Machine-Learning-Based Elderly Stroke Monitoring System Using Electroencephalography Vital Signals
Stroke is the third highest cause of death worldwide after cancer and heart disease, and the number of stroke diseases due to aging is set to at least triple by 2030. As the top three causes of death worldwide are all related to chronic disease, the importance of healthcare is increasing even more....
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MDPI AG
2021-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/4/1761 |
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| author | Yoon-A Choi Sejin Park Jong-Arm Jun Chee Meng Benjamin Ho Cheol-Sig Pyo Hansung Lee Jaehak Yu |
| author_facet | Yoon-A Choi Sejin Park Jong-Arm Jun Chee Meng Benjamin Ho Cheol-Sig Pyo Hansung Lee Jaehak Yu |
| author_sort | Yoon-A Choi |
| collection | DOAJ |
| description | Stroke is the third highest cause of death worldwide after cancer and heart disease, and the number of stroke diseases due to aging is set to at least triple by 2030. As the top three causes of death worldwide are all related to chronic disease, the importance of healthcare is increasing even more. Models that can predict real-time health conditions and diseases using various healthcare services are attracting increasing attention. Most diagnosis and prediction methods of stroke for the elderly involve imaging techniques such as magnetic resonance imaging (MRI). It is difficult to rapidly and accurately diagnose and predict stroke diseases due to the long testing times and high costs associated with MRI. Thus, in this paper, we design and implement a health monitoring system that can predict the precursors of stroke diseases in the elderly in real time during daily walking. First, raw electroencephalography (EEG) data from six channels were preprocessed via Fast Fourier Transform (FFT). The raw EEG power values were then extracted from the raw spectra: alpha (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>α</mi></semantics></math></inline-formula>), beta (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>β</mi></semantics></math></inline-formula>), gamma (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>γ</mi></semantics></math></inline-formula>), delta (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>δ</mi></semantics></math></inline-formula>), and theta (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>θ</mi></semantics></math></inline-formula>) as well as the low <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>β</mi></semantics></math></inline-formula>, high <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>β</mi></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>θ</mi></semantics></math></inline-formula> to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>β</mi></semantics></math></inline-formula> ratio, respectively. The experiments in this paper confirm that the important features of EEG biometric signals alone during walking can accurately determine stroke precursors and occurrence in the elderly with more than 90% accuracy. Further, the Random Forest algorithm with quartiles and Z-score normalization validates the clinical significance and performance of the system proposed in this paper with a 92.51% stroke prediction accuracy. The proposed system can be implemented at a low cost, and it can be applied for early disease detection and prediction using the precursor symptoms of real-time stroke. Furthermore, it is expected that it will be able to detect other diseases such as cancer and heart disease in the future. |
| first_indexed | 2024-03-09T00:49:34Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T00:49:34Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-6d08de533b3d4205b503786c1435780e2023-12-11T17:18:04ZengMDPI AGApplied Sciences2076-34172021-02-01114176110.3390/app11041761Machine-Learning-Based Elderly Stroke Monitoring System Using Electroencephalography Vital SignalsYoon-A Choi0Sejin Park1Jong-Arm Jun2Chee Meng Benjamin Ho3Cheol-Sig Pyo4Hansung Lee5Jaehak Yu6Department of KSB Convergence Research, Electronics and Telecommunications Research Institute (ETRI), Daejeon 34129, KoreaResearch Team for Health & Safety Convergence, Korea Research Institute of Standards and Science (KRISS), Daejeon 34113, KoreaDepartment of KSB Convergence Research, Electronics and Telecommunications Research Institute (ETRI), Daejeon 34129, KoreaAI Research Team, Sewon Intelligence Company, 35 Sejong-daero, Jung-gu, Seoul 04512, KoreaDepartment of KSB Convergence Research, Electronics and Telecommunications Research Institute (ETRI), Daejeon 34129, KoreaSchool of Computer Engineering, Youngsan University, 288 Junam-Ro, Yangsan, Gyeongnam 50510, KoreaDepartment of KSB Convergence Research, Electronics and Telecommunications Research Institute (ETRI), Daejeon 34129, KoreaStroke is the third highest cause of death worldwide after cancer and heart disease, and the number of stroke diseases due to aging is set to at least triple by 2030. As the top three causes of death worldwide are all related to chronic disease, the importance of healthcare is increasing even more. Models that can predict real-time health conditions and diseases using various healthcare services are attracting increasing attention. Most diagnosis and prediction methods of stroke for the elderly involve imaging techniques such as magnetic resonance imaging (MRI). It is difficult to rapidly and accurately diagnose and predict stroke diseases due to the long testing times and high costs associated with MRI. Thus, in this paper, we design and implement a health monitoring system that can predict the precursors of stroke diseases in the elderly in real time during daily walking. First, raw electroencephalography (EEG) data from six channels were preprocessed via Fast Fourier Transform (FFT). The raw EEG power values were then extracted from the raw spectra: alpha (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>α</mi></semantics></math></inline-formula>), beta (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>β</mi></semantics></math></inline-formula>), gamma (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>γ</mi></semantics></math></inline-formula>), delta (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>δ</mi></semantics></math></inline-formula>), and theta (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>θ</mi></semantics></math></inline-formula>) as well as the low <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>β</mi></semantics></math></inline-formula>, high <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>β</mi></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>θ</mi></semantics></math></inline-formula> to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>β</mi></semantics></math></inline-formula> ratio, respectively. The experiments in this paper confirm that the important features of EEG biometric signals alone during walking can accurately determine stroke precursors and occurrence in the elderly with more than 90% accuracy. Further, the Random Forest algorithm with quartiles and Z-score normalization validates the clinical significance and performance of the system proposed in this paper with a 92.51% stroke prediction accuracy. The proposed system can be implemented at a low cost, and it can be applied for early disease detection and prediction using the precursor symptoms of real-time stroke. Furthermore, it is expected that it will be able to detect other diseases such as cancer and heart disease in the future.https://www.mdpi.com/2076-3417/11/4/1761electroencephalographymachine learningstroke predictionreal-time health monitoringstroke disease analysis |
| spellingShingle | Yoon-A Choi Sejin Park Jong-Arm Jun Chee Meng Benjamin Ho Cheol-Sig Pyo Hansung Lee Jaehak Yu Machine-Learning-Based Elderly Stroke Monitoring System Using Electroencephalography Vital Signals Applied Sciences electroencephalography machine learning stroke prediction real-time health monitoring stroke disease analysis |
| title | Machine-Learning-Based Elderly Stroke Monitoring System Using Electroencephalography Vital Signals |
| title_full | Machine-Learning-Based Elderly Stroke Monitoring System Using Electroencephalography Vital Signals |
| title_fullStr | Machine-Learning-Based Elderly Stroke Monitoring System Using Electroencephalography Vital Signals |
| title_full_unstemmed | Machine-Learning-Based Elderly Stroke Monitoring System Using Electroencephalography Vital Signals |
| title_short | Machine-Learning-Based Elderly Stroke Monitoring System Using Electroencephalography Vital Signals |
| title_sort | machine learning based elderly stroke monitoring system using electroencephalography vital signals |
| topic | electroencephalography machine learning stroke prediction real-time health monitoring stroke disease analysis |
| url | https://www.mdpi.com/2076-3417/11/4/1761 |
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