A Multi-Scale Parallel Convolutional Neural Network for Automatic Sleep Apnea Detection Using Single-Channel EEG Signals

2018 ◽  
Author(s):  
Dihong Jiang ◽  
Yu Ma ◽  
Yuanyuan Wang
Keyword(s):  
Neural Network ◽  
Sleep Apnea ◽  
Convolutional Neural Network ◽  
Single Channel ◽  
Eeg Signals ◽  
Multi Scale ◽  
Apnea Detection

scholarly journals A Multi-scale Residual Convolutional Neural Network for Sleep Staging Based on Single Channel Electroencephalography Signal

2021 ◽  
Author(s):  
QINGHUA ZHONG ◽  
Haibo Lei ◽  
Qianru Chen ◽  
Guofu Zhou
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sleep Disorder ◽  
Eye Movement ◽  
Cross Validation ◽  
Single Channel ◽  
Rapid Eye Movement Sleep ◽  
Sleep Staging ◽  
Eeg Signals ◽  
Multi Scale

Abstract Sleep disorder is a serious public health problem. Non hospital sleep monitoring system for monitoring sleep quality can effectively support the screening of sleep disorder related diseases. A new algorithm of multi-scale residual convolutional neural network (MS-RESCNN) was proposed to discover the feature of electroencephalography (EEG) signals detected with wearable system and staging the sleep stage. EEG signals were analyzed by this algorithm every 30 seconds, and then sleep staging results of wake-up (W), rapid eye movement sleep (REM) and non-rapid eye movement sleep (NREM) were outputed. NREM can also be subdivided into N1, N2 and N3 stages. 5-fold cross validation and independent subject cross validation were performed on the dataset with Kappa cofficients 0.7360 and 0.7001, respectively. The accuracy rates of those methods were 92.06% and 91.13%, respectively. Compared with the other methods, our proposed method can obtain the information of sleep stages from single channel EEG signals without special feature extraction. It has a good performance and can provide support for clinical application based on automatic sleep staging.

scholarly journals Sleep apnea detection from a single-lead ECG signal with automatic feature-extraction through a modified LeNet-5 convolutional neural network

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7731 ◽  
Author(s):  
Tao Wang ◽  
Changhua Lu ◽  
Guohao Shen ◽  
Feng Hong
Keyword(s):  
Neural Network ◽  
Sleep Apnea ◽  
Convolutional Neural Network ◽  
Single Channel ◽  
Feature Representation ◽  
Training Data ◽  
Ecg Signal ◽  
Automatic Feature Extraction ◽  
Adjacent Segments ◽  
The Impact

Sleep apnea (SA) is the most common respiratory sleep disorder, leading to some serious neurological and cardiovascular diseases if left untreated. The diagnosis of SA is traditionally made using Polysomnography (PSG). However, this method requires many electrodes and wires, as well as an expert to monitor the test. Several researchers have proposed instead using a single channel signal for SA diagnosis. Among these options, the ECG signal is one of the most physiologically relevant signals of SA occurrence, and one that can be easily recorded using a wearable device. However, existing ECG signal-based methods mainly use features (i.e. frequency domain, time domain, and other nonlinear features) acquired from ECG and its derived signals in order to construct the model. This requires researchers to have rich experience in ECG, which is not common. A convolutional neural network (CNN) is a kind of deep neural network that can automatically learn effective feature representation from training data and has been successfully applied in many fields. Meanwhile, most studies have not considered the impact of adjacent segments on SA detection. Therefore, in this study, we propose a modified LeNet-5 convolutional neural network with adjacent segments for SA detection. Our experimental results show that our proposed method is useful for SA detection, and achieves better or comparable results when compared with traditional machine learning methods.

scholarly journals Detection of sleep apnea from single-channel electroencephalogram (EEG) using an explainable convolutional neural network

2021 ◽  
Author(s):  
Lachlan D Barnes ◽  
Kevin Lee ◽  
Andreas W Kempa-Liehr ◽  
Luke E Hallum
Keyword(s):  
Neural Network ◽  
Sleep Apnea ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Deleterious Effect ◽  
Network Performance ◽  
Single Channel ◽  
Center Frequency ◽  
Beta Band ◽  
Eeg Recordings

AbstractSleep apnea (SA) is a common disorder involving the cessation of breathing during sleep. It can cause daytime hypersomnia, accidents, and, if allowed to progress, serious, chronic conditions. Continuous positive airway pressure is an effective SA treatment. However, long waitlists impede timely diagnosis; overnight sleep studies involve trained technicians scoring a polysomnograph, which comprises multiple physiological signals including multi-channel electroencephalography (EEG). Therefore, it is important to develop simplified and automated approaches to detect SA. We have developed an explainable convolutional neural network (CNN) to detect SA from single-channel EEG recordings which generalizes across subjects. The network architecture consisted of three convolutional layers. We tuned hyperparameters using the Hyperband algorithm, optimized parameters using Adam, and quantified network performance with subjectwise 10-fold cross-validation. Our CNN performed with an accuracy of 76.7% and a Matthews correlation coefficient (MCC) of 0.54. This performance was reliably above the conservative baselines of 50% (accuracy) and 0.0 (MCC). To explain the mechanisms of our trained network, we used critical-band masking (CBM): after training, we added bandlimited noise to test recordings; we parametrically varied the noise band center frequency and noise intensity, quantifying the deleterious effect on performance. We reconciled the effects of CBM with lesioning, wherein we zeroed the trained network’s 1st-layer filter kernels in turn, quantifying the deleterious effect on performance. These analyses indicated that the network learned frequency-band information consistent with known SA biomarkers, specifically, delta and beta band activity. Our results indicate single-channel EEG may have clinical potential for SA diagnosis.

A novel convolutional neural network method for subject-independent driver drowsiness detection based on single-channel data and EEG alpha spindles

2021 ◽  
pp. 095441192110178
Author(s):  
Sara Houshmand ◽  
Reza Kazemi ◽  
Hamed Salmanzadeh
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Driving Simulator ◽  
Single Channel ◽  
Reference Method ◽  
Eeg Signals ◽  
Drowsiness Detection ◽  
Driver Drowsiness ◽  
Driving Scenario ◽  
Eeg Alpha

A significant number of fatal accidents are caused by drowsy drivers worldwide. Driver drowsiness detection based on electroencephalography (EEG) signals has high accuracy and is known as a reference method for evaluating drowsiness. Among brain waves, EEG alpha spindle activity is a silent feature of decreasing alertness levels. In this paper, based on the detection of EEG alpha spindles, a novel driver drowsiness detection method is presented. The EEG spindles were detected using Continuous Wavelet Transform (CWT) analysis and the Morlet function. To do so, the signal is divided into 30-s epochs, and the observer rating of drowsiness determines the drowsiness level in each epoch. Tests were conducted on 17 healthy males in a driving simulator with a monotonous driving scenario. The Convolutional Neural Network (CNN) is used for classifying EEG signals and automatically learns features of the early drowsy state. The subject-independent classification results for single-channel P4 show 94% accuracy.

scholarly journals Multi-Objective Hyperparameter Optimization of Convolutional Neural Network for Obstructive Sleep Apnea Detection

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 129586-129599 ◽  
Author(s):  
Sheikh Shanawaz Mostafa ◽  
Fabio Mendonca ◽  
Antonio G. Ravelo-Garcia ◽  
Gabriel Julia-Serda ◽  
Fernando Morgado-Dias
Keyword(s):  
Neural Network ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Convolutional Neural Network ◽  
Hyperparameter Optimization ◽  
Multi Objective ◽  
Obstructive Sleep ◽  
Apnea Detection
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