationship remains uncertain. The objective of this study was to evaluate the temporal relationships between objectively measured sleep disturbance and daytime or nighttime autonomic dysregulation in a co-twin control study. METHODS: A total of 68 members (34 pairs) of the Vietnam Era Twin Registry were studied. Twins underwent 7-day in-home actigraphy to derive objective measures of sleep disturbance. Autonomic function indexed by heart rate variability (HRV) was obtained using 7-day ECG monitoring with a wearable patch. Multivariable vector autoregressive models with Granger causality tests were used to examine the temporal directionality of the association between daytime and nighttime HRV and sleep metrics, within twin pairs, using 7-day collected ECG data. RESULTS: Twins were all male, mostly white (96%), with mean (SD) age of 69 (2) years. Higher daytime HRV across multiple domains was bidirectionally associated with longer total sleep time and lower wake after sleep onset; these temporal dynamics were extended to a window of 48 h. In contrast, there was no association between nighttime HRV and sleep measures in subsequent nights, or between sleep measures from previous nights and subsequent nighttime HRV. CONCLUSIONS: Daytime, but not nighttime, autonomic function indexed by HRV has bidirectional associations with several sleep dimensions. Dysfunctions in autonomic regulation during wakefulness can lead to subsequent shorter sleep duration and worse sleep continuity, and vice versa, and their influence on each other may extend beyond 24 h.

Benchmarking Real-Time Algorithms for In-Phase Auditory Stimulation of Low Amplitude Slow Waves With Wearable EEG Devices During Sleep.

OBJECTIVE: In-phase stimulation of EEG slow waves (SW) during deep sleep has shown to improve cognitive function. SW enhancement is particularly desirable in subjects with low-amplitude SW such as older adults or patients suffering from neurodegeneration. However, existing algorithms to estimate the up-phase of EEG suffer from a poor phase accuracy at low amplitudes and when SW frequencies are not constant. METHODS: We introduce two novel algorithms for real-time EEG phase estimation on autonomous wearable devices, a phase-locked loop (PLL) and, for the first time, a phase vocoder (PV). We compared these phase tracking algorithms with a simple amplitude threshold approach. The optimized algorithms were benchmarked for phase accuracy, the capacity to estimate phase at SW amplitudes between 20 and 60 µV, and SW frequencies above 1 Hz on 324 home-based recordings from healthy older adults and Parkinson disease (PD) patients. Furthermore, the algorithms were implemented on a wearable device and the computational efficiency and the performance was evaluated in simulation and with a PD patient. RESULTS: All three algorithms delivered more than 70% of the stimulation triggers during the SW up-phase. The PV showed the highest capacity on targeting low-amplitude SW and SW with frequencies above 1 Hz. The hardware testing revealed that both PV and PLL have marginal impact on microcontroller load, while the efficiency of the PV was 4% lower. Active stimulation did not influence the phase tracking. CONCLUSION: This work demonstrated that phase-accurate auditory stimulation can also be delivered during fully remote sleep interventions in populations with low-amplitude SW.

Boosting Recovery During Sleep by Means of Auditory Stimulation.

Sufficient recovery during sleep is the basis of physical and psychological well-being. Understanding the physiological mechanisms underlying this restorative function is essential for developing novel approaches to promote recovery during sleep. Phase-targeted auditory stimulation (PTAS) is an increasingly popular technique for boosting the key electrophysiological marker of recovery during sleep, slow-wave activity (SWA, 1-4 Hz EEG power). However, it is unknown whether PTAS induces physiological sleep. In this study, we demonstrate that, when applied during deep sleep, PTAS accelerates SWA decline across the night which is associated with an overnight improvement in attentional performance. Thus, we provide evidence that PTAS enhances physiological sleep and demonstrate under which conditions this occurs most efficiently. These findings will be important for future translation into clinical populations suffering from insufficient recovery during sleep.

Neuromodulation by means of phase-locked auditory stimulation affects key marker of excitability and connectivity during sleep.

