An Automatic Sleep Stage Classification Algorithm Using Improved Model Based Essence Features

Shen, Huaming; Ran, Feng; Xu, Meihua; Guez, A.; Li, Ang; Guo, Aike

doi:10.3390/s20174677

Cited by 31 publications

(8 citation statements)

References 31 publications

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“…In this study, the model was trained end-to-end via back propagation without any requirement for feature selection work. This is one of the advantages of our method over other transitional methods [9][10][11][12][13][14] because the convolutional layers and gated recurrent units can automatically learn the features of each sleep stage. Another advantage is that our method is designed with a simple architecture that can produce better learning efficiency than the existing methods.…”

Section: Main Findingsmentioning

confidence: 99%

“…These traditional methods extract hand-engineered features through time-domain, frequencydomain, and time-frequency analysis, then concatenate these features into feature vectors and feed the feature vectors into vector-based classifiers such as support vector machine (SVM), decision tree, and random forest. For instance, in the multi-stage sleep classification, the applied handcrafted feature extraction methods are time-frequency distributions [9], graph theory [10,11], wavelet transform [12], signal modeling [13], improved model based essence features [14] and traditional machine learning techniques are support vector machine [15], ensemble learning based classifiers [16], random under sampling boosting [17], and discriminant analysis [18], etc. The hand-crafted feature extraction-based machine method cannot extract the deeply concealed characteristics from the signals using traditional machine learning methods because of its shallow architectures.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Deep Learning Scheme for Multi-Channel Sleep Stage Classification

Shao¹,

Kim²

2022

Computers, Materials &Amp; Continua

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Sleep stage classification plays a significant role in the accurate diagnosis and treatment of sleep-related diseases. This study aims to develop an efficient deep learning based scheme for correctly identifying sleep stages using multi-biological signals such as electroencephalography (EEG), electrocardiogram (ECG), electromyogram (EMG), and electrooculogram (EOG). Most of the prior studies in sleep stage classification focus on hand-crafted feature extraction methods. Traditional hand-crafted feature extraction methods choose features manually from raw data, which is tedious, and these features are limited in their ability to balance efficiency and accuracy. Moreover, most of the existing works on sleep staging are either single channel (a single-lead EEG may not contain enough information) or only EEG signal based which can not reveal more complicated physical features for reliable classification of various sleep stages. This study proposes an approach to combine Convolutional Neural Networks (CNNs) and Gated Recurrent Units (GRUs) that can discover hidden features from multi-biological signal data to recognize the different sleep stages efficiently. In the proposed scheme, the CNN is designed to extract concealed features from the multi-biological signals, and the GRU is employed to automatically learn the transition rules among different sleep stages. After that, the softmax layers are used to classify various sleep stages. The proposed method was tested on two publicly available databases: Sleep

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Section: Main Findingsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Hybrid Deep Learning Scheme for Multi-Channel Sleep Stage Classification

Shao¹,

Kim²

2022

Computers, Materials &Amp; Continua

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“…Another study [ 20 ] suggests to use an automated deep neural network using a multi-model integration approach and multiple input signal channels. A sleep stage classification method has been proposed that involves extracting features from EEG signals using an improved model-based essence feature extraction technique [ 21 ]. These features are then used to train a support vector machine (SVM) classifier to classify sleep stages.…”

Section: Related Workmentioning

confidence: 99%

“…Motivated by these unprecedented successes in different domains, ML/DL-based schemes have received considerable attention from the sleep research community. More specifically, DL-based methods, which include convolutional neural networks (CNN), recurrent neural networks (RNN), and a variant of RNNs called long short-term memory (LSTM) network, have been extensively explored to design automatic sleep scoring because of their powerful capabilities of capturing spatial and temporal features from the complex data distribution and mapping them in decision making with higher accuracy without manual intervention [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27]. In addition, the ever-growing annotated sleep databases and their availability has also stimulated sleep researchers to design and develop novel sleep scoring methods and to test under powerful DL frameworks.…”

Section: Introductionmentioning

confidence: 99%

An End-to-End Multi-Channel Convolutional Bi-LSTM Network for Automatic Sleep Stage Detection

Toma

Choi

2023

Sensors

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Sleep stage detection from polysomnography (PSG) recordings is a widely used method of monitoring sleep quality. Despite significant progress in the development of machine-learning (ML)-based and deep-learning (DL)-based automatic sleep stage detection schemes focusing on single-channel PSG data, such as single-channel electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG), developing a standard model is still an active subject of research. Often, the use of a single source of information suffers from data inefficiency and data-skewed problems. Instead, a multi-channel input-based classifier can mitigate the aforementioned challenges and achieve better performance. However, it requires extensive computational resources to train the model, and, hence, a tradeoff between performance and computational resources cannot be ignored. In this article, we aim to introduce a multi-channel, more specifically a four-channel, convolutional bidirectional long short-term memory (Bi-LSTM) network that can effectively exploit spatiotemporal features of data collected from multiple channels of the PSG recording (e.g., EEG Fpz-Cz, EEG Pz-Oz, EOG, and EMG) for automatic sleep stage detection. First, a dual-channel convolutional Bi-LSTM network module has been designed and pre-trained utilizing data from every two distinct channels of the PSG recording. Subsequently, we have leveraged the concept of transfer learning circuitously and have fused two dual-channel convolutional Bi-LSTM network modules to detect sleep stages. In the dual-channel convolutional Bi-LSTM module, a two-layer convolutional neural network has been utilized to extract spatial features from two channels of the PSG recordings. These extracted spatial features are subsequently coupled and given as input at every level of the Bi-LSTM network to extract and learn rich temporal correlated features. Both Sleep EDF-20 and Sleep EDF-78 (expanded version of Sleep EDF-20) datasets are used in this study to evaluate the result. The model that includes an EEG Fpz-Cz + EOG module and an EEG Fpz-Cz + EMG module can classify sleep stage with the highest value of accuracy (ACC), Kappa (Kp), and F1 score (e.g., 91.44%, 0.89, and 88.69%, respectively) on the Sleep EDF-20 dataset. On the other hand, the model consisting of an EEG Fpz-Cz + EMG module and an EEG Pz-Oz + EOG module shows the best performance (e.g., the value of ACC, Kp, and F1 score are 90.21%, 0.86, and 87.02%, respectively) compared to other combinations for the Sleep EDF-78 dataset. In addition, a comparative study with respect to other existing literature has been provided and discussed in order to exhibit the efficacy of our proposed model.

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“…The automatic sleep stage classification technique can facilitate the diagnosis of sleep disorders and free medical experts from labor-consuming work. Shen et al [ 9 ] have proposed an improved model-based essence features that combines locality energy and dual state space models for automatic sleep stage detection on single-channel electroencephalograph signals. The experimental results have shown high classification accuracy compared with state-of-the-art methods.…”

mentioning

confidence: 99%