A Novel Method of Segmentation and Classification for Meditation in Health Care Systems

Meditation improves positivity in behavioral as well as psychological changes, which are brought elucidated by knowing neuro-physiological consequences of meditation. In the field of cognitive science, neuroscience and physiological research, Electroencephalogram (EEG) is extensively utilized. The primary tasks of EEG signal analysis is to identify the noisy signal as well as enormous data that create signal processing and subsequent analysis. Beforehand any analysis of the EEG signal, the obtained raw signal must be preprocessed for eliminating undesirable artifacts as well as horrible noise. With the aim of resolving this issue, in this research, raw signals are preprocessed with the help of Band-Pass Filter (BPF) for noise removal method. Instead, in adaptive Sliding Window with Fuzzy C Means Clustering (SW-FCM) segmentation is presented, which precisely as well as automatically segments the signals. So as to analyze the accuracy, five features such as electroencephalography alpha spectrum, frequency of the main peak, Amplitude of the main peak, Higher Order Crossing (HOC), and wavelet features are used as the evaluating variables. Lastly to assess the meditation experience with Fuzzy Kernel least square Support Vector Machine (FKLSSVM) classifier, the presented method with a cross-sectional analysis is utilized. These two classifiers are utilized for meditation experience classification by utilizing an individual feature vector values from equivalent EEG signals. The dataset samples are gathered from Open source Brain-Computer Interface (Open BCI) platform. Outcomes attained are matched up for diverse techniques for identifying as well as for classifying signal segments features using MATLAB. Presented classifiers of the meditation process validate quick interpretation methods that differentiate meditation experience and valuable performance related to artificial approaches for the big-data analysis.