Recognition of Patient Groups with Sleep Related Disorders using Bio-signal Processing and Deep Learning

Accurately diagnosing sleep disorders is essential for clinical assessments and treatments. Polysomnography (PSG) has long been used for detection of various sleep disorders. In this research, electrocardiography (ECG) and electromayography (EMG) have been used for recognition of breathing and movement-related sleep disorders. Bio-signal processing has been performed by extracting EMG features exploiting entropy and statistical moments, in addition to developing an iterative pulse peak detection algorithm using synchrosqueezed wavelet transform (SSWT) for reliable extraction of heart rate and breathing-related features from ECG. A deep learning framework has been designed to incorporate EMG and ECG features. The framework has been used to classify four groups: healthy subjects, patients with obstructive sleep apnea (OSA), patients with restless leg syndrome (RLS) and patients with both OSA and RLS. The proposed deep learning framework produced a mean accuracy of 72% and weighted F1 score of 0.57 across subjects for our formulated four-class problem

A review of automated sleep stage scoring based on physiological signals for the new millennia

Background and Objective: Sleep is an important part of our life. That importance is highlighted by the multitude of health problems which result from sleep disorders. Detecting these sleep disorders requires an accurate interpretation of physiological signals. Prerequisite for this interpretation is an understanding of the way in which sleep stage changes manifest themselves in the signal waveform. With that understanding it is possible to build automated sleep stage scoring systems. Apart from their practical relevance for automating sleep disorder diagnosis, these systems provide a good indication of the amount of sleep stage related information communicated by a specific physiological signal. Methods: This article provides a comprehensive review of automated sleep stage scoring systems, which were created since the year 2000. The systems were developed for Electrocardiogram (ECG), Electroencephalogram (EEG), Electrooculogram (EOG), and a combination of signals. Results: Our review shows that all of these signals contain information for sleep stage scoring. Conclusions: The result is important, because it allows us to shift our research focus away from information extraction methods to systemic improvements, such as patient comfort, redundancy, safety and cost

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

Detection of Obstructive Sleep Apnoea Using Features Extracted From Segmented Time-Series ECG Signals With a One Dimensional Convolutional Neural Network

This paper reports on ongoing research, which aims to prove that features of Obstructed Sleep Apnoea (OSA) can be automatically identified from single-lead electrocardiogram (ECG) signals using a One-Dimensional Convolutional Neural Network (1DCNN) model. The 1DCNN is also compared against other machine learning (ML) classifier models, namely Support Vector Machine (SVM) and Random Forest Classifier (RFC). The 1DCNN architecture consists of 4 major parts, a Convolutional Layer, a Flattened Dense Layer, a Max Pooling Layer and a Fully Connected Multilayer Perceptron (MLP), with 1 Hidden Layer and a SoftMax output. The model repeatedly learns how to better extract prominent features from one-dimensional data and map it to the MLP for increased prediction. Training and validation are achieved using pre-processed time-series ECG signals captured from 35 ECG recordings. Using our unique windowing strategy, the data is shaped into 5 datasets of different window sizes. A total of 15 models (5 for each group, 1DCNNs, RFCs, SVMs) were evaluated using various metrics, with each being run over numerous experiments. Results show the 1DCNN-500 model delivered the greatest degree of accuracy and rapidity in comparison to the best producing RFC and SVM classifiers. 1DCNN-500 (Sensitivity 0.9743, Specificity 0.9708, Accuracy 0.9699); RFC-500 (Sensitivity/Recall (0) 0.90 / (1) 0.94, Precision (0) 0.94 / (1) 0.90, Accuracy 0.91); SVM-500 (Sensitivity (0) 0.94 / (1) 0.50, Precision (0) 0.65 / (1) 0.90, Accuracy 0.72). The model presents a novel approach that could provide support mechanisms in clinical practice to promptly diagnose patients suffering from OSA

Performance comparison of machine learning techniques in sleep scoring based on wavelet features and neighboring component analysis

Introduction: Sleep scoring is an important step in the treatment of sleep disorders. Manual annotation of sleep stages is time-consuming and experience-relevant and, therefore, needs to be done using machine learning techniques. Methods: Sleep-EDF polysomnography was used in this study as a dataset. Support vector machines and artificial neural network performance were compared in sleep scoring using wavelet tree features and neighborhood component analysis. Results: Neighboring component analysis as a combination of linear and non-linear feature selection method had a substantial role in feature dimension reduction. Artificial neural network and support vector machine achieved 90.30 and 89.93 accuracy, respectively. Discussion and Conclusion: Similar to the state of the art performance, the introduced method in the present study achieved an acceptable performance in sleep scoring. Furthermore, its performance can be enhanced using a technique combined with other techniques in feature generation and dimension reduction. It is hoped that, in the future, intelligent techniques can be used in the process of diagnosing and treating sleep disorders. © 2018 Alizadeh Savareh et al

Automatic analysis of overnight airflow to help in the diagnosis of pediatric obstructive sleep apnea

La apnea obstructiva del sueño (AOS) pediátrica es una enfermedad respiratoria altamente prevalente e infradiagnosticada que puede afectar negativamente a las funciones fisiológicas y cognitivas de los niños, causándoles graves deficiencias neurocognitivas, cardiometabólicas y endocrinas. El método estándar para su diagnóstico es la polisomnografía nocturna, una prueba compleja, de elevado coste, altamente intrusiva y poco accesible, lo que genera largas listas de espera y retrasos en el diagnóstico. Por ello, es necesario desarrollar pruebas diagnósticas más sencillas. Una de estas alternativas es el análisis automático de señales cardiorrespiratorias. Así, esta tesis doctoral presenta un compendio de cuatro publicaciones que proponen el uso de novedosos métodos de procesado de señal (no lineal, espectral, bispectral, gráficos de recurrencia y wavelet) que permiten caracterizar exhaustivamente el comportamiento del flujo aéreo nocturno de los niños y simplificar el diagnóstico de la apnea obstructiva del sueño pediátrica.Departamento de Teoría de la Señal y Comunicaciones e Ingeniería TelemáticaDoctorado en Tecnologías de la Información y las Telecomunicacione