A method to detect sleep apnea based on deep neural network and hidden Markov model using single-lead ECG signal

Li, Kunyang; Pan, Weifeng; Li, Yifan; Jiang, Qing; Liu, Guanzheng

doi:10.1016/j.neucom.2018.03.011

Cited by 191 publications

(122 citation statements)

References 31 publications

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“…The classification accuracies and cross-validation errors evaluated by the leave-one-out method for a sample two-hour long EEG record are presented in Table 4. Input features include relative power in 4 frequency bands (1)(2)(3)(4)(4)(5)(6)(7)(8)(8)(9)(10)(11)(12)(12)(13)(14)(15)(16)(17)(18)(19)(20). For the given set of features, the neural network model provide classification results with higher accuracy and lower cross-validation error than did the k-nearest neighbour and decision tree methods.…”

Section: Methods Parameters Accuracy (%) Cross-validmentioning

confidence: 99%

“…Sleep features are very specific and there exist many studies proposing machine learning [9,10] for the automatic detection of sleep stages [11][12][13][14]. A specific interest is devoted to the use of hidden Markov models for automatic sleep staging [15][16][17], to the relation between the adjacent sleep segments, and to deep convolutional neural network [18,19] as well. Figure 2 presents the rapidly growing interest in the PSG data analysis as illustrated by the number of papers registered in the Web of Science (WoS) database.…”

Section: Introductionmentioning

confidence: 99%

“…(1)(2)(3)(4)(4)(5)(6)(7)(8)(8)(9)(10)(11)(12)(12)(13)(14)(15)(16)(16)(17)(18)(19)(20)), 2. additional features evaluated from the 4 frequency bands (0.05-0.2, 0.2-0.3, 0.3-0.4, 0.4-0.8 Hz) of the flow channel and 3 frequency bands (4-15, 15-30, 30-40 Hz) of the electro-oculogram channel.…”

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confidence: 99%

“…Fraiwan et al [8] It is projected that the neural network model will be further used for more extensive datasets and adaptation of the results to the experience of the neurologist to reduce the time necessary for manual classification. A special attention will be paid to deep convolutional neural networks [17,18] that allow row data processing without specification of separate features.…”

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Multi-Class Sleep Stage Analysis and Adaptive Pattern Recognition

et al. 2018

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Multimodal signal analysis based on sophisticated sensors, efficient communication systems and fast parallel processing methods has a rapidly increasing range of multidisciplinary applications. The present paper is devoted to pattern recognition, machine learning, and the analysis of sleep stages in the detection of sleep disorders using polysomnography (PSG) data, including electroencephalography (EEG), breathing (Flow), and electro-oculogram (EOG) signals. The proposed method is based on the classification of selected features by a neural network system with sigmoidal and softmax transfer functions using Bayesian methods for the evaluation of the probabilities of the separate classes. The application is devoted to the analysis of the sleep stages of 184 individuals with different diagnoses, using EEG and further PSG signals. Data analysis points to an average increase of the length of the Wake stage by 2.7% per 10 years and a decrease of the length of the Rapid Eye Movement (REM) stages by 0.8% per 10 years. The mean classification accuracy for given sets of records and single EEG and multimodal features is 88.7% ( standard deviation, STD: 2.1) and 89.6% (STD:1.9), respectively. The proposed methods enable the use of adaptive learning processes for the detection and classification of health disorders based on prior specialist experience and man-machine interaction.

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Section: Methods Parameters Accuracy (%) Cross-validmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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Multi-Class Sleep Stage Analysis and Adaptive Pattern Recognition

et al. 2018

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“…In [14], like the ECG model discussed in [13], the other ECG based method was proposed. Unlike earlier model wherein the feature selection is important which is highly dependent on the subject matter expertise and is subjective, the proposed model is a deep neural network and HMM (Hidden Markov Model) that relies on the single-lead ECG signal.…”

Section: Related Workmentioning

confidence: 99%

Predictive Analytics for Obstructive Sleep Apnea Detection

2019

IJITEE

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many of the research studies that have focused on the issue of sleep apnea conditions among the people, emphasize the fact that the numbers are rising in significant numbers year on year. Profoundly, identifying symptoms in the patients is very important to ascertain the possible impact of sleep apnea in patients. The researchers in earlier studies have focused on the conditions of systematical physical examination over the patients who are prone to physical examination for head and neck aches, has relative impact of the osa conditions and also on some scoring-based models using the machine learning solutions. The scope of a new model could be about identification of the features in two stage model. The first stage could be about understanding the lifestyle and psychological conditions of the patient data and accordingly choose the metrics and the model of osa detection tool that can be used for analysis. If such comprehensive approach can be developed, it can be effective process for developing a sustainable solution.

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Automatic diagnosis of sleep apnea from biomedical signals using artificial intelligence techniques: Methods, challenges, and future works

Moridian

Shoeibi

Khodatars

et al. 2022

WIREs Data Min & Knowl

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Apnea is a sleep disorder that stops or reduces airflow for a short time during sleep. Sleep apnea may last for a few seconds and happen for many while sleeping. This reduction in breathing is associated with loud snoring, which may awaken the person with a feeling of suffocation. So far, a variety of methods have been introduced by researchers to diagnose sleep apnea, among which the polysomnography (PSG) method is known to be the best. Analysis of PSG signals is very complicated. Many studies have been conducted on the automatic diagnosis of sleep apnea from biological signals using artificial intelligence (AI), including machine learning (ML) and deep learning (DL) methods. This research reviews and investigates the studies on the diagnosis of sleep apnea using AI methods. First, computer aided diagnosis system (CADS) for sleep apnea using ML and DL techniques along with its parts including dataset, preprocessing, and ML and DL methods are introduced. This research also summarizes the important specifications of the studies on the diagnosis of sleep apnea using ML and DL methods in a table. In the following, a comprehensive discussion is made on the studies carried out in this field. The challenges in the diagnosis of sleep apnea using AI methods are of paramount importance for researchers. Accordingly, these obstacles are elaborately addressed. In another section, the most important future works for studies on sleep apnea detection from PSG signals and AI techniques are presented. Ultimately, the essential findings of this study are provided in the conclusion section. This article is categorized under: Technologies > Artificial Intelligence Application Areas > Data Mining Software Tools Algorithmic Development > Biological Data Mining

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A method to detect sleep apnea based on deep neural network and hidden Markov model using single-lead ECG signal

Cited by 191 publications

References 31 publications

Multi-Class Sleep Stage Analysis and Adaptive Pattern Recognition

Multi-Class Sleep Stage Analysis and Adaptive Pattern Recognition

Predictive Analytics for Obstructive Sleep Apnea Detection

Automatic diagnosis of sleep apnea from biomedical signals using artificial intelligence techniques: Methods, challenges, and future works

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