An empirical comparison of machine learning algorithms for the classification of brain signals to assess the impact of combined yoga and Sudarshan Kriya. Issue 7 (19th May 2022)
- Record Type:
- Journal Article
- Title:
- An empirical comparison of machine learning algorithms for the classification of brain signals to assess the impact of combined yoga and Sudarshan Kriya. Issue 7 (19th May 2022)
- Main Title:
- An empirical comparison of machine learning algorithms for the classification of brain signals to assess the impact of combined yoga and Sudarshan Kriya
- Authors:
- Sharma, Himika
Raj, Rajnish
Juneja, Mamta - Abstract:
- Abstract: Today's fast paced life reports so much stress among people that it may lead to various psychological and physical illnesses. Yoga and meditation are the best strategies to reduce the effect of stress on physical and mental level without any side-effects. In this study, combined yoga and Sudarshan Kriya (SK) has been used as an alternative and complementary therapy for the management of stress. The aim of the study is to find a method to classify the meditator and non-meditator states with the best accuracy. The 50 subjects have been participating in this study and divided into two groups, i.e. study and control group. The subjects with regular practice of Yoga and SK are known as meditators and the ones without any practice of yoga and meditation were known as non-meditators. Electroencephalogram (EEG) signals were acquired from these both groups before and after 3 months. The statistical parameters were computed from these acquired EEG signals using Discrete Wavelet Transform (DWT). These extracted statistical parameters were given as input to the classifiers. The decision tree, discriminant analysis, logistic regression, Support Vector Machine (SVM), Weighted K- Nearest Neighbour (KNN) and ensemble classifiers were used for classification of meditator and non- meditator states from the acquired EEG signals. The results have demonstrated that the SVM method gives the highest classification accuracy as compared to other classifiers. The proposed method can be usedAbstract: Today's fast paced life reports so much stress among people that it may lead to various psychological and physical illnesses. Yoga and meditation are the best strategies to reduce the effect of stress on physical and mental level without any side-effects. In this study, combined yoga and Sudarshan Kriya (SK) has been used as an alternative and complementary therapy for the management of stress. The aim of the study is to find a method to classify the meditator and non-meditator states with the best accuracy. The 50 subjects have been participating in this study and divided into two groups, i.e. study and control group. The subjects with regular practice of Yoga and SK are known as meditators and the ones without any practice of yoga and meditation were known as non-meditators. Electroencephalogram (EEG) signals were acquired from these both groups before and after 3 months. The statistical parameters were computed from these acquired EEG signals using Discrete Wavelet Transform (DWT). These extracted statistical parameters were given as input to the classifiers. The decision tree, discriminant analysis, logistic regression, Support Vector Machine (SVM), Weighted K- Nearest Neighbour (KNN) and ensemble classifiers were used for classification of meditator and non- meditator states from the acquired EEG signals. The results have demonstrated that the SVM method gives the highest classification accuracy as compared to other classifiers. The proposed method can be used as a diagnosis system in clinical practices. … (more)
- Is Part Of:
- Computer methods in biomechanics and biomedical engineering. Volume 25:Issue 7(2022)
- Journal:
- Computer methods in biomechanics and biomedical engineering
- Issue:
- Volume 25:Issue 7(2022)
- Issue Display:
- Volume 25, Issue 7 (2022)
- Year:
- 2022
- Volume:
- 25
- Issue:
- 7
- Issue Sort Value:
- 2022-0025-0007-0000
- Page Start:
- 721
- Page End:
- 728
- Publication Date:
- 2022-05-19
- Subjects:
- Electroencephalogram (EEG) -- logistic regression -- support vector machine (SVM) -- weighted K-nearest neighbour (KNN) -- ensemble classifier -- discrete wavelet transform (DWT)
Biomechanics -- Data processing -- Periodicals
Biomedical engineering -- Periodicals
Biomechanics -- Periodicals
Biomedical Engineering -- methods -- Periodicals
Computing Methodologies -- Periodicals
612.7 - Journal URLs:
- http://www.tandfonline.com/toc/gcmb20/current ↗
http://www.tandfonline.com/ ↗ - DOI:
- 10.1080/10255842.2021.1975682 ↗
- Languages:
- English
- ISSNs:
- 1025-5842
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3394.100250
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- 21375.xml