ECG-derived Respiration Signals Research Articles

Tracking Tidal Volume From Holter and Wearable Armband Electrocardiogram Monitoring.

A novel method for tracking the tidal volume (TV) from electrocardiogram (ECG) is presented. The method is based on the amplitude of ECG-derived respiration (EDR) signals. Three different morphology-based EDR signals and three different amplitude estimation methods have been studied, leading to a total of 9 amplitude-EDR (AEDR) signals per ECG channel. The potential of these AEDR signals to track the changes in TV was analyzed. These methods do not need a calibration process. In addition, a personalized-calibration approach for TV estimation is proposed, based on a linear model that uses all AEDR signals from a device. All methods have been validated with two different ECG devices: a commercial Holter monitor, and a custom-made wearable armband. The lowest errors for the personalized-calibration methods, compared to a reference TV, were -3.48% [-17.41% / 12.93%] (median [first quartile / third quartile]) for the Holter monitor, and 0.28% [-10.90% / 17.15%] for the armband. On the other hand, medians of correlations to the reference TV were higher than 0.8 for uncalibrated methods, while they were higher than 0.9 for personal-calibrated methods. These results suggest that TV changes can be tracked from ECG using either a conventional (Holter) setup, or our custom-made wearable armband. These results also suggest that the methods are not as reliable in applications that induce small changes in TV, but they can be potentially useful for detecting large changes in TV, such as sleep apnea/hypopnea and/or exacerbations of a chronic respiratory disease.

ECG-based estimation of respiration-induced autonomic modulation of AV nodal conduction during atrial fibrillation.

Introduction: Information about autonomic nervous system (ANS) activity may offer insights about atrial fibrillation (AF) progression and support personalized AF treatment but is not easily accessible from the ECG. In this study, we propose a new approach for ECG-based assessment of respiratory modulation in atrioventricular (AV) nodal refractory period and conduction delay. Methods: A 1-dimensional convolutional neural network (1D-CNN) was trained to estimate respiratory modulation of AV nodal conduction properties from 1-minute segments of RR series, respiration signals, and atrial fibrillatory rates (AFR) using synthetic data that replicates clinical ECG-derived data. The synthetic data were generated using a network model of the AV node and 4 million unique model parameter sets. The 1D-CNN was then used to analyze respiratory modulation in clinical deep breathing test data of 28 patients in AF, where an ECG-derived respiration signal was extracted using a novel approach based on periodic component analysis. Results: We demonstrated using synthetic data that the 1D-CNN can estimate the respiratory modulation from RR series alone with a Pearson sample correlation of r = 0.805 and that the addition of either respiration signal (r = 0.830), AFR (r = 0.837), or both (r = 0.855) improves the estimation. Discussion: Initial results from analysis of ECG data suggest that our proposed estimate of respiration-induced autonomic modulation, a resp, is reproducible and sufficiently sensitive to monitor changes and detect individual differences. However, further studies are needed to verify the reproducibility, sensitivity, and clinical significance of a resp.

Unsupervised ensembling of multiple software sensors with phase synchronization: a robust approach for electrocardiogram-derived respiration

Objective. We aimed to fuse the outputs of different electrocardiogram-derived respiration (EDR) algorithms to create one higher quality EDR signal. Methods. We viewed each EDR algorithm as a software sensor that recorded breathing activity from a different vantage point, identified high-quality software sensors based on the respiratory signal quality index, aligned the highest-quality EDRs with a phase synchronization technique based on the graph connection Laplacian, and finally fused those aligned, high-quality EDRs. We refer to the output as the sync-ensembled EDR signal. The proposed algorithm was evaluated on two large-scale databases of whole-night polysomnograms. We evaluated the performance of the proposed algorithm using three respiratory signals recorded from different hardware sensors, and compared it with other existing EDR algorithms. A sensitivity analysis was carried out for a total of five cases: fusion by taking the mean of EDR signals, and the four cases of EDR signal alignment without and with synchronization and without and with signal quality selection. Results. The sync-ensembled EDR algorithm outperforms existing EDR algorithms when evaluated by the synchronized correlation ( γ -score), optimal transport (OT) distance, and estimated average respiratory rate score, all with statistical significance. The sensitivity analysis shows that the signal quality selection and EDR signal alignment are both critical for the performance, both with statistical significance. Conclusion. The sync-ensembled EDR provides robust respiratory information from electrocardiogram. Significance. Phase synchronization is not only theoretically rigorous but also practical to design a robust EDR.

