Ensemble Computational Intelligent for Insomnia Sleep Stage Detection via the Sleep ECG Signal
Insomnia is a common sleep disorder in which patients cannot sleep properly. Accurate detection of insomnia disorder is a crucial step for mental disease analysis in the early stages. The disruption in getting quality sleep is one of the big sources of cardiovascular syndromes such as blood pressure...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9917483/ |
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| author | Pragati Tripathi M. A. Ansari Tapan Kumar Gandhi Rajat Mehrotra Md Belal Bin Heyat Faijan Akhtar Chiagoziem C. Ukwuoma Abdullah Y. Muaad Yasser M. Kadah Mugahed A. Al-Antari Jian Ping Li |
| author_facet | Pragati Tripathi M. A. Ansari Tapan Kumar Gandhi Rajat Mehrotra Md Belal Bin Heyat Faijan Akhtar Chiagoziem C. Ukwuoma Abdullah Y. Muaad Yasser M. Kadah Mugahed A. Al-Antari Jian Ping Li |
| author_sort | Pragati Tripathi |
| collection | DOAJ |
| description | Insomnia is a common sleep disorder in which patients cannot sleep properly. Accurate detection of insomnia disorder is a crucial step for mental disease analysis in the early stages. The disruption in getting quality sleep is one of the big sources of cardiovascular syndromes such as blood pressure and stroke. The traditional insomnia detection methods are time-consuming, cumbersome, and more expensive because they demand a long time from a trained neurophysiologist, and they are prone to human error, hence, the accuracy of diagnosis gets compromised. Therefore, the automatic insomnia diagnosis from the electrocardiogram (ECG) records is vital for timely detection and cure. In this paper, a novel hybrid artificial intelligence (AI) approach is proposed based on the power spectral density (PSD) of the heart rate variability (HRV) to detect insomnia in three classification scenarios: (1) subject-based classification scenario (normal Vs. insomnia), (2) sleep stage-based classification (REM Vs. W. stage), and (3) the combined classification scenario using both subject-based and sleep stage-based deep features. The ensemble learning of random forest (RF) and decision tree (DT) classifiers are used to perform the first and second classification scenarios, while the linear discriminant analysis (LDA) classifier is used to perform the third combined scenario. The proposed framework includes data collection, investigation of the ECG signals, extraction of the signal HRV, estimation of the PSD, and AI-based classification via hybrid machine learning classifiers. The proposed framework is fine-tuned and evaluated using the free public PhysioNet dataset over fivefold trails cross-validation. For the subject-based classification scenario, the detection performance in terms of sensitivity, specificity, and accuracy is recorded to be 96.0%, 94.0%, and 96.0%, respectively. For the sleep stage-based classification scenario, the detection evaluation results are recorded equally with 96.0% for ceiling level accuracy, sensitivity, and specificity. For the combined classification scenario, the LDA classifier has achieved the best insomnia detection accuracy with 99.0%. In the future, the proposed hybrid AI approach could be applicable for mobile observation schemes to automatically detect insomnia disorders. |
| first_indexed | 2024-04-13T16:55:21Z |
| format | Article |
| id | doaj.art-bd3ee3c83b8240f1b2b74eb79f869e62 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-13T16:55:21Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-bd3ee3c83b8240f1b2b74eb79f869e622022-12-22T02:38:50ZengIEEEIEEE Access2169-35362022-01-011010871010872110.1109/ACCESS.2022.32121209917483Ensemble Computational Intelligent for Insomnia Sleep Stage Detection via the Sleep ECG SignalPragati Tripathi0https://orcid.org/0000-0002-2770-2390M. A. Ansari1Tapan Kumar Gandhi2Rajat Mehrotra3https://orcid.org/0000-0002-7170-4895Md Belal Bin Heyat4https://orcid.org/0000-0001-5307-9582Faijan Akhtar5Chiagoziem C. Ukwuoma6https://orcid.org/0000-0002-4532-6026Abdullah Y. Muaad7Yasser M. Kadah8https://orcid.org/0000-0002-2166-3191Mugahed A. Al-Antari9https://orcid.org/0000-0002-4457-4407Jian Ping Li10https://orcid.org/0000-0003-2192-1450Department of Electrical Engineering, Greater Noida, Gautam Buddha University, Uttar Pradesh, IndiaDepartment of Electrical Engineering, Greater Noida, Gautam Buddha University, Uttar Pradesh, IndiaDepartment of Electrical Engineering, Indian Institute of Technology Delhi, Delhi, IndiaDepartment of Electrical