Bidirectional LSTM with MFCC Feature Extraction for Sleep Arousal Detection in Multi-channel Signal Data

Kim, Hyunseob; Jun, Tae Joon; Nguyen, Giang V.; Kim, Dae-Young

doi:10.1007/978-3-030-36708-4_36

Cited by 2 publications

(4 citation statements)

References 10 publications

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“…Deep learning methods possess the strong capability to learn complex features by directly applying them to raw data without extracting any hand-crafted features. Only recently have researchers begun to show a preference for deep learning methods, such as CNN [68][69][70][71], ResNet [48], the Siamese architecture network [70], RNN, and LSTM [59,72,73], over traditional machine learning methods in arousal detection.…”

Section: Microarousal Detection With Deep Learning Methodsmentioning

confidence: 99%

“…Bi-LSTM depends on the output layer not only at the current time but also at the next moment. Some recent studies used RNN and LSTM networks to analyze PSG time series [59,72,73].…”

Section: Microarousal Detection With Rnn and Long Short-term Memory (Lstm)mentioning

confidence: 99%

“…Bi-LSTM can make a sequential prediction, and a Mell-Frequency Cepstral Coefficient (MFCC) can be applied uniformly on the signal data regardless of signal type. Therefore, Kim et al [73] applied a Bi-LSTM model with MFCC feature vectors to sequentially detect arousal in a single learning model. PSG data are split to training, validation, and test data with a ratio of 7:1:2.…”

Section: Microarousal Detection With Rnn and Long Short-term Memory (Lstm)mentioning

confidence: 99%

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A Review of Methods for Sleep Arousal Detection Using Polysomnographic Signals

Qian

Qiu

et al. 2021

Brain Sciences

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Multiple types of sleep arousal account for a large proportion of the causes of sleep disorders. The detection of sleep arousals is very important for diagnosing sleep disorders and reducing the risk of further complications including heart disease and cognitive impairment. Sleep arousal scoring is manually completed by sleep experts by checking the recordings of several periods of sleep polysomnography (PSG), which is a time-consuming and tedious work. Therefore, the development of efficient, fast, and reliable automatic sleep arousal detection system from PSG may provide powerful help for clinicians. This paper reviews the automatic arousal detection methods in recent years, which are based on statistical rules and deep learning methods. For statistical detection methods, three important processes are typically involved, including preprocessing, feature extraction and classifier selection. For deep learning methods, different models are discussed by now, including convolution neural network (CNN), recurrent neural network (RNN), long-term and short-term memory neural network (LSTM), residual neural network (ResNet), and the combinations of these neural networks. The prediction results of these neural network models are close to the judgments of human experts, and these methods have shown robust generalization capabilities on different data sets. Therefore, we conclude that the deep neural network will be the main research method of automatic arousal detection in the future.

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Section: Microarousal Detection With Deep Learning Methodsmentioning

confidence: 99%

“…Bi-LSTM depends on the output layer not only at the current time but also at the next moment. Some recent studies used RNN and LSTM networks to analyze PSG time series [59,72,73].…”

Section: Microarousal Detection With Rnn and Long Short-term Memory (Lstm)mentioning

confidence: 99%

Section: Microarousal Detection With Rnn and Long Short-term Memory (Lstm)mentioning

confidence: 99%

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A Review of Methods for Sleep Arousal Detection Using Polysomnographic Signals

Qian

Qiu

et al. 2021

Brain Sciences

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“…Numerous prior investigations have concentrated on either sleep arousal detection or sleep stage scoring. Deep learning models for sleep arousal detection have commonly utilized PSG data as input (8,(18)(19)(20). Notable examples include the work by Liu et al (10) and Howe-Patterson et al (19), achieving an AUROC of 0.95 and AUPRC of 0.57, respectively.…”

Section: Correlation Analyses Between Intermediate Outputs and Cardia...mentioning

confidence: 99%

A Multi-task Deep Learning Algorithm for Sleep Stage Scoring and Sleep Arousal Detection

Huo,

Li,

Roveda

et al. 2024

Preprint

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Background: Sleep is crucial for overall health and well-being. Analyzing sleep stages and sleep fragmentation can enhance our understanding of sleep quality and help protect individuals’ sleep health. The objective of this study is to explore the development and application of a multitask deep learning model that can simultaneously detect sleep arousal and score sleep stages. Methods: We employed a task-incremental learning approach to develop the multi-task deep learning model. For development and testing, 1069 polysomnography records were incorporated. Additionally, the model’s fairness was assessed across various subgroups, including sex, age, and ethnicity. Beyond performance evaluation, we investigated the intermediate features extracted by the model from the raw ECG signal. Results: The multi-task deep learning model achieved a Cohen’s 𝜅 of 0.68 for sleep stage prediction and an area under the receiver operating characteristic (AUROC) of 0.94 for arousal detection. Additionally, the model showed consistent performance across different subgroups. The analysis of the interpretability of the intermediate features validated their applicability and adaptability to related tasks in sleep analysis. Conclusion: This study demonstrated the capacity of the multi-task deep learning model to score sleep stages and arousal simultaneously with substantial precision using a single-lead ECG. This research offers a promising avenue for advancing incremental learning and multitask deep learning models in sleep analysis.

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Bidirectional LSTM with MFCC Feature Extraction for Sleep Arousal Detection in Multi-channel Signal Data

Cited by 2 publications

References 10 publications

A Review of Methods for Sleep Arousal Detection Using Polysomnographic Signals

A Review of Methods for Sleep Arousal Detection Using Polysomnographic Signals

A Multi-task Deep Learning Algorithm for Sleep Stage Scoring and Sleep Arousal Detection

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