A Novel ECG-Derived Respiration Method Combining Frequency-Domain Feature and Interacting Multiple Model Smoother.

ECG-derived respiration (EDR) is a low-cost and productive means for capturing respiratory activity. In particular, as the primary procedure in some cardiorespiratory-related studies, the quality of EDR is decisive for the performance of subsequent analyses. In this paper, we proposed a novel EDR method based on the feature derived from the first moment (mean frequency) of the power spectrum (FMS). After obtaining the EDR signal from the feature, we introduced the Interacting Multiple Model (IMM) smoother to enhance the similarity of the EDR signal to the reference respiration. The assessment of the approach consisted of two steps: 1) the performance of extracted feature was verified against R-peak misalignment and noise. 2) the enhancement of IMM smoother to EDR waveforms was evaluated based on waveform correlation and respiratory rate estimation. All the assessments were conducted under the Fantasia database and Drivers database. The FMS improved robustness against R peak offsets compared to most established feature-based EDR algorithms, but a slight 5% improvement of waveform correlation against RR interval-based feature under accurate R peaks. The IMM smoother performed similarly with the Kalman filter in the static database but realized the enhancement of some extent of the EDR waveform in the ambulatory database. The proposed method investigated frequency domain mapping of ECG morphological changes caused by respiratory modulation and explained the EDR signal as a non-stationary time series, which provided a direction of better fitting the natural respiration process and enhancing the EDR waveform.

Can Respiration Complexity Help the Diagnosis of Disorders of Consciousness in Rehabilitation?

Autonomic Nervous System (ANS) activity, as cardiac, respiratory and electrodermal activity, has been shown to provide specific information on different consciousness states. Respiration rates (RRs) are considered indicators of ANS activity and breathing patterns are currently already included in the evaluation of patients in critical care. The aim of this work was to derive a proxy of autonomic functions via the RR variability and compare its diagnostic capability with known neurophysiological biomarkers of consciousness. In a cohort of sub-acute patients with brain injury during post-acute rehabilitation, polygraphy (ECG, EEG) recordings were collected. The EEG was labeled via descriptors based on American Clinical Neurophysiology Society terminology and the respiration variability was extracted by computing the Approximate Entropy (ApEN) of the ECG-derived respiration signal. Competing logistic regressions were applied to evaluate the improvement in model performances introduced by the RR ApEN. Higher RR complexity was significantly associated with higher consciousness levels and improved diagnostic models' performances in contrast to the ones built with only electroencephalographic descriptors. Adding a quantitative, instrumentally based complexity measure of RR variability to multimodal consciousness assessment protocols may improve diagnostic accuracy based only on electroencephalographic descriptors. Overall, this study promotes the integration of biomarkers derived from the central and the autonomous nervous system for the most comprehensive diagnosis of consciousness in a rehabilitation setting.

Identification of Respiration Types Through Respiratory Signal Derived From Clinical and Wearable Electrocardiograms.

To evaluate suitability of respiratory signals derived from clinical 12-lead electrocardiograms (ECGs) and wearable 1-lead ECG to identify different respiration types. ECGs were simultaneously acquired through the M12R ECG Holter by Global Instrumentation and the chest strap BioHarness 3.0 by Zephyr from 42 healthy subjects alternating normal breathing, breath holding, and deep breathing. Respiration signals were derived from the ECGs through the Segmented-Beat Modulation Method (SBMM)-based algorithm and the algorithms by Van Gent, Charlton, Soni and Sarkar, and characterized in terms of breathing rate and amplitude. Respiration classification was performed through a linear support vector machine and evaluated by F1 score. Best F1 scores were 86.59%(lead V2) and 80.57%, when considering 12-lead and 1-lead ECGs, respectively, and using SBMM-based algorithm. ECG-derived respiratory signals allow reliable identification of different respiration types even when acquired through wearable sensors, if associated to appropriate processing algorithms, such as the SBMM-based algorithm.

