An empirical comparison of machine learning algorithms for the classification of brain signals to assess the impact of combined yoga and Sudarshan Kriya

• Published in: Computer methods in biomechanics and biomedical engineering Vol. 25; no. 7; pp. 721 - 728
• Main Authors: Sharma, Himika, Raj, Rajnish, Juneja, Mamta
• Format: Journal Article
• Language: English
• Published: England Taylor & Francis 19.05.2022; Taylor & Francis Ltd
• Subjects: Algorithms; Classification; Classifiers; Decision analysis; Decision trees; Discrete Wavelet Transform; discrete wavelet transform (DWT); Discriminant analysis; EEG; Electroencephalogram (EEG); Electroencephalography; Empirical analysis; ensemble classifier; logistic regression; Machine learning; Meditation; Parameters; Signal classification; Statistical analysis; Statistics; support vector machine (SVM); Support vector machines; Wavelet transforms; weighted K-nearest neighbour (KNN); Yoga; discrete wavelet transform (DWT); ensemble classifier; Electroencephalogram (EEG); logistic regression; support vector machine (SVM); weighted K-nearest neighbour (KNN)
• ISSN: 1025-5842; 1476-8259; 1476-8259
• DOI: 10.1080/10255842.2021.1975682

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.