Performance analysis of machine learning algorithms on automated sleep staging feature sets
Abstract With the speeding up of social activities, rapid changes in lifestyles, and an increase in the pressure in professional fields, people are suffering from several types of sleep‐related disorders. It is a very tedious task for clinicians to monitor the entire sleep durations of the subjects...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-06-01
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| Series: | CAAI Transactions on Intelligence Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/cit2.12042 |
| _version_ | 1811337366980263936 |
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| author | Santosh Satapathy D Loganathan Hari Kishan Kondaveeti RamaKrushna Rath |
| author_facet | Santosh Satapathy D Loganathan Hari Kishan Kondaveeti RamaKrushna Rath |
| author_sort | Santosh Satapathy |
| collection | DOAJ |
| description | Abstract With the speeding up of social activities, rapid changes in lifestyles, and an increase in the pressure in professional fields, people are suffering from several types of sleep‐related disorders. It is a very tedious task for clinicians to monitor the entire sleep durations of the subjects and analyse the sleep staging in traditional and manual laboratory environmental methods. For the purpose of accurate diagnosis of different sleep disorders, we have considered the automated analysis of sleep epochs, which were collected from the subjects during sleep time. The complete process of an automated approach of sleep stages’ classification is majorly executed through four steps: pre‐processing the raw signals, feature extraction, feature selection, and classification. In this study, we have extracted 12 statistical properties from input signals. The proposed models are tested in three different combinations of features sets. In the first experiment, the feature set contained all the 12 features. The second and third experiments were conducted with the nine and five best features. The patient records come from the ISRUC‐Sleep database. The highest classification accuracy was achieved for sleep staging through combinations with the five feature set. From the categories of the subjects, the reported accuracy results were found to exceed above 90%. As per the outcome from the proposed system the random forest classification techniques achieved best accuracy incomparable to that of the other two classifiers. |
| first_indexed | 2024-04-13T17:52:54Z |
| format | Article |
| id | doaj.art-f5ecae9180f94d239afc9a9b6eb71517 |
| institution | Directory Open Access Journal |
| issn | 2468-2322 |
| language | English |
| last_indexed | 2024-04-13T17:52:54Z |
| publishDate | 2021-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | CAAI Transactions on Intelligence Technology |
| spelling | doaj.art-f5ecae9180f94d239afc9a9b6eb715172022-12-22T02:36:37ZengWileyCAAI Transactions on Intelligence Technology2468-23222021-06-016215517410.1049/cit2.12042Performance analysis of machine learning algorithms on automated sleep staging feature setsSantosh Satapathy0D Loganathan1Hari Kishan Kondaveeti2RamaKrushna Rath3Puducherry Research Scholar of Computer Science and Engineering Pondicherry Engineering College, Puducherry IndiaProfessor of Computer Science and Engineering Pondicherry Engineering College, Puducherry Puducherry IndiaAssistant Professor of Computer Science and Engineering VIT University, Amaravati Andhra Pradesh IndiaResearch Scholar of Computer Science and Engineering, Anna University Chennai IndiaAbstract With the speeding up of social activities, rapid changes in lifestyles, and an increase in the pressure in professional fields, people are suffering from several types of sleep‐related disorders. It is a very tedious task for clinicians to monitor the entire sleep durations of the subjects and analyse the sleep staging in traditional and manual laboratory environmental methods. For the purpose of accurate diagnosis of different sleep disorders, we have considered the automated analysis of sleep epochs, which were collected from the subjects during sleep time. The complete process of an automated approach of sleep stages’ classification is majorly executed through four steps: pre‐processing the raw signals, feature extraction, feature selection, and classification. In this study, we have extracted 12 statistical properties from input signals. The proposed models are tested in three different combinations of features sets. In the first experiment, the feature set contained all the 12 features. The second and third experiments were conducted with the nine and five best features. The patient records come from the ISRUC‐Sleep database. The highest classification accuracy was achieved for sleep staging through combinations with the five feature set. From the categories of the subjects, the reported accuracy results were found to exceed above 90%. As per the outcome from the proposed system the random forest classification techniques achieved best accuracy incomparable to that of the other two classifiers.https://doi.org/10.1049/cit2.12042electroencephalographyfeature extractionlearning (artificial intelligence)medical signal processingpatient diagnosispattern classification |
| spellingShingle | Santosh Satapathy D Loganathan Hari Kishan Kondaveeti RamaKrushna Rath Performance analysis of machine learning algorithms on automated sleep staging feature sets CAAI Transactions on Intelligence Technology electroencephalography feature extraction learning (artificial intelligence) medical signal processing patient diagnosis pattern classification |
| title | Performance analysis of machine learning algorithms on automated sleep staging feature sets |
| title_full | Performance analysis of machine learning algorithms on automated sleep staging feature sets |
| title_fullStr | Performance analysis of machine learning algorithms on automated sleep staging feature sets |
| title_full_unstemmed | Performance analysis of machine learning algorithms on automated sleep staging feature sets |
| title_short | Performance analysis of machine learning algorithms on automated sleep staging feature sets |
| title_sort | performance analysis of machine learning algorithms on automated sleep staging feature sets |
| topic | electroencephalography feature extraction learning (artificial intelligence) medical signal processing patient diagnosis pattern classification |
| url | https://doi.org/10.1049/cit2.12042 |
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