Transcutaneous Cervical Vagus Nerve Stimulation Reduces Respiratory Variability in the Context of Opioid Withdrawal.

Opioid withdrawal's physiological effects are a major impediment to recovery from opioid use disorder (OUD). Prior work has demonstrated that transcutaneous cervical vagus nerve stimulation (tcVNS) can counteract some of opioid withdrawal's physiological effects by reducing heart rate and perceived symptoms. The purpose of this study was to assess the effects of tcVNS on respiratory manifestations of opioid withdrawal - specifically, respiratory timings and their variability. Patients with OUD (N = 21) underwent acute opioid withdrawal over the course of a two-hour protocol. The protocol involved opioid cues to induce opioid craving and neutral conditions for control purposes. Patients were randomly assigned to receive double-blind active tcVNS (n = 10) or sham stimulation (n = 11) throughout the protocol. Respiratory effort and electrocardiogram-derived respiration signals were used to estimate inspiration time (Ti), expiration time (Te), and respiration rate (RR), along with each measure's variability quantified via interquartile range (IQR). Comparing the active and sham groups, active tcVNS significantly reduced IQR(Ti) - a variability measure - compared to sham stimulation (p = .02). Relative to baseline, the active group's median change in IQR(Ti) was 500 ms less than the sham group's median change in IQR(Ti). Notably, IQR(Ti) was found to be positively associated with post-traumatic stress disorder symptoms in prior work. Therefore, a reduction in IQR(Ti) suggests that tcVNS downregulates the respiratory stress response associated with opioid withdrawal. Although further investigations are necessary, these results promisingly suggest that tcVNS - a non-pharmacologic, non-invasive, readily implemented neuromodulation approach - can serve as a novel therapy to mitigate opioid withdrawal symptoms.

A subspace projection approach to quantify respiratory variations in the f-wave frequency trend.

Background: The autonomic nervous system (ANS) is known as a potent modulator of the initiation and perpetuation of atrial fibrillation (AF), hence information about ANS activity during AF may improve treatment strategy. Respiratory induced ANS variation in the f-waves of the ECG may provide such information. Objective: This paper proposes a novel approach for improved estimation of such respiratory induced variations and investigates the impact of deep breathing on the f-wave frequency in AF patients. Methods: A harmonic model is fitted to the f-wave signal to estimate a high-resolution f-wave frequency trend, and an orthogonal subspace projection approach is employed to quantify variations in the frequency trend that are linearly related to respiration using an ECG-derived respiration signal. The performance of the proposed approach is evaluated and compared to that of a previously proposed bandpass filtering approach using simulated f-wave signals. Further, the proposed approach is applied to analyze ECG data recorded for 5 min during baseline and 1 min deep breathing from 28 AF patients from the Swedish cardiopulmonary bioimage study (SCAPIS). Results: The simulation results show that the estimates of respiratory variations obtained using the proposed approach are more accurate than estimates obtained using the previous approach. Results from the analysis of SCAPIS data show no significant differences between baseline and deep breathing in heart rate (75.5 ± 22.9 vs. 74 ± 22.3) bpm, atrial fibrillation rate (6.93 ± 1.18 vs. 6.94 ± 0.66) Hz and respiratory f-wave frequency variations (0.130 ± 0.042 vs. 0.130 ± 0.034) Hz. However, individual variations are large with changes in heart rate and atrial fibrillatory rate in response to deep breathing ranging from −9% to +5% and −8% to +6%, respectively and there is a weak correlation between changes in heart rate and changes in atrial fibrillatory rate (r = 0.38, p < 0.03). Conclusion: Respiratory induced f-wave frequency variations were observed at baseline and during deep breathing. No significant changes in the magnitude of these variations in response to deep breathing was observed in the present study population.