The propagating pattern of sleep slow waves (high-amplitude oscillations < 4.5 Hz) serves as a blueprint of cortical excitability and brain connectivity. Phase-locked auditory stimulation is a promising tool for the modulation of ongoing brain activity during sleep; however, its underlying mechanisms remain unknown. Here, eighteen healthy young adults were measured with high-density electroencephalography in three experimental conditions; one with no stimulation, one with up- and one with down-phase stimulation; ten participants were included in the analysis. We show that up-phase auditory stimulation on a right prefrontal area locally enhances cortical involvement and promotes traveling by increasing the propagating distance and duration of targeted small-amplitude waves. On the contrary, down-phase stimulation proves more efficient at perturbing large-amplitude waves and interferes with ongoing traveling by disengaging cortical regions and interrupting high synchronicity in the target area as indicated by increased traveling speed. These results point out different underlying mechanisms mediating the effects of up- and down-phase stimulation and highlight the strength of traveling wave analysis as a sensitive and informative method for the study of connectivity and cortical excitability alterations.

Transfer learning from ECG to PPG for improved sleep staging from wrist-worn wearables.

Objective.To develop a sleep staging method from wrist-worn accelerometry and the photoplethysmogram (PPG) by leveraging transfer learning from a large electrocardiogram (ECG) database.Approach.In previous work, we developed a deep convolutional neural network for sleep staging from ECG using the cross-spectrogram of ECG-derived respiration and instantaneous beat intervals, heart rate variability metrics, spectral characteristics, and signal quality measures derived from 5793 subjects in Sleep Heart Health Study (SHHS). We updated the weights of this model by transfer learning using PPG data derived from the Empatica E4 wristwatch worn by 105 subjects in the 'Emory Twin Study Follow-up' (ETSF) database, for whom overnight polysomnographic (PSG) scoring was available. The relative performance of PPG, and actigraphy (Act), plus combinations of these two signals, with and without transfer learning was assessed.Main results.The performance of our model with transfer learning showed higher accuracy (1-9 percentage points) and Cohen's Kappa (0.01-0.13) than those without transfer learning for every classification category. Statistically significant, though relatively small, incremental differences in accuracy occurred for every classification category as tested with the McNemar test. The out-of-sample classification performance using features from PPG and actigraphy for four-class classification was Accuracy (Acc) = 68.62% and Kappa = 0.44. For two-class classification, the performance was Acc = 81.49% and Kappa = 0.58.Significance.We proposed a combined PPG and actigraphy-based sleep stage classification approach using transfer learning from a large ECG sleep database. Results demonstrate that the transfer learning approach improves estimates of sleep state. The use of automated beat detectors and quality metrics means human over-reading is not required, and the approach can be scaled for large cross-sectional or longitudinal studies using wrist-worn devices for sleep staging.

Classification and Prediction of Post-Trauma Outcomes Related to PTSD Using Circadian Rhythm Changes Measured via Wrist-Worn Research Watch in a Large Longitudinal Cohort.

Post-Traumatic Stress Disorder (PTSD) is a psychiatric condition resulting from threatening or horrifying events. We hypothesized that circadian rhythm changes, measured by a wrist-worn research watch are predictive of post-trauma outcomes. APPROACH: 1618 post-trauma patients were enrolled after admission to emergency departments (ED). Three standardized questionnaires were administered at week eight to measure post-trauma outcomes related to PTSD, sleep disturbance, and pain interference with daily life. Pulse activity and movement data were captured from a research watch for eight weeks. Standard and novel movement and cardiovascular metrics that reflect circadian rhythms were derived using this data. These features were used to train different classifiers to predict the three outcomes derived from week-eight surveys. Clinical surveys administered at ED were also used as features in the baseline models. RESULTS: The highest cross-validated performance of research watch-based features was achieved for classifying participants with pain interference by a logistic regression model, with an area under the receiver operating characteristic curve (AUC) of 0.70. The ED survey-based model achieved an AUC of 0.77, and the fusion of research watch and ED survey metrics improved the AUC to 0.79. SIGNIFICANCE: This work represents the first attempt to predict and classify post-trauma symptoms from passive wearable data using machine learning approaches that leverage the circadian desynchrony in a potential PTSD population.

