Cargando…
Validation of the influence of biosignals on performance of machine learning algorithms for sleep stage classification
BACKGROUND: Sleep stage identification is critical in multiple areas (e.g. medicine or psychology) to diagnose sleep-related disorders. Previous studies have reported that the performance of machine learning algorithms can be changed depending on the biosignals and feature-extraction processes in sl...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10017951/ https://www.ncbi.nlm.nih.gov/pubmed/36937698 http://dx.doi.org/10.1177/20552076231163783 |
_version_ | 1784907704084463616 |
---|---|
author | Choi, Junggu Kwon, Seohyun Park, Sohyun Han, Sanghoon |
author_facet | Choi, Junggu Kwon, Seohyun Park, Sohyun Han, Sanghoon |
author_sort | Choi, Junggu |
collection | PubMed |
description | BACKGROUND: Sleep stage identification is critical in multiple areas (e.g. medicine or psychology) to diagnose sleep-related disorders. Previous studies have reported that the performance of machine learning algorithms can be changed depending on the biosignals and feature-extraction processes in sleep stage classification. METHODS: To compare as many conditions as possible, 414 experimental conditions were applied, considering the combination of different biosignals, biosignal length, and window length. Five biosignals in polysomnography (i.e. electrocardiogram (ECG), electroencephalogram (EEG), electromyogram (EMG), electrooculogram left, and electrooculogram right) were used to identify optimal signal combinations for classification. In addition, three different signal-length conditions and six different window-length conditions were applied. The validity of each condition was examined via classification performance from the XGBoost classifiers trained using 10-fold cross-validation. Furthermore, results considering feature importance were examined to validate the experimental results in terms of model explanation. RESULTS: The combination of EEG + EMG + ECG with a 40 s window and 120 s signal length resulted in the best classification performance (precision: 0.853, recall: 0.855, F1-score: 0.853, and accuracy: 0.853). Compared to other conditions and feature importance results, EEG signals showed a relatively higher importance for classification in the present study. CONCLUSION: We determined the optimal biosignal and window conditions for the feature-extraction process in machine learning algorithm-based sleep stage classification. Our experimental results inform researchers in the future conduct of related studies. To generalize our results, more diverse methodologies and conditions should be applied in future studies. |
format | Online Article Text |
id | pubmed-10017951 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-100179512023-03-17 Validation of the influence of biosignals on performance of machine learning algorithms for sleep stage classification Choi, Junggu Kwon, Seohyun Park, Sohyun Han, Sanghoon Digit Health Original Research BACKGROUND: Sleep stage identification is critical in multiple areas (e.g. medicine or psychology) to diagnose sleep-related disorders. Previous studies have reported that the performance of machine learning algorithms can be changed depending on the biosignals and feature-extraction processes in sleep stage classification. METHODS: To compare as many conditions as possible, 414 experimental conditions were applied, considering the combination of different biosignals, biosignal length, and window length. Five biosignals in polysomnography (i.e. electrocardiogram (ECG), electroencephalogram (EEG), electromyogram (EMG), electrooculogram left, and electrooculogram right) were used to identify optimal signal combinations for classification. In addition, three different signal-length conditions and six different window-length conditions were applied. The validity of each condition was examined via classification performance from the XGBoost classifiers trained using 10-fold cross-validation. Furthermore, results considering feature importance were examined to validate the experimental results in terms of model explanation. RESULTS: The combination of EEG + EMG + ECG with a 40 s window and 120 s signal length resulted in the best classification performance (precision: 0.853, recall: 0.855, F1-score: 0.853, and accuracy: 0.853). Compared to other conditions and feature importance results, EEG signals showed a relatively higher importance for classification in the present study. CONCLUSION: We determined the optimal biosignal and window conditions for the feature-extraction process in machine learning algorithm-based sleep stage classification. Our experimental results inform researchers in the future conduct of related studies. To generalize our results, more diverse methodologies and conditions should be applied in future studies. SAGE Publications 2023-03-14 /pmc/articles/PMC10017951/ /pubmed/36937698 http://dx.doi.org/10.1177/20552076231163783 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
spellingShingle | Original Research Choi, Junggu Kwon, Seohyun Park, Sohyun Han, Sanghoon Validation of the influence of biosignals on performance of machine learning algorithms for sleep stage classification |
title | Validation of the influence of biosignals on performance of machine learning algorithms for sleep stage classification |
title_full | Validation of the influence of biosignals on performance of machine learning algorithms for sleep stage classification |
title_fullStr | Validation of the influence of biosignals on performance of machine learning algorithms for sleep stage classification |
title_full_unstemmed | Validation of the influence of biosignals on performance of machine learning algorithms for sleep stage classification |
title_short | Validation of the influence of biosignals on performance of machine learning algorithms for sleep stage classification |
title_sort | validation of the influence of biosignals on performance of machine learning algorithms for sleep stage classification |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10017951/ https://www.ncbi.nlm.nih.gov/pubmed/36937698 http://dx.doi.org/10.1177/20552076231163783 |
work_keys_str_mv | AT choijunggu validationoftheinfluenceofbiosignalsonperformanceofmachinelearningalgorithmsforsleepstageclassification AT kwonseohyun validationoftheinfluenceofbiosignalsonperformanceofmachinelearningalgorithmsforsleepstageclassification AT parksohyun validationoftheinfluenceofbiosignalsonperformanceofmachinelearningalgorithmsforsleepstageclassification AT hansanghoon validationoftheinfluenceofbiosignalsonperformanceofmachinelearningalgorithmsforsleepstageclassification |