ECG-derived respiration estimation from single-lead ECG using gaussian process and phase space reconstruction methods

A Deep Learning Framework for Automatic Sleep Apnea Classification Based on Empirical Mode Decomposition Derived from Single-Lead Electrocardiogram

BACKGROUND

Although polysomnography (PSG) is a gold standard tool for diagnosing sleep apnea (SA), it can reduce the patient's sleep quality by the placement of several disturbing sensors and can only be interpreted by a highly trained sleep technician or scientist. In recent years, electrocardiogram (ECG)-derived respiration (EDR) and heart rate variability (HRV) have been used to automatically diagnose SA and reduce the drawbacks of PSG. Up to now, most of the proposed approaches focus on machine-learning (ML) algorithms and feature engineering, which require prior expert knowledge and experience. The present study proposes an SA detection algorithm to differentiate a normal and apnea event using a deep-learning (DL) framework based on 1D and 2D deep CNN with empirical mode decomposition (EMD) of a preprocessed ECG signal. The EMD is ideally suited to extract essential components which are characteristic of the underlying biological or physiological processes. In addition, the simple and compact architecture of 1D deep CNN, which only performs 1D convolutions, and pretrained 2D deep CNNs, are suitable for real-time and low-cost hardware implementation.

METHOD

This study was validated using 7 h to nearly 10 h overnight ECG recordings from 33 subjects with an average apnea-hypopnea index (AHI) of 30.23/h originated from PhysioNet Apnea-ECG database (PAED). In preprocessing, the raw ECG signal was normalized and filtered using the FIR band pass filter. The preprocessed ECG signal was then decomposed using the empirical mode decomposition (EMD) technique to generate several features. Several important generated features were selected using neighborhood component analysis (NCA). Finally, deep learning algorithm based on 1D and 2D deep CNN were used to perform the classification of normal and apnea event. The synthetic minority oversampling technique (SMOTE) was also applied to evaluate the influence of the imbalanced data problem.

RESULTS

The segment-level classification performance had 93.8% accuracy with 94.9% sensitivity and 92.7% specificity based on 5-fold cross-validation (5fold-CV), meanwhile, the subject-level classification performance had 83.5% accuracy with 75.9% sensitivity and 88.7% specificity based on leave-one-subject-out cross-validation (LOSO-CV).

CONCLUSION

A novel and robust SA detection algorithm based on the ECG decomposed signal using EMD and deep CNN was successfully developed in this study.

Environmental Benefits of Sleep Apnoea Detection in the Home Environment

Sleep Apnoea (SA) is a common chronic illness that affects nearly 1 billion people around the world, and the number of patients is rising. SA causes a wide range of psychological and physiological ailments that have detrimental effects on a patient’s wellbeing. The high prevalence and negative health effects make SA a public health problem. Whilst the current gold standard diagnostic procedure, polysomnography (PSG), is reliable, it is resource-expensive and can have a negative impact on sleep quality, as well as the environment. With this study, we focus on the environmental impact that arises from resource utilisation during SA detection, and we propose remote monitoring (RM) as a potential solution that can improve the resource efficiency and reduce travel. By reusing infrastructure technology, such as mobile communication, cloud computing, and artificial intelligence (AI), RM establishes SA detection and diagnosis support services in the home environment. However, there are considerable barriers to a widespread adoption of this technology. To gain a better understanding of the available technology and its associated strength, as well as weaknesses, we reviewed scientific papers that used various strategies for RM-based SA detection. Our review focused on 113 studies that were conducted between 2018 and 2022 and that were listed in Google Scholar. We found that just over 50% of the proposed RM systems incorporated real time signal processing and around 20% of the studies did not report on this important aspect. From an environmental perspective, this is a significant shortcoming, because 30% of the studies were based on measurement devices that must travel whenever the internal buffer is full. The environmental impact of that travel might constitute an additional need for changing from offline to online SA detection in the home environment.