Respiratory Rate Estimation Using U-Net-Based Cascaded Framework From Electrocardiogram and Seismocardiogram Signals.

At-home monitoring of respiration is of critical urgency especially in the era of the global pandemic due to COVID-19. Electrocardiogram (ECG) and seismocardiogram (SCG) signals-measured in less cumbersome contact form factors than the conventional sealed mask that measures respiratory air flow-are promising solutions for respiratory monitoring. In particular, respiratory rates (RR) can be estimated from ECG-derived respiratory (EDR) and SCG-derived respiratory (SDR) signals. Yet, non-respiratory artifacts might still be present in these surrogates of respiratory signals, hindering the accuracy of the RRs estimated. In this paper, we propose a novel U-Net-based cascaded framework to address this problem. The EDR and SDR signals were transformed to the spectro-temporal domain and subsequently denoised by a 2D U-Net to reduce the non-respiratory artifacts. We have shown that the U-Net that fused an EDR input and an SDR input achieved a low mean absolute error of 0.82 breaths per minute (bpm) and a coefficient of determination (R2) of 0.89 using data collected from our chest-worn wearable patch. We also qualitatively provided insights on the complementariness between EDR and SDR signals and demonstrated the generalizability of the proposed framework. ECG and SCG collected from a chest-worn wearable patch can complement each other and yield reliable RR estimation using the proposed cascaded framework. We anticipate that convenient and comfortable ECG and SCG measurement systems can be augmented with this framework to facilitate pervasive and accurate RR measurement.

Robust Estimation of Respiratory Variability Uncovers Correlates of Limbic Brain Activity and Transcutaneous Cervical Vagus Nerve Stimulation in the Context of Traumatic Stress.

Variations in respiration patterns are a characteristic response to distress due to underlying neurorespiratory couplings. Yet, no work to date has quantified respiration pattern variability (RPV) in the context of traumatic stress and studied its functional neural correlates - this analysis aims to address this gap. Fifty human subjects with prior traumatic experiences (24 with posttraumatic stress disorder (PTSD)) completed a ∼3-hr protocol involving personalized traumatic scripts and active/sham (double-blind) transcutaneous cervical vagus nerve stimulation (tcVNS). High-resolution positron emission tomography functional neuroimages, electrocardiogram (ECG), and respiratory effort (RSP) data were collected during the protocol. Supplementing the RSP signal with ECG-derived respiration for quality assessment and timing extraction, RPV metrics were quantified and analyzed. Specifically, correlation analyses were performed using neuroactivity in selected limbic regions, and responses to active and sham tcVNS were compared. The single-lag unscaled autocorrelation of respiration rate correlated negatively with left amygdala activity and positively with right rostromedial prefrontal cortex (rmPFC) activity for non-PTSD; it also correlated negatively with left and right insulae activity and positively with right rmPFC activity for PTSD. The single-lag unscaled autocorrelation of expiration time was greater following active stimulation for non-PTSD. Quantifying RPV is of demonstrable importance to assessing trauma-induced changes in neural function and tcVNS effects on respiratory physiology. This is the first demonstration of RPV's pertinence to traumatic stress- and tcVNS-induced neurorespiratory responses. The open-source processing pipeline elucidated herein uniquely includes both RSP and ECG-derived respiration signals for quality assessment, timing estimation, and RPV extraction.

QRS Morphology-Based EDR Signal—Factors Determining its Properties

Respiration-induced signals contain a clinically significant information. It could be obtained utilizing both direct and indirect methods. ECG-Derived Respiration (EDR) method is the latter one. However, in this case, two approaches could be distinguished. First one is based on determining changes in the morphology of QRS complexes while the second one is based on the Respiratory Sinus Arrhythmia (RSA) mechanism. The former approach is discussed in the paper. The basic properties of QRS morphology-based EDR signal were evaluated by means of simulations performed using a developed FEM model and appropriate experiments. In effect, basing on changes of the leads’ voltages induced by a heart translation or rotation, or by both mechanisms undergoing simultaneously, the sensitivity-like surfaces and the synthetic EDR signals were obtained. A very good agreement of the experimental and simulation results was achieved. The QRS morphology-based signal’s properties strongly depend on geometrical relation of the used lead to dominating direction of the heart translation, its rotation, and on personal relation between these two mechanisms. The conclusions presented should be taken into account especially when developing or using QRS morphology-based approach in respiratory monitoring systems.