Multispectral Video Fusion for Non-Contact Monitoring of Respiratory Rate and Apnea.

Continuous monitoring of respiratory activity is desirable in many clinical applications to detect respiratory events. Non-contact monitoring of respiration can be achieved with near- and far-infrared spectrum cameras. However, current technologies are not sufficiently robust to be used in clinical applications. For example, they fail to estimate an accurate respiratory rate (RR) during apnea. We present a novel algorithm based on multispectral data fusion that aims at estimating RR also during apnea. The algorithm independently addresses the RR estimation and apnea detection tasks. Respiratory information is extracted from multiple sources and fed into an RR estimator and an apnea detector whose results are fused into a final respiratory activity estimation. We evaluated the system retrospectively using data from 30 healthy adults who performed diverse controlled breathing tasks while lying supine in a dark room and reproduced central and obstructive apneic events. Combining multiple respiratory information from multispectral cameras improved the root mean square error (RMSE) accuracy of the RR estimation from up to 4.64 monospectral data down to 1.60 breaths/min. The median F1 scores for classifying obstructive (0.75 to 0.86) and central apnea (0.75 to 0.93) also improved. Furthermore, the independent consideration of apnea detection led to a more robust system (RMSE of 4.44 vs. 7.96 breaths/min). Our findings may represent a step towards the use of cameras for vital sign monitoring in medical applications.

An unbiased, efficient sleep-wake detection algorithm for a population with sleep disorders: change point decoder.

STUDY OBJECTIVES: The usage of wrist-worn wearables to detect sleep-wake states remains a formidable challenge, particularly among individuals with disordered sleep. We developed a novel and unbiased data-driven method for the detection of sleep-wake and compared its performance with the well-established Oakley algorithm (OA) relative to polysomnography (PSG) in elderly men with disordered sleep. METHODS: Overnight in-lab PSG from 102 participants was compared with accelerometry and photoplethysmography simultaneously collected with a wearable device (Empatica E4). A binary segmentation algorithm was used to detect change points in these signals. A model that estimates sleep or wake states given the changes in these signals was established (change point decoder, CPD). The CPD's performance was compared with the performance of the OA in relation to PSG. RESULTS: On the testing set, OA provided sleep accuracy of 0.85, wake accuracy of 0.54, AUC of 0.67, and Kappa of 0.39. Comparable values for CPD were 0.70, 0.74, 0.78, and 0.40. The CPD method had sleep onset latency error of -22.9 min, sleep efficiency error of 2.09%, and underestimated the number of sleep-wake transitions with an error of 64.4. The OA method's performance was 28.6 min, -0.03%, and -17.2, respectively. CONCLUSIONS: The CPD aggregates information from both cardiac and motion signals for state determination as well as the cross-dimensional influences from these domains. Therefore, CPD classification achieved balanced performance and higher AUC, despite underestimating sleep-wake transitions. The CPD could be used as an alternate framework to investigate sleep-wake dynamics within the conventional time frame of 30-s epochs.

A low-complexity photoplethysmographic systolic peak detector for compressed sensed data.

OBJECTIVE: Recent advances in wearable technologies and signal processing have made it possible to perform health monitoring during everyday life activities. Despite the fact that new technologies allow the storage of large volumes of data on small devices, limitations remain when data have to be transmitted or processed with devices with both energy and computational constraints. APPROACH: This work focuses on the implementation and validation of a photoplethysmogram (PPG) low-complexity analysis method for sensors that acquire a compressed PPG signal through compressive sensing (CS) and allows for the accurate detection of the PPG systolic peak in the compressed domain. Three public datasets were used consisting of a total of about 52 h of PPG signals from 600 patients with normal and abnormal rhythms. Peaks were manually annotated by experts or derived from the annotated synchronized ECG. MAIN RESULTS: The proposed method achieved a pooled average F1 measure on the three datasets of 91% [Formula: see text] 8% for a 5% compression ratio (CR), 89% [Formula: see text] 10% for CR = 70% and 82% [Formula: see text] 12% for CR of 90%. The pooled average F1 measure on the original uncompressed data using an offline open source peak detector is F1 = 91% [Formula: see text] 11%. The proposed method is up to ∼100 times faster with respect to methods using decompression followed by peak detection. SIGNIFICANCE: Results demonstrate that it is possible to achieve detection performance, in terms of the F1 measure, comparable with those obtained on the original uncompressed and filtered signal, making the proposed approach appropriate for real-time wearable systems with energy and computation constraints.