DETECTION OF VENTRICULAR FIBRILLATION USING WAVELET TRANSFORM AND PHASE SPACE RECONSTRUCTION FROM ECG SIGNALS

This study proposes the detection of ventricular fibrillation (VF) by wavelet transforms (WTs) and phase space reconstruction (PSR) from electrocardiogram (ECG) signals. A neural network with weighted fuzzy memberships (NEWFM) is used to detect VF as a classifier. In the first step, the WT was used to remove noise in ECG signals. In the second step, coordinates were mapped from the wavelet coefficients by the PSR. In the final step, NEWFM used the mapped coordinates-based features as inputs. The NEWFM has the bounded sum of weighted fuzzy memberships (BSWFM) that can easily appear the distinctness between the normal sinus rhythm (NSR) and VF in the graphical characteristics. The BSWFM can easily be set up in a portable automatic external defibrillator (AED) to detect VF in an emergency.

A Survey on Recent Advances in Machine Learning Based Sleep Apnea Detection Systems

Sleep apnea is a sleep disorder that affects a large population. This disorder can cause or augment the exposure to cardiovascular dysfunction, stroke, diabetes, and poor productivity. The polysomnography (PSG) test, which is the gold standard for sleep apnea detection, is expensive, inconvenient, and unavailable to the population at large. This calls for more friendly and accessible solutions for diagnosing sleep apnea. In this paper, we examine how sleep apnea is detected clinically, and how a combination of advances in embedded systems and machine learning can help make its diagnosis easier, more affordable, and accessible. We present the relevance of machine learning in sleep apnea detection, and a study of the recent advances in the aforementioned area. The review covers research based on machine learning, deep learning, and sensor fusion, and focuses on the following facets of sleep apnea detection: (i) type of sensors used for data collection, (ii) feature engineering approaches applied on the data (iii) classifiers used for sleep apnea detection/classification. We also analyze the challenges in the design of sleep apnea detection systems, based on the literature survey.

Extraction and Analysis of Respiratory Motion Using a Comprehensive Wearable Health Monitoring System

Respiratory activity is an important vital sign of life that can indicate health status. Diseases such as bronchitis, emphysema, pneumonia and coronavirus cause respiratory disorders that affect the respiratory systems. Typically, the diagnosis of these diseases is facilitated by pulmonary auscultation using a stethoscope. We present a new attempt to develop a lightweight, comprehensive wearable sensor system to monitor respiration using a multi-sensor approach. We employed new wearable sensor technology using a novel integration of acoustics and biopotentials to monitor various vital signs on two volunteers. In this study, a new method to monitor lung function, such as respiration rate and tidal volume, is presented using the multi-sensor approach. Using the new sensor, we obtained lung sound, electrocardiogram (ECG), and electromyogram (EMG) measurements at the external intercostal muscles (EIM) and at the diaphragm during breathing cycles with 500 mL, 625 mL, 750 mL, 875 mL, and 1000 mL tidal volume. The tidal volumes were controlled with a spirometer. The duration of each breathing cycle was 8 s and was timed using a metronome. For each of the different tidal volumes, the EMG data was plotted against time and the area under the curve (AUC) was calculated. The AUC calculated from EMG data obtained at the diaphragm and EIM represent the expansion of the diaphragm and EIM respectively. AUC obtained from EMG data collected at the diaphragm had a lower variance between samples per tidal volume compared to those monitored at the EIM. Using cubic spline interpolation, we built a model for computing tidal volume from EMG data at the diaphragm. Our findings show that the new sensor can be used to measure respiration rate and variations thereof and holds potential to estimate tidal lung volume from EMG measurements obtained from the diaphragm.

