Abstract
Obstructive sleep apnea (OSA) is a common sleep-related respiratory disorder. Around the world, more and more people are suffering from OSA. Because of the limitation of monitor equipment, many people with OSA remain undetected. Therefore, we propose a sleep-monitoring model based on single-channel electrocardiogram using a convolutional neural network (CNN), which can be used in portable OSA monitor devices. To learn different scale features, the first convolution layer comprises three types of filters. The long short-term memory (LSTM) is used to learn the long-term dependencies such as the OSA transition rules. The softmax function is connected to the final fully connected layer to obtain the final decision. To detect a complete OSA event, the raw ECG signals are segmented by a 10 s overlapping sliding window. The proposed model is trained with the segmented raw signals and is subsequently tested to evaluate its event detection performance. According to experiment analysis, the proposed model exhibits Cohen's kappa coefficient of 0.92, a sensitivity of 96.1%, a specificity of 96.2%, and an accuracy of 96.1% with respect to the Apnea-ECG dataset. The proposed model is significantly higher than the results from the baseline method. The results prove that our approach could be a useful tool for detecting OSA on the basis of a single-lead ECG.
Highlights
Obstructive sleep apnea (OSA) is a major sleep-disordered breathing (SDB) syndrome that is an independent risk factor of coronary heart disease, hypertension, and arrhythmia [1]
Because long short-term memory (LSTM) maintains internal memory and utilizes feedback connections to learn temporal information from sequences of inputs, in this study, we propose a new method for OSA detection using the convolutional neural networks (CNNs) and LSTM. e LSTM [37] is used to learn these dependencies, such as the transition rules employed by physicians, to identify future OSA events from previous ECG epochs
The proposed model does not use the contextual information to score OSA, making it unable to distinguish the transition epochs. e other reason may be that the proposed model finds it difficult to score the artifact epochs. e ECG signals can be polluted by unwanted noise signals, including body movement
Summary
Obstructive sleep apnea (OSA) is a major sleep-disordered breathing (SDB) syndrome that is an independent risk factor of coronary heart disease, hypertension, and arrhythmia [1]. According to the manual of the American Academy of Sleep Medicine (AASM) [2], OSA in adults is scored when there is a 90% or more reduction in the baseline of the oral and nasal respiration amplitude for 10 s or more, occuring during sleep. According to the AASM [2], polysomnography (PSG) is considered to be the gold standard for OSA detection, which is based on a comprehensive evaluation of the sleep signals [10]. PSG involves overnight recording of the patient and the measurement of many signals using the sensors attached to the body, e.g., an electroencephalogram (EEG), electromyogram (EMG), electrocardiogram (ECG), and electrooculogram (EOG), to monitor the respiratory effort and other biophysiological signals [1].
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