Transcutaneous Cervical Vagus Nerve Stimulation Lengthens Exhalation in the Context of Traumatic Stress.

Transcutaneous electrical stimulation of the vagus nerve is believed to deliver afferent signaling to the brain that, in turn, yields downstream changes in peripheral physiology, including cardiovascular and respiratory parameters. While the effects of transcutaneous cervical vagus nerve stimulation (tcVNS) on these parameters have been studied broadly, little is known regarding the specific effects of tcVNS on exhalation time and the spontaneous respiration cycle. By understanding such effects, tcVNS could be used to counterbalance sympathetic hyperactivity following distress by enhancing vagal tone through parasympathetically favored modulation of inspiration and expiration - specifically, lengthened expiration relative to inspiration. We thus investigated the effects of tcVNS on respiration timings by decomposing the respiration cycle into inspiration and expiration times and incorporating state-of-the-art respiration quality assessment algorithms for respiratory effort belt and electrocardiogram derived respiration signals. This enabled robust estimation of respiration timings from quality measurements alone. We thereby found that tcVNS increases expiration time minutes after stimulation, compared to a sham control (N = 26). This suggests that tcVNS could counteract sympathovagal imbalance, given the relationship between expiration and heightened vagal tone.

Automatic detection of obstructive and restrictive lung disease from features extracted from ECG and ECG derived respiration signals

Early detection of both obstructive and restrictive lung diseases is essential for maintaining proper lung function, which plays a pivotal role in decreasing the escalating number of deaths worldwide. Even though conventional methods are vastly used for diagnosing these two diseases, their use is often associated with high cost, low comfort, high patient-effort dependency, and interference with natural breathing. Consequently, the development of a non-invasive, inexpensive, reliable, and comfortable technique for detecting these respiratory diseases has become an important area of research. Therefore, unobtrusive and automatic identification of these two pulmonary diseases based on ECG and ECG derived respiration (EDR) has been presented in this study. Temporal information was extracted from the morphological variations observed in both ECG and EDR signals to derive distinctive features. Finally, supervised classifiers were used for differentiating the subjects into normal, obstructive, and restrictive categories. Performance of the classifiers evaluated on 90 subjects (both normal and diseased) demonstrated classification accuracy of more than 98 %. Results indicate that the proposed method can be effectively utilized for primary identification of obstructive and restrictive lung diseases.

Respiration Monitoring via Forcecardiography Sensors

In the last few decades, a number of wearable systems for respiration monitoring that help to significantly reduce patients’ discomfort and improve the reliability of measurements have been presented. A recent research trend in biosignal acquisition is focusing on the development of monolithic sensors for monitoring multiple vital signs, which could improve the simultaneous recording of different physiological data. This study presents a performance analysis of respiration monitoring performed via forcecardiography (FCG) sensors, as compared to ECG-derived respiration (EDR) and electroresistive respiration band (ERB), which was assumed as the reference. FCG is a novel technique that records the cardiac-induced vibrations of the chest wall via specific force sensors, which provide seismocardiogram-like information, along with a novel component that seems to be related to the ventricular volume variations. Simultaneous acquisitions were obtained from seven healthy subjects at rest, during both quiet breathing and forced respiration at higher and lower rates. The raw FCG sensor signals featured a large, low-frequency, respiratory component (R-FCG), in addition to the common FCG signal. Statistical analyses of R-FCG, EDR and ERB signals showed that FCG sensors ensure a more sensitive and precise detection of respiratory acts than EDR (sensitivity: 100% vs. 95.8%, positive predictive value: 98.9% vs. 92.5%), as well as a superior accuracy and precision in interbreath interval measurement (linear regression slopes and intercepts: 0.99, 0.026 s (R2 = 0.98) vs. 0.98, 0.11 s (R2 = 0.88), Bland–Altman limits of agreement: ±0.61 s vs. ±1.5 s). This study represents a first proof of concept for the simultaneous recording of respiration signals and forcecardiograms with a single, local, small, unobtrusive, cheap sensor. This would extend the scope of FCG to monitoring multiple vital signs, as well as to the analysis of cardiorespiratory interactions, also paving the way for the continuous, long-term monitoring of patients with heart and pulmonary diseases.

