Single Channel ECG for Obstructive Sleep Apnea Severity Detection using a Deep Learning Approach

Obstructive sleep apnea (OSA) is a common sleep disorder caused by abnormal breathing. The severity of OSA can lead to many symptoms such as sudden cardiac death (SCD). Polysomnography (PSG) is a gold standard for OSA diagnosis. It records many signals from the patient's body for at least one whole night and calculates the Apnea-Hypopnea Index (AHI) which is the number of apnea or hypopnea incidences per hour. This value is then used to classify patients into OSA severity levels. However, it has many disadvantages and limitations. Consequently, we proposed a novel methodology of OSA severity classification using a Deep Learning approach. We focused on the classification between normal subjects (AHI $$ 30). The 15-second raw ECG records with apnea or hypopnea events were used with a series of deep learning models. The main advantages of our proposed method include easier data acquisition, instantaneous OSA severity detection, and effective feature extraction without domain knowledge from expertise. To evaluate our proposed method, 545 subjects of which 364 were normal and 181 were severe OSA patients obtained from the MrOS sleep study (Visit 1) database were used with the k-fold cross-validation technique. The accuracy of 79.45\% for OSA severity classification with sensitivity, specificity, and F-score was achieved. This is significantly higher than the results from the SVM classifier with RR Intervals and ECG derived respiration (EDR) signal feature extraction. The promising result shows that this proposed method is a good start for the detection of OSA severity from a single channel ECG which can be obtained from wearable devices at home and can also be applied to near real-time alerting systems such as before SCD occurs

Obstructive Sleep Apnea Detection using Frequency Analysis of Electrocardiographic RR Interval and Machine Learning Algorithms

Background: Obstructive Sleep Apnea (OSA) is a respiratory disorder due to obstructive upper airway (mainly in the oropharynx) periodically during sleep. The common examination used to diagnose sleep disorders is Polysomnography (PSG). Diagnose with PSG feels uncomfortable for the patient because the patient’s body is fitted with many sensors. Objective: This study aims to propose an OSA detection using the Fast Fourier Transform (FFT) statistics of electrocardiographic RR Interval (R interval from one peak to the peak of the pulse of the next pulse R) and machine learning algorithms.Material and Methods: In this case-control study, data were taken from the Massachusetts Institute of Technology at Beth Israel Hospital (MIT-BIH) based on the Apnea ECG database (RR Interval). The machine learning algorithms were Linear Discriminant Analysis (LDA), Artificial Neural Network (ANN), K-Nearest Neighbors (K-NN), and Support Vector Machine (SVM). Results: The OSA detection technique was designed and tested, and five features of the FFT were examined, namely mean (f1), Shannon entropy (f2), standard deviation (f3), median (f4), and geometric mean (f5). The OSA detection found the highest performance using ANN. Among the ANN types tested, the ANN with gradient descent backpropagation resulted in the best performance with accuracy, sensitivity, and specificity of 84.64%, 94.21%, and 64.03%, respectively. The lowest performance was found when LDA was applied.  Conclusion: ANN with gradient-descent backpropagation performed higher than LDA, SVM, and KNN for OSA detection

On the Generalization of Sleep Apnea Detection Methods Based on Heart Rate Variability and Machine Learning

[EN] Obstructive sleep apnea (OSA) is a respiratory disorder highly correlated with severe cardiovascular diseases that has unleashed the interest of hundreds of experts aiming to overcome the elevated requirements of polysomnography, the gold standard for its detection. In this regard, a variety of algorithms based on heart rate variability (HRV) features and machine learning (ML) classifiers have been recently proposed for epoch-wise OSA detection from the surface electrocardiogram signal. Many researchers have employed freely available databases to assess their methods in a reproducible way, but most were purely tested with cross-validation approaches and even some using solely a single database for training and testing procedures. Hence, although promising values of diagnostic accuracy have been reported by some of these methods, they are suspected to be overestimated and the present work aims to analyze the actual generalization ability of several epoch-wise OSA detectors obtained through a common ML pipeline and typical HRV features. Precisely, the performance of the generated OSA detectors has been compared on two validation approaches, i.e., the widely used epoch-wise, k-fold cross-validation and the highly recommended external validation, both considering different combinations of well-known public databases. Regardless of the used ML classifiers and the selected HRV-based features, the external validation results have been 20 to 40% lower than those obtained with cross-validation in terms of accuracy, sensitivity, and specificity. Consequently, these results suggest that ML-based OSA detectors trained with public databases are still not sufficiently general to be employed in clinical practice, as well as that larger, more representative public datasets and the use of external validation are mandatory to improve the generalization ability and to obtain reliable assessment of the true predictive power of these algorithms, respectively.This research has received financial support from public grants PID2021-00X128525-IV0 and PID2021-123804OB-I00 of the Spanish Government 10.13039/501100011033 jointly with the European Regional Development Fund, SBPLY/17/180501/000411 and SBPLY/21/180501/000186 from Junta de Comunidades de Castilla-La Mancha, and AICO/2021/286 from Generalitat Valenciana. Moreover, Daniele Padovano holds a predoctoral scholarship 2022-PRED-20642, which is cofinanced by the operating program of European Social Fund (ESF) 2014-2020 of Castilla-La Mancha.Padovano, D.; Martínez-Rodrigo, A.; Pastor, JM.; Rieta, JJ.; Alcaraz, R. (2022). On the Generalization of Sleep Apnea Detection Methods Based on Heart Rate Variability and Machine Learning. IEEE Access. 10:92710-92725. https://doi.org/10.1109/ACCESS.2022.320191192710927251

Electrocardiogram pattern recognition and analysis based on artificial neural networks and support vector machines: a review.

