MS-Net: Sleep apnea detection in PPG using multi-scale block and shadow module one-dimensional convolutional neural network

Wei, Keming; Zou, Lang; Liu, Guanzheng; Wang, Changhong

doi:10.1016/j.compbiomed.2022.106469

Cited by 16 publications

(5 citation statements)

References 43 publications

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“…The convolutional neural network has recently been utilized to diagnose sleep apnea using photoplethysmography (PPG). By examining the changes in the PPG signal that represent the respiratory and cardiac activities, PPG can be utilized to identify sleep apnea (60,61). To determine the stages of sleep and the arousal events that occur during sleep, PPG can also record the autonomic nervous system's modulation.…”

Section: Discussionmentioning

confidence: 99%

Systematic Review of Detecting Sleep Apnea Using Artificial Intelligence: An Insight to Convolutional Neural Network Method

Samadi,

Samadi

et al. 2024

Arch Neurosci

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Background: Sleep apnea is a prevalent sleep disorder, especially in males and older ages. The common diagnostic methods, including polysomnography (PSG), are expensive, difficult to perform, and time-consuming. Numerous studies are focusing on developing easy-to-perform methods based on artificial intelligence (AI) for the early diagnosis of sleep apnea. This systematic review aimed to gather current methods based on the convolutional neural network (CNN) for the diagnosis of sleep apnea. Methods: Three international electronic databases (PubMed, Web of Science [WoS], and Scopus) were searched from 2010 to October 2023. All studies that have developed CNN-based methods for the diagnosis of sleep apnea and have accomplished the performance tests were included. Finally, the characteristics of the studies were extracted and summarized. Results: A total of 36 studies were included in this systematic review. Various physiological signals have been proposed to detect sleep apnea, including electrocardiogram (ECG), blood oxygen saturation (SpO2), sound signals, respiratory signals, electroencephalogram (EEG), and nasal airflow. Electrocardiogram was the most frequently used signal in the studies, followed by SpO2. The highest reported accuracy was achieved by SpO2 or ECG-based methods and with a one-dimensional CNN (1D-CNN) classifier. Using multiple signals did not necessarily increase the performance of test results. Conclusions: Diagnostic methods based on CNN can be used only as screening tools or home diagnosis of sleep apnea. These methods are easy to perform and can only reduce the diagnostic costs and waiting time for a sleep study in special scenarios. Nevertheless, PSG is still the gold standard for the diagnosis of sleep disorders.

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Section: Discussionmentioning

confidence: 99%

Systematic Review of Detecting Sleep Apnea Using Artificial Intelligence: An Insight to Convolutional Neural Network Method

Samadi,

Samadi

et al. 2024

Arch Neurosci

View full text Add to dashboard Cite

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“…5 d, LSTM and 3D-CNN-based models are proposed, respectively. However, these architectures have limitations in extracting temporal features since they mainly rely on local features [28] . Although the receptive field might cover the entire image, the pixels that are “far away” from their corresponding feature have limited influence on the value of that feature.…”

Section: Proposed Approachmentioning

confidence: 99%

Convolutional neural network-based classification and monitoring models for lung cancer detection: 3D perspective approach

Muñoz-Aseguinolaza,

Fernandez-Iriondo,

Rodríguez-Moreno

et al. 2023

Heliyon

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“…Recent years have witnessed rapid advances in artificial intelligence technology, and CNNs, as an effective method for feature extraction, have shown outstanding performance in various fields besides computer vision. In particular, one-dimension CNNs excel at handling time-series data [27]. This paper employs an MS-1DCN to capture local fluctuation features in stock sequence data.…”

Section: Multi-scale One-dimension Convolution Layermentioning

confidence: 99%

A Framework for Enhancing Stock Investment Performance by Predicting Important Trading Points with Return-Adaptive Piecewise Linear Representation and Batch Attention Multi-Scale Convolutional Recurrent Neural Network

Lin,

Liu

2023

Entropy

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Efficient stock status analysis and forecasting are important for stock market participants to be able to improve returns and reduce associated risks. However, stock market data are replete with noise and randomness, rendering the task of attaining precise price predictions arduous. Moreover, the lagging phenomenon of price prediction makes it hard for the corresponding trading strategy to capture the turning points, resulting in lower investment returns. To address this issue, we propose a framework for Important Trading Point (ITP) prediction based on Return-Adaptive Piecewise Linear Representation (RA-PLR) and a Batch Attention Multi-Scale Convolution Recurrent Neural Network (Batch-MCRNN) with the starting point of improving stock investment returns. Firstly, a novel RA-PLR method is adopted to detect historical ITPs in the stock market. Then, we apply the Batch-MCRNN model to integrate the information of the data across space, time, and sample dimensions for predicting future ITPs. Finally, we design a trading strategy that combines the Relative Strength Index (RSI) and the Double Check (DC) method to match ITP predictions. We conducted a comprehensive and systematic comparison with several state-of-the-art benchmark models on real-world datasets regarding prediction accuracy, risk, return, and other indicators. Our proposed method significantly outperformed the comparative methods on all indicators and has significant reference value for stock investment.

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MS-Net: Sleep apnea detection in PPG using multi-scale block and shadow module one-dimensional convolutional neural network

Cited by 16 publications

References 43 publications

Systematic Review of Detecting Sleep Apnea Using Artificial Intelligence: An Insight to Convolutional Neural Network Method

Systematic Review of Detecting Sleep Apnea Using Artificial Intelligence: An Insight to Convolutional Neural Network Method

Convolutional neural network-based classification and monitoring models for lung cancer detection: 3D perspective approach

A Framework for Enhancing Stock Investment Performance by Predicting Important Trading Points with Return-Adaptive Piecewise Linear Representation and Batch Attention Multi-Scale Convolutional Recurrent Neural Network

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