scholarly journals Detection of obstructive sleep apnea through ECG signal features

2012 ◽  
Author(s):  
Laiali Almazaydeh ◽  
Khaled Elleithy ◽  
Miad Faezipour
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Ecg Signal ◽  
Signal Features ◽  
Obstructive Sleep

OBSTRUCTIVE SLEEP APNEA CLASSIFICATION WITH ARTIFICIAL NEURAL NETWORK BASED ON TWO SYNCHRONIC HRV SERIES

2015 ◽  
Vol 27 (02) ◽  
pp. 1550011 ◽  
Author(s):  
Mehmet Akşahin ◽  
Aykut Erdamar ◽  
Hikmet Fırat ◽  
Sadık Ardıç ◽  
Osman Eroğul
Keyword(s):  
Neural Network ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Absolute Error ◽  
Feed Forward Neural Network ◽  
Signal Features ◽  
Single Feature ◽  
Obstructive Sleep ◽  
Spectrum Density ◽  
Hidden Layer

In the present study, "obstructive sleep apnea (OSA) patients" and "non-OSA patients" were classified into two groups using with two synchronic heart rate variability (HRV) series obtained from electrocardiography (ECG) and photoplethysmography (PPG) signals. A linear synchronization method called cross power spectrum density (CPSD), commonly used on HRV series, was performed to obtain high-quality signal features to discriminate OSA from controls. To classify simultaneous sleep ECG and PPG signals recorded from OSA and non-OSA patients, various feed forward neural network (FFNN) architectures are used and mean relative absolute error (MRAE) is applied on FFNN results to show affectivities of developed algorithm. The FFNN architectures were trained with various numbers of neurons and hidden layers. The results show that HRV synchronization is directly related to sleep respiratory signals. The CPSD of the HRV series can confirm the clinical diagnosis; both groups determined by an expert physician can be 99% truly classified as a single hidden-layer FFNN structure with 0.0623 MRAE, in which the maximum and phase values of the CPSD curve are assigned as two features. In future work, features taken from different physiological signals can be added to define a single feature that can classify apnea without error.

scholarly journals Diagnosis of Obstructive Sleep Apnea from ECG Signals Using Machine Learning and Deep Learning Classifiers

2021 ◽  
Vol 11 (14) ◽  
pp. 6622
Author(s):  
Alaa Sheta ◽  
Hamza Turabieh ◽  
Thaer Thaher ◽  
Jingwei Too ◽  
Majdi Mafarja ◽  
...  
Keyword(s):  
Machine Learning ◽  
Obstructive Sleep Apnea ◽  
Deep Learning ◽  
Sleep Apnea ◽  
Early Stage ◽  
Notch Filter ◽  
Ecg Signal ◽  
Ecg Signals ◽  
Cad System ◽  
Obstructive Sleep

Obstructive sleep apnea (OSA) is a well-known sleep ailment. OSA mostly occurs due to the shortage of oxygen for the human body, which causes several symptoms (i.e., low concentration, daytime sleepiness, and irritability). Discovering the existence of OSA at an early stage can save lives and reduce the cost of treatment. The computer-aided diagnosis (CAD) system can quickly detect OSA by examining the electrocardiogram (ECG) signals. Over-serving ECG using a visual procedure is challenging for physicians, time-consuming, expensive, and subjective. In general, automated detection of the ECG signal’s arrhythmia is a complex task due to the complexity of the data quantity and clinical content. Moreover, ECG signals are usually affected by noise (i.e., patient movement and disturbances generated by electric devices or infrastructure), which reduces the quality of the collected data. Machine learning (ML) and Deep Learning (DL) gain a higher interest in health care systems due to its ability of achieving an excellent performance compared to traditional classifiers. We propose a CAD system to diagnose apnea events based on ECG in an automated way in this work. The proposed system follows the following steps: (1) remove noise from the ECG signal using a Notch filter. (2) extract nine features from the ECG signal (3) use thirteen ML and four types of DL models for the diagnosis of sleep apnea. The experimental results show that our proposed approach offers a good performance of DL classifiers to detect OSA. The proposed model achieves an accuracy of 86.25% in the validation stage.

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