Hypoglossal Nerve Stimulation and Heart Rate Variability: Analysis of STAR Trial Responders.

OBJECTIVE: Hypoglossal nerve stimulation represents a novel therapy for the treatment of moderate-severe obstructive sleep apnea; nonetheless, its cardiovascular effects are not known. We examine the effects of hypoglossal nerve stimulation on heart rate variability, a measure of autonomic function. STUDY DESIGN: Substudy of the STAR trial (Stimulation Therapy for Apnea Reduction): a multicenter prospective single-group cohort. SETTING: Academic and private practice centers in the United States and Europe. SUBJECTS AND METHODS: A subset of responder participants (n = 46) from the STAR trial was randomized to therapy withdrawal or therapy maintenance 12 months after surgery. Heart rate variability analysis included standard deviation of the R-R interval (SDNN), low-frequency power of the R-R interval, and high-frequency power of the R-R interval. Analysis was performed by sleep with 5-minute sliding window epochs during baseline, 12 months, and the maintenance/withdrawal period. RESULTS: A significant improvement from baseline to 12 months in heart rate variability was seen for SDNN and low frequency across all sleep stages. SDNN analysis demonstrated no change in the wake period (mean ± SD: 0.042 ± 0.01 vs 0.077 ± 0.07, P = .19). Reduction in SDNN was correlated to improvement in apnea-hypopnea index ( r = 0.39, P = .03). In the therapy withdrawal group, no significant changes in SDNN were seen for N1/N2, N3, or rapid eye movement sleep. CONCLUSION: Hypoglossal nerve stimulation therapy appears to reduce heart rate variability during sleep. This reduction was not affected by a 1-week withdrawal period. Larger prospective studies are required to better understand the effect of hypoglossal nerve stimulation on autonomic dysfunction in obstructive sleep apnea.

An open source benchmarked toolbox for cardiovascular waveform and interval analysis.

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.

Matched Filtering for Heart Rate Estimation on Compressive Sensing ECG Measurements.

OBJECTIVE: Compressive sensing (CS) has recently been applied as a low-complexity compression framework for long-term monitoring of electrocardiogram (ECG) signals using wireless body sensor networks. Long-term recording of ECG signals can be useful for diagnostic purposes and to monitor the evolution of several widespread diseases. In particular, beat-to-beat intervals provide important clinical information, and these can be derived from the ECG signal by computing the distance between QRS complexes (R-peaks). Numerous methods for R-peak detection are available for uncompressed ECG. However, in the case of compressed sensed data, signal reconstruction can be performed with relatively complex optimization algorithms, which may require significant energy consumption. This paper addresses the problem of heart rate estimation from CS ECG recordings, avoiding the reconstruction of the entire signal. METHODS: We consider a framework, where the ECG signals are represented under the form of CS linear measurements. The QRS locations are estimated in the compressed domain by computing the correlation of the compressed ECG and a known QRS template. RESULTS: Experiments on actual ECG signals show that our novel solution is competitive with methods applied to the reconstructed signals. CONCLUSION: Avoiding the reconstruction procedure, the proposed method proves to be very convenient for real-time low-power applications.

Atrial fibrillation detection on compressed sensed ECG.