Engineering, G.L. Bajaj Institute of Technology and Management, Greater Noida, Uttar Pradesh, IndiaIoT Research Center, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, ChinaSchool of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaSchool of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaDepartment of Studies in Computer Science, University of Mysore, Mysuru, Karnataka, IndiaElectrical and Computer Engineering Department, King Abdulaziz University, Jeddah, Saudi ArabiaDepartment of Artificial Intelligence, Daeyang AI Center, College of Software and Convergence Technology, Sejong University, Seoul, Republic of KoreaSchool of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaInsomnia is a common sleep disorder in which patients cannot sleep properly. Accurate detection of insomnia disorder is a crucial step for mental disease analysis in the early stages. The disruption in getting quality sleep is one of the big sources of cardiovascular syndromes such as blood pressure and stroke. The traditional insomnia detection methods are time-consuming, cumbersome, and more expensive because they demand a long time from a trained neurophysiologist, and they are prone to human error, hence, the accuracy of diagnosis gets compromised. Therefore, the automatic insomnia diagnosis from the electrocardiogram (ECG) records is vital for timely detection and cure. In this paper, a novel hybrid artificial intelligence (AI) approach is proposed based on the power spectral density (PSD) of the heart rate variability (HRV) to detect insomnia in three classification scenarios: (1) subject-based classification scenario (normal Vs. insomnia), (2) sleep stage-based classification (REM Vs. W. stage), and (3) the combined classification scenario using both subject-based and sleep stage-based deep features. The ensemble learning of random forest (RF) and decision tree (DT) classifiers are used to perform the first and second classification scenarios, while the linear discriminant analysis (LDA) classifier is used to perform the third combined scenario. The proposed framework includes data collection, investigation of the ECG signals, extraction of the signal HRV, estimation of the PSD, and AI-based classification via hybrid machine learning classifiers. The proposed framework is fine-tuned and evaluated using the free public PhysioNet dataset over fivefold trails cross-validation. For the subject-based classification scenario, the detection performance in terms of sensitivity, specificity, and accuracy is recorded to be 96.0%, 94.0%, and 96.0%, respectively. For the sleep stage-based classification scenario, the detection evaluation results are recorded equally with 96.0% for ceiling level accuracy, sensitivity, and specificity. For the combined classification scenario, the LDA classifier has achieved the best insomnia detection accuracy with 99.0%. In the future, the proposed hybrid AI approach could be applicable for mobile observation schemes to automatically detect insomnia disorders.https://ieeexplore.ieee.org/document/9917483/Sleep disordercardiovascular syndromesECG sleep signalsAI-based insomnia detection27 machine learningCAP sleep database |
| spellingShingle | Pragati Tripathi M. A. Ansari Tapan Kumar Gandhi Rajat Mehrotra Md Belal Bin Heyat Faijan Akhtar Chiagoziem C. Ukwuoma Abdullah Y. Muaad Yasser M. Kadah Mugahed A. Al-Antari Jian Ping Li Ensemble Computational Intelligent for Insomnia Sleep Stage Detection via the Sleep ECG Signal IEEE Access Sleep disorder cardiovascular syndromes ECG sleep signals AI-based insomnia detection 27 machine learning CAP sleep database |
| title | Ensemble Computational Intelligent for Insomnia Sleep Stage Detection via the Sleep ECG Signal |
| title_full | Ensemble Computational Intelligent for Insomnia Sleep Stage Detection via the Sleep ECG Signal |
| title_fullStr | Ensemble Computational Intelligent for Insomnia Sleep Stage Detection via the Sleep ECG Signal |
| title_full_unstemmed | Ensemble Computational Intelligent for Insomnia Sleep Stage Detection via the Sleep ECG Signal |
| title_short | Ensemble Computational Intelligent for Insomnia Sleep Stage Detection via the Sleep ECG Signal |
| title_sort | ensemble computational intelligent for insomnia sleep stage detection via the sleep ecg signal |
| topic | Sleep disorder cardiovascular syndromes ECG sleep signals AI-based insomnia detection 27 machine learning CAP sleep database |
| url | https://ieeexplore.ieee.org/document/9917483/ |
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