Estimating Heart Rate and Respiratory Rate from a Single Lead Electrocardiogram Using Ensemble Empirical Mode Decomposition and Spectral Data Fusion

Cardiopulmonary monitoring is important and useful for diagnosing and managing multiple conditions, such as stress and sleep disorders. Wearable ambulatory systems can provide continuous, comfortable, and inexpensive means for monitoring; it always has been a research subject in recent years. Being simple and cost-effective, electrocardiogram-based commercial products can be found in the market that provides cardiac diagnostic information for assessment, including heart rate measurement and atrial fibrillation identification. Based on a data-driven and self-adaptive approach, this study aims to estimate heart rate and respiratory rate simultaneously from one lead electrocardiogram signal. In contrast to ensemble empirical mode decomposition with principle component analysis, performed in the time domain, our method uses spectral data fusion, together with intrinsic mode functions using ensemble empirical mode decomposition obtains a more accurate heart rate and respiratory rate. Equipped with a rule-based selection of defined frequency levels for respiratory rate (RR) estimation, the proposed method obtains (0.92, 1.32) beat per minute for the heart rate and (2.20, 2.92) breath per minute for the respiratory rate as their mean absolute error and root mean square error, respectively outperforming other existing methods.

Detection of Epileptic Seizures Using Wavelet Transform, Peak Extraction and PSR from EEG Signals

In this study, techniques were proposed for the detection of epileptic seizures from electroencephalogram (EEG) signals using the wavelet transform (WT), peak extraction and phase–space reconstruction (PSR) based Euclidean distances. In the first step, the wavelet coefficients were extracted after eliminating the noise from the EEG signals using a WT, which is a widely used signal processing technique. In the second step, the peaks were extracted from the wavelet coefficients. In the third step, the continuous peaks that were extracted were mapped to 3D coordinates using PSR. In the fourth step, the Euclidean distances between the mapped 3D coordinates and the origin were obtained. The features of the Euclidean distances obtained were extracted using statistical techniques. The final features extracted were used as inputs to the neural network with weighted fuzzy membership (NEWFM). NEWFM contains the bounded sum of weighted fuzzy memberships (BSWFMs) that can reveal the differences in the graphic characteristics between normal EEG signals and epileptic-seizure EEG signals. The BSWFMs can easily be embedded in a portable device to detect epileptic seizures from EEG signals in real life.

Cardiopulmonary Activity Monitoring Using Millimeter Wave Radars

Current cardiopulmonary activity monitoring is based on contact devices which cannot be used in extreme cases such as premature infants, burnt victims or rescue operations. In order to overcome these limitations, the use of radar technologies emerges as an alternative. This paper aims to enhance the comprehension that non-contact technologies, in particular radar techniques, offer as a monitoring tool. For this purpose, a modified low cost commercial 122 GHz frequency-modulated continuous-wave (FMCW) radar is used to better fit the current application domain. The radar signals obtained are processed using a classic linear filtering algorithm aiming to separate the breathing from the heartbeat component while preserving signals integrity. In a standoff configuration and with different subject orientations, results show that the signal obtained with the radar can be used to extract not only the respiratory and heartbeat rates, but also the heart rate variability (HRV) sequence. Moreover, results evidence the coupling between breathing and heartbeat, also showing that the HRV sequence obtained can identify the respiratory sinus arrhythmia (RSA) effect. Finally, the radar is tested in a simultaneous multi-target scenario, demonstrating its monitoring capabilities in more complex situations. Nevertheless, there are some challenges left to use the system in a real-life monitoring environments, such as the removal of random body movements.

Analysis of the fetal cardio-electrohysterographic coupling at the third trimester of gestation in healthy women by Bivariate Phase-Rectified Signal Averaging

INTRODUCTION

The fetal cardio-electrohysterographic coupling (FCEC) is defined as the influence of the uterine electrical activity on fetal heart rate. FCEC has been mainly evaluated by visual analysis of cardiotocographic data during labor; however, this physiological phenomenon is poorly explored during the antenatal period. Here we propose an approach known as Bivariate Phase-Rectified Signal Averaging analysis (BPRSA) to assess such FCEC in the late third trimester of low-risk pregnancies. We hypothesized that BPRSA is a more reliable measure of FCEC than visual analysis and conventional measures such as cross-correlation, coherence, and cross-sample entropy. Additionally, by using BPRSA it is possible to detect FCEC even from the third trimester of pregnancy.