A method based on cardiopulmonary coupling analysis for sleep quality assessment with FPGA implementation

The relevance of sleep quality examination for clinical diagnosis is increasing with the discovery of new relationships with several diseases and the overall wellness. This assessment is commonly performed by conducting interviews with the subjects, evaluating the self-report and psychological variables. However, this approach has a major constraint since the subject is a poor self-observer of sleep behaviors. To address this issue, a method based on the examination of a physiological signal was developed. Specifically, the single-lead electrocardiogram signal was examined to estimate the cardiopulmonary coupling between the electrocardiogram derived respiration signal and the normal-to-normal sinus interbeat interval series. A one dimensional array was created from the coupling signal and was fed to a convolutional neural network to estimate the sleep quality. The age-related cyclic alternating pattern rate percentages in healthy subjects was considered as the classification reference. An accuracy of 91 % was attained by the developed model, with an area under the receiver operating characteristic curve of 97 %. The performance is in the upper range of the reported performance by the works presented in the state of the art, advocating the relevance of the proposed method. The model was implemented in a small field programmable gate array board. Hence, a home monitoring device was created, composed of a processing unit, a sensing module and a display unit. The device is resilient, easy to self-assemble and operate, and can conceivably be employed for clinical analysis.

Wave-shape oscillatory model for nonstationary periodic time series analysis

The oscillations observed in many time series, particularly in biomedicine, exhibit morphological variations over time. These morphological variations are caused by intrinsic or extrinsic changes to the state of the generating system, henceforth referred to as dynamics. To model these time series (including and specifically pathophysiological ones) and estimate the underlying dynamics, we provide a novel wave-shape oscillatory model. In this model, time-dependent variations in cycle shape occur along a manifold called the wave-shape manifold. To estimate the wave-shape manifold associated with an oscillatory time series, study the dynamics, and visualize the time-dependent changes along the wave-shape manifold, we propose a novel algorithm coined Dynamic Diffusion map (DDmap) by applying the well-established diffusion maps (DM) algorithm to the set of all observed oscillations. We provide a theoretical guarantee on the dynamical information recovered by the DDmap algorithm under the proposed model. Applying the proposed model and algorithm to arterial blood pressure (ABP) signals recorded during general anesthesia leads to the extraction of nociception information. Applying the wave-shape oscillatory model and the DDmap algorithm to cardiac cycles in the electrocardiogram (ECG) leads to ectopy detection and a new ECG-derived respiratory signal, even when the subject has atrial fibrillation.

Comparison between Embroidered and Gel Electrodes on ECG-Derived Respiration Rate.

Respiratory rate (RR), which is an essential observation for prediagnosis, can be obtained indirectly from the electrocardiogram (ECG), the so-called ECG-derived respiration (EDR). In this paper, we compared embroidered electrodes with gel electrodes on their performance in capturing EDR signals and analysed which frequency feature best estimates RR. Data were collected from 9 healthy subjects. Results reveal that (1) embroidered electrodes performed similarly to gel electrodes (P = 0.077), (2) using the median frequency of the obtained EDR signals is significantly better (P = 0.01) than the counting methods in the time domain. The obtained results are relevant for the future development of textile-based sensors.

Electrocardiogram Derived Respiration for Tracking Changes in Tidal Volume from a Wearable Armband.