OBJECTIVE: Compressive sensing (CS) approaches to electrocardiogram (ECG) analysis provide efficient methods for real time encoding of cardiac activity. In doing so, it is important to assess the downstream effect of the compression on any signal processing and classification algorithms. CS is particularly suitable for low power wearable devices, thanks to its low-complex digital or hardware implementation that directly acquires a compressed version of the signal through random projections. In this work, we evaluate the impact of CS compression on atrial fibrillation (AF) detection accuracy. APPROACH: We compare schemes with data reconstruction based on wavelet and Gaussian models, followed by a P&T-based identification of beat-to-beat (RR) intervals on the MIT-BIH atrial fibrillation database. A state-of-the-art AF detector is applied to the RR time series and the accuracy of the AF detector is then evaluated under different levels of compression. We also consider a new beat detection procedure which operates directly in the compressed domain, avoiding costly signal reconstruction procedures. MAIN RESULTS: We demonstrate that for compression ratios up to 30[Formula: see text] the AF detector applied to RR intervals derived from the compressed signal exhibits results comparable to those achieved when employing a standard QRS detector on the raw uncompressed signals, and exhibits only a 2% accuracy drop at a compression ratio of 60%. We also show that the Gaussian-based reconstruction approach is superior in terms of AF detection accuracy, with a negligible drop in performance at a compression ratio ⩽75%, compared to a wavelet approach, which exhibited a significant drop in accuracy at a compression ratio ⩾65%. SIGNIFICANCE: The results suggest that CS should be considered as a plausible methodology for both efficient real time ECG compression (at moderate compression rates) and for offline analysis (at high compression rates).

Energy and Quality Evaluation for Compressive Sensing of Fetal Electrocardiogram Signals.

This manuscript addresses the problem of non-invasive fetal Electrocardiogram (ECG) signal acquisition with low power/low complexity sensors. A sensor architecture using the Compressive Sensing (CS) paradigm is compared to a standard compression scheme using wavelets in terms of energy consumption vs. reconstruction quality, and, more importantly, vs. performance of fetal heart beat detection in the reconstructed signals. We show in this paper that a CS scheme based on reconstruction with an over-complete dictionary has similar reconstruction quality to one based on wavelet compression. We also consider, as a more important figure of merit, the accuracy of fetal beat detection after reconstruction as a function of the sensor power consumption. Experimental results with an actual implementation in a commercial device show that CS allows significant reduction of energy consumption in the sensor node, and that the detection performance is comparable to that obtained from original signals for compression ratios up to about 75%.

Separation and Analysis of Fetal-ECG Signals From Compressed Sensed Abdominal ECG Recordings.

OBJECTIVE: Analysis of fetal electrocardiogram (f-ECG) waveforms as well as fetal heart-rate (fHR) evaluation provide important information about the condition of the fetus during pregnancy. A continuous monitoring of f-ECG, for example using the technologies already applied for adults ECG tele-monitor-ing (e.g., Wireless Body Sensor Networks (WBSNs)), may increase early detection of fetal arrhythmias. In this study, we propose a novel framework, based on compressive sensing (CS) theory, for the compression and joint detection/classification of mother and fetal heart beats. METHODS: Our scheme is based on the sparse representation of the components derived from independent component analysis (ICA), which we propose to apply directly in the compressed domain. Detection and classification is based on the activated atoms in a specifically designed reconstruction dictionary. RESULTS: Validation of the proposed compression and detection framework has been done on two publicly available datasets, showing promising results (sensitivity S = 92.5 %, P += 92 % , F1 = 92.2 % for the Silesia dataset and S = 78 % , P += 77 %, F1 = 77.5 % for the Challenge dataset A, with average reconstruction quality PRD = 8.5 % and PRD = 7.5 %, respectively). CONCLUSION: The experiments confirm that the proposed framework may be used for compression of abdominal f-ECG and to obtain real-time information of the fHR, providing a suitable solution for real time, very low-power f-ECG monitoring. SIGNIFICANCE: To the authors' knowledge, this is the first time that a framework for the low-power CS compression of fetal abdominal ECG is proposed combined with a beat detector for an fHR estimation.