MATERIAL AND METHODS

Healthy pregnant women in the last third trimester of pregnancy (36.6 ± 1.8 gestational weeks) without any clinical manifestation of labor were enrolled in the Maternal and Childhood Research Center (CIMIGen), Mexico City (n = 37). Ten minutes of maternal electrohysterogram (EHG) and fetal heart rate (FHR) data were collected by a transabdominal non-invasive device. The FCEC was quantified by the coefficient of coherence, the maximum normalized cross-correlation, and the cross-sample entropy obtained either from the EHG and FHR raw signals or from the corresponding BPRSA graphs.

RESULTS

We found that by using BPRSA, the FCEC was detected in 92% cases (34/37) compared to 48% cases (18/37) using the coefficient of coherence between the EHG and FHR raw signals. Also, BPRSA indicated FCEC in 82% cases (30/37) compared to 30% cases (11/37) using the maximum normalized cross-correlation. By comparing the analyses, the BPRSA evidenced higher FCEC in comparison to the coupling estimated from the raw EHG and FHR signals.

CONCLUSIONS

Our results support the consideration that in the third trimester of pregnancy, the fetal heart rate is also influenced by uterine activity despite the emerging manifestation of this activity before labor. To quantify FCEC, the BPRSA can be applied to FHR and EHG transabdominal signals acquired in the third trimester of pregnancy.

Detection of Abnormal Respiration from Multiple-Input Respiratory Signals

In this paper, we propose a novel approach for the detection of abnormal signals from multiple respiration signals. An ultrawide-band (UWB) radar was used to acquire respiration signals that represent a distance from the chest to the radar sensor, i.e., shape variation of the chest due to breathing (inhaling or exhaling) activity provides quantitative information (distance values) about respiratory status. Distribution, shape, and variation of values across time provide information to determine respiratory status, one of the most important indicators of human health. In this paper, respiratory status was categorized into two classes, normal and abnormal. Abnormal respiration (apnea in this paper) was emulated by interrupting breathing activity because it is difficult to acquire real apnea from patients in hospital wards. This paper considered two cases, single and multiple respiration. In the first case, a single normal- or abnormal-respiration signal was used as input, and output was the classified status of respiration. In the second case, multiple respiration signals were simultaneously used as inputs, and we focused on determining the existence of abnormal signals in multiple respiration signals. In the case of multiple inputs, filters with varying cut-off frequency were applied to input signals followed by the analysis of output signals in response to the filters. To substantiate the proposed method, experiment results are provided. In this paper, classification results showed 93% of the successful rate in the case of multiple inputs, and results are promising for applications to monitoring systems of human respiration.

A Comparative Study of ECG-derived Respiration in Ambulatory Monitoring using the Single-lead ECG

Cardiorespiratory monitoring is crucial for the diagnosis and management of multiple conditions such as stress and sleep disorders. Therefore, the development of ambulatory systems providing continuous, comfortable, and inexpensive means for monitoring represents an important research topic. Several techniques have been proposed in the literature to derive respiratory information from the ECG signal. Ten methods to compute single-lead ECG-derived respiration (EDR) were compared under multiple conditions, including different recording systems, baseline wander, normal and abnormal breathing patterns, changes in breathing rate, noise, and artifacts. Respiratory rates, wave morphology, and cardiorespiratory information were derived from the ECG and compared to those extracted from a reference respiratory signal. Three datasets were considered for analysis, involving a total 59 482 one-min, single-lead ECG segments recorded from 156 subjects. The results indicate that the methods based on QRS slopes outperform the other methods. This result is particularly interesting since simplicity is crucial for the development of ECG-based ambulatory systems.