Sleep staging classification based on a new parallel fusion method of multiple sources signals

Hei, Yafang; Yuan, Tuming; Fan, Zhigao; Bo, Yang; Hu, Jiancheng

doi:10.1088/1361-6579/ac647b

Cited by 4 publications

(2 citation statements)

References 40 publications

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“…Therefore, the signal can be mathematically expressed, as revealed in Equation ( 2) [44]. The disintegration of HRVs utilizing the DWT method has been employed by researchers for various diagnostic applications like an automated diagnosis of diabetes [48] and sleep staging classification [49] in the last few decades.…”

Section: Discrete Wavelet Transform (Dwt)mentioning

confidence: 99%

A Review of Methods and Applications for a Heart Rate Variability Analysis

Nayak,

Pradhan,

Mohanty

et al. 2023

Algorithms

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Heart rate variability (HRV) has emerged as an essential non-invasive tool for understanding cardiac autonomic function over the last few decades. This can be attributed to the direct connection between the heart’s rhythm and the activity of the sympathetic and parasympathetic nervous systems. The cost-effectiveness and ease with which one may obtain HRV data also make it an exciting and potential clinical tool for evaluating and identifying various health impairments. This article comprehensively describes a range of signal decomposition techniques and time-series modeling methods recently used in HRV analyses apart from the conventional HRV generation and feature extraction methods. Various weight-based feature selection approaches and dimensionality reduction techniques are summarized to assess the relevance of each HRV feature vector. The popular machine learning-based HRV feature classification techniques are also described. Some notable clinical applications of HRV analyses, like the detection of diabetes, sleep apnea, myocardial infarction, cardiac arrhythmia, hypertension, renal failure, psychiatric disorders, ANS Activity of Patients Undergoing Weaning from Mechanical Ventilation, and monitoring of fetal distress and neonatal critical care, are discussed. The latest research on the effect of external stimuli (like consuming alcohol) on autonomic nervous system (ANS) activity using HRV analyses is also summarized. The HRV analysis approaches summarized in our article can help future researchers to dive deep into their potential diagnostic applications.

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Section: Discrete Wavelet Transform (Dwt)mentioning

confidence: 99%

A Review of Methods and Applications for a Heart Rate Variability Analysis

Nayak,

Pradhan,

Mohanty

et al. 2023

Algorithms

View full text Add to dashboard Cite

show abstract

“…However, the Chesma, with its five hydrogel electrodes, enables an excellent SNR (18.31 dB against −2.62 dB obtained with dry electrodes), allowing accurate tracking of the eye movements and heart parameters also in conditions in which motion artifacts are present. In addition, the two acquired information can be usefully processed for both sleep staging and sleep disorder detection [47,48].…”

mentioning

confidence: 99%

Methodologies and Wearable Devices to Monitor Biophysical Parameters Related to Sleep Dysfunctions: An Overview

et al. 2022

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Sleep is crucial for human health from metabolic, mental, emotional, and social points of view; obtaining good sleep in terms of quality and duration is fundamental for maintaining a good life quality. Over the years, several systems have been proposed in the scientific literature and on the market to derive metrics used to quantify sleep quality as well as detect sleep disturbances and disorders. In this field, wearable systems have an important role in the discreet, accurate, and long-term detection of biophysical markers useful to determine sleep quality. This paper presents the current state-of-the-art wearable systems and software tools for sleep staging and detecting sleep disorders and dysfunctions. At first, the paper discusses sleep’s functions and the importance of monitoring sleep to detect eventual sleep disturbance and disorders. Afterward, an overview of prototype and commercial headband-like wearable devices to monitor sleep is presented, both reported in the scientific literature and on the market, allowing unobtrusive and accurate detection of sleep quality markers. Furthermore, a survey of scientific works related the effect of the COVID-19 pandemic on sleep functions, attributable to both infection and lifestyle changes. In addition, a survey of algorithms for sleep staging and detecting sleep disorders is introduced based on an analysis of single or multiple biosignals (EEG—electroencephalography, ECG—electrocardiography, EMG—electromyography, EOG—electrooculography, etc.). Lastly, comparative analyses and insights are provided to determine the future trends related to sleep monitoring systems.

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Current status and prospects of automatic sleep stages scoring: Review

et al. 2023

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The scoring of sleep stages is one of the essential tasks in sleep analysis. Since a manual procedure requires considerable human and financial resources, and incorporates some subjectivity, an automated approach could result in several advantages. There have been many developments in this area, and in order to provide a comprehensive overview, it is essential to review relevant recent works and summarise the characteristics of the approaches, which is the main aim of this article. To achieve it, we examined articles published between 2018 and 2022 that dealt with the automated scoring of sleep stages. In the final selection for in-depth analysis, 125 articles were included after reviewing a total of 515 publications. The results revealed that automatic scoring demonstrates good quality (with Cohen's kappa up to over 0.80 and accuracy up to over 90%) in analysing EEG/EEG + EOG + EMG signals. At the same time, it should be noted that there has been no breakthrough in the quality of results using these signals in recent years. Systems involving other signals that could potentially be acquired more conveniently for the user (e.g. respiratory, cardiac or movement signals) remain more challenging in the implementation with a high level of reliability but have considerable innovation capability. In general, automatic sleep stage scoring has excellent potential to assist medical professionals while providing an objective assessment.

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Sleep staging classification based on a new parallel fusion method of multiple sources signals

Cited by 4 publications

References 40 publications

A Review of Methods and Applications for a Heart Rate Variability Analysis

A Review of Methods and Applications for a Heart Rate Variability Analysis

Methodologies and Wearable Devices to Monitor Biophysical Parameters Related to Sleep Dysfunctions: An Overview

Current status and prospects of automatic sleep stages scoring: Review

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