A pilot study on tracking changes in tidal volume (TV) using ECG signals acquired by a wearable armband is presented. The wearable armband provides three ECG channels by using three pairs of dry electrodes, resulting in a device that is convenient for long-term daily monitoring. An additional ECG channel was derived by computing the first principal component of the three original channels (by means of principal component analysis). Armband and spirometer signals were simultaneously recorded from five healthy subjects who were instructed to breathe with varying TV. Three electrocardiogram derived respiration (EDR) methods based on QRS complex morphology were studied: the QRS slopes range (SR), the R-wave angle (Փ), and the R-S amplitude (RS). The peak-to-peak amplitudes of these EDR signals were estimated as surrogates for TV, and their correlations with the reference TV (estimated from the spirometer signal) were computed. In addition, a multiple linear regression model was calculated for each subject, using the peak-to-peak amplitudes from the three EDR methods from the four ECG channels. Obtained correlations between TV and EDR peak-to-peak amplitude ranged from 0.0448 up to 0.8491. For every subject, a moderate correlation (>0.5) was obtained for at least one EDR method. Furthermore, the correlations obtained for the subject-specific multiple linear regression model ranged from 0.8234 up to 0.9154, and the goodness of fit was 0.73±0.07 (median ± standard deviation). These results suggest that the peak-to-peak amplitudes of the EDR methods are linearly related to the TV. opening the possibility of estimating TV directly from an armband ECG device.Clinical Relevance- This opens the door to possible continuous monitoring of TV from the armband by using EDR.

Detection of sleep apnea from heart beat interval and ECG derived respiration signals using sliding mode singular spectrum analysis

The heartbeat interval (HBI) signal (RR-time series), and electrocardiogram (ECG) derived respiration (EDR) signal quantify the information about the cardiopulmonary activity, and monitoring these two signals simultaneously will provide more information for the sleep apnea detection. This paper proposes a novel approach to detect sleep apnea using both HBI and EDR signals. The approach consists of the decomposition of both HBI and EDR signals into reconstructed components (RCs) or modes using a data-driven signal processing approach namely, the sliding mode singular spectrum analysis (SM-SSA), extraction of features from each RC, and the use of classifier for the detection of sleep apnea. The features such as the mean and the standard deviation values are extracted from the instantaneous amplitude (IA) and instantaneous frequency (IF) of each RC of both HBI and EDR signals. The classifiers, such as the stacked autoencoder based deep neural network (SAE-DNN), and support vector machine (SVM) are considered to classify normal and apnea episodes using the statistical features obtained from the RCs of HBI and EDR signals. The proposed approach is evaluated using different public databases such as apnea-ECG database, University College Dublin (UCD) database, and Physionet challenge database, respectively. The results demonstrate that the combination of the statistical features and SVM classifier has the sensitivity and specificity values of 82.45% and 79.72%, respectively using the 10-fold cross-validation based selection of training and test instances from the apnea-ECG database. Moreover, for subject-specific cross-validation, the proposed method has an average sensitivity and specificity values of 62.87%, and 81.53%, respectively. The proposed method has achieved the accuracy values of 94.3%, and 72% for per-recording based classification of sleep apnea and normal classes using signals from apnea-ECG and UCD databases.

Automatic classification of apnea and normal subjects using new features extracted from HRV and ECG-derived respiration signals

A novel framework for automatic detection of obstructive sleep apnea (OSA) is introduced in which a symbolic dynamics method, alphabet entropy, along with other well-known features such as fuzzy/approximate and sample entropy are calculated from ECG-derived respiration (EDR) and heart rate variability (HRV) signals. In addition, six different algorithms are employed in the extraction of the EDR signal from a single-lead ECG, and the results are compared. The sequential feature selection method is applied to pick the most effective features. Finally, the picked features are fed into the different classifiers to classify OSA patients and normal subjects. The Physionet Apnea-ECG and Fantasia datasets are utilized to assess the proposed OSA detection method and EDR extraction algorithms, respectively. The results show that the GentleBoost classifier has achieved the accuracy of 93.26% and 100% in per-segment and per-recording classifications, respectively. The proposed automatic OSA detection system outperforms other existing state-of-the-art methods in per-segment classification.