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Detection of sleep apnea using Machine learning algorithms based on ECG Signals: A comprehensive systematic review by Nader Salari; Amin Hosseinian-Far; Masoud Mohammadi; Hooman Ghasemi; Habibolah Khazaie; Alireza Daneshkhah; Arash Ahmadi is a Computer Science article available to read on EtoBox.
What is Detection of sleep apnea using Machine learning algorithms based on ECG Signals: A comprehensive systematic review about?
Sleep apnea (SA) is a common sleep disorder that is not easy to detect. Recent studies have highlighted ECG analysis as an effective method of diagnosing SA. Because the changes caused by SA on the ECG are imperceptible, the need for new methods in diagnosing this disease is required more than ever. Machine Learning (ML) is recognized as one of the most successful methods of computer aided diagnosis. ML uses new methods to diagnose diseases using past clinical results. The purpose of this study is to evaluate studies using ML algorithms based on ECG characteristics to assess people suffering from SA. In this study, systematically-reviewed articles written in English before October 2020 and indexed in PubMed, Scopus, Web of Science, and IEEE databases were searched with no lower time limit. From these articles, 48 were selected for further review. The selected articles adopteddifferent ML methods for classification. All of these studies were binary where SA was detected from the normal state based on a full ECG stripe (per record), or based on one-minute segments (per segment). Our analysis show that the most common features used in the studies were frequency, time series, and stati
Who reads Detection of sleep apnea using Machine learning algorithms based on ECG Signals: A comprehensive systematic review?
It is typically read by researchers, students, and practitioners in Computer Science.
- Author
- Nader Salari; Amin Hosseinian-Far; Masoud Mohammadi; Hooman Ghasemi; Habibolah Khazaie; Alireza Daneshkhah; Arash Ahmadi
- Publisher
- Elsevier BV
- Published
- 2022
- Language
- EN
- Field
- Computer Science (Physical Sciences)