Computer systems for Electrocardiogram (ECG) analysis support the clinician in tedious tasks (e.g., Holter ECG monitored in Intensive Care Units) or in prompt detection of dangerous events (e.g., ventricular fibrillation). Together with clinical applications (arrhythmia detection and heart rate variability analysis), ECG is currently being investigated in biometrics (human identification), an emerging area receiving increasing attention. Methodologies for clinical applications can have both differences and similarities with respect to biometrics. This paper reviews methods of ECG processing from a pattern recognition perspective. In particular, we focus on features commonly used for heartbeat classification. Considering the vast literature in the field and the limited space of this review, we dedicated a detailed discussion only to a few classifiers (Artificial Neural Networks and Support Vector Machines) because of their popularity; however, other techniques such as Hidden Markov Models and Kalman Filtering will be also mentioned

Heart Diseases Diagnosis Using Artificial Neural Networks

Information technology has virtually altered every aspect of human life in the present era. The application of informatics in the health sector is rapidly gaining prominence and the benefits of this innovative paradigm are being realized across the globe. This evolution produced large number of patients’ data that can be employed by computer technologies and machine learning techniques, and turned into useful information and knowledge. This data can be used to develop expert systems to help in diagnosing some life-threating diseases such as heart diseases, with less cost, processing time and improved diagnosis accuracy. Even though, modern medicine is generating huge amount of data every day, little has been done to use this available data to solve challenges faced in the successful diagnosis of heart diseases. Highlighting the need for more research into the usage of robust data mining techniques to help health care professionals in the diagnosis of heart diseases and other debilitating disease conditions. Based on the foregoing, this thesis aims to develop a health informatics system for the classification of heart diseases using data mining techniques focusing on Radial Basis functions and emerging Neural Networks approach. The presented research involves three development stages; firstly, the development of a preliminary classification system for Coronary Artery Disease (CAD) using Radial Basis Function (RBF) neural networks. The research then deploys the deep learning approach to detect three different types of heart diseases i.e. Sleep Apnea, Arrhythmias and CAD by designing two novel classification systems; the first adopt a novel deep neural network method (with Rectified Linear unit activation) design as the second approach in this thesis and the other implements a novel multilayer kernel machine to mimic the behaviour of deep learning as the third approach. Additionally, this thesis uses a dataset obtained from patients, and employs normalization and feature extraction means to explore it in a unique way that facilitates its usage for training and validating different classification methods. This unique dataset is useful to researchers and practitioners working in heart disease treatment and diagnosis. The findings from the study reveal that the proposed models have high classification performance that is comparable, or perhaps exceed in some cases, the existing automated and manual methods of heart disease diagnosis. Besides, the proposed deep-learning models provide better performance when applied on large data sets (e.g., in the case of Sleep Apnea), with reasonable performance with smaller data sets. The proposed system for clinical diagnoses of heart diseases, contributes to the accurate detection of such disease, and could serve as an important tool in the area of clinic support system. The outcome of this study in form of implementation tool can be used by cardiologists to help them make more consistent diagnosis of heart diseases

An effect of physical exercise-induced fatigue on the vital sign parameters: a preliminary study

Vital sign monitoring is an important body measurement to identify health condition and diagnose any disease and illness. In sports, physical exercise will contribute to the changes of the physiological systems, specifically for the vital signs. Therefore, the objective of this study was to determine the effect of physical fatigue exercise on the vital sign parameters. This is significant for the fitness identification and prediction of each individual when performing an exercise. Five male subjects with no history of injuries and random BMI were selected from students of biomedical engineering, Universiti Teknologi Malaysia. Based on the relationship between physical movement and physiology, the parameters considered were heart rate, blood pressure, and body temperature. Subjects were required to run on the treadmill at an initial speed of 4 km/h with an increase of 1 km/h at every 2 minutes interval. The effect of exercise was marked according to the fatigue protocol where the subject was induced to the maximum condition of performance. All parameters were measured twice, for pre and post exercise-induced protocol. The analysis of relationship of each parameter between pre and post fatigue was p<0.05. The results revealed that the heart rate and gap between blood pressure’s systolic and diastolic were greater for all categories except underweight, where the systolic blood pressure dropped to below 100mmHg at the end of exercise. Also, the body temperature was slightly declined to balance the thermoregulatory system with sweating. Hence, the vigorous physical movement could contribute to the active physiological system based on body metabolism. Heart rate and blood pressure presented significant effects from the fatiguing exercise whereas the body temperature did not indicate any distinguishable impact. The results presented might act as the basis of reference for physical exercise by monitoring the vital sign parameters