Dataset Information

A cross-sectional evaluation of meditation experience on electroencephalography data by artificial neural network and support vector machine classifiers.

ABSTRACT: To quantitate the meditation experience is a subjective and complex issue because it is confounded by many factors such as emotional state, method of meditation, and personal physical condition. In this study, we propose a strategy with a cross-sectional analysis to evaluate the meditation experience with 2 artificial intelligence techniques: artificial neural network and support vector machine. Within this analysis system, 3 features of the electroencephalography alpha spectrum and variant normalizing scaling are manipulated as the evaluating variables for the detection of accuracy. Thereafter, by modulating the sliding window (the period of the analyzed data) and shifting interval of the window (the time interval to shift the analyzed data), the effect of immediate analysis for the 2 methods is compared. This analysis system is performed on 3 meditation groups, categorizing their meditation experiences in 10-year intervals from novice to junior and to senior. After an exhausted calculation and cross-validation across all variables, the high accuracy rate >98% is achievable under the criterion of 0.5-minute sliding window and 2?seconds shifting interval for both methods. In a word, the minimum analyzable data length is 0.5?minute and the minimum recognizable temporal resolution is 2?seconds in the decision of meditative classification. Our proposed classifier of the meditation experience promotes a rapid evaluation system to distinguish meditation experience and a beneficial utilization of artificial techniques for the big-data analysis.

SUBMITTER: Lee YH

PROVIDER: S-EPMC5406072 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A cross-sectional evaluation of meditation experience on electroencephalography data by artificial neural network and support vector machine classifiers.

Lee Yu-Hao YH Hsieh Ya-Ju YJ Shiah Yung-Jong YJ Lin Yu-Huei YH Chen Chiao-Yun CY Tyan Yu-Chang YC GengQiu JiaCheng J Hsu Chung-Yao CY Chen Sharon Chia-Ju SC

Medicine 20170401 16

To quantitate the meditation experience is a subjective and complex issue because it is confounded by many factors such as emotional state, method of meditation, and personal physical condition. In this study, we propose a strategy with a cross-sectional analysis to evaluate the meditation experience with 2 artificial intelligence techniques: artificial neural network and support vector machine. Within this analysis system, 3 features of the electroencephalography alpha spectrum and variant norm ...[more]

PMID: 28422856

Similar Datasets

Project description:BackgroundInterview-based schizophrenia (SCZ) diagnostic methods are not completely valid. Moreover, SCZ-the disease entity is very heterogeneous. Supervised-Machine-Learning (sML) application of Artificial-Intelligence holds a tremendous promise in solving these issues.AimsTo sML-based discriminating validity of resting-state electroencephalographic (EEG) quantitative features in classifying SCZ from healthy and, positive (PS) and negative symptom (NS) subgroups, using a high-density recording.Settings and designData collected at a tertiary care mental-health institute using a cross-sectional study design and analyzed at a premier Engineering Institute.Materials and methodsData of 38-SCZ patients and 20-healthy controls were retrieved. The positive-negative subgroup classification was done using Positive and Negative Syndrome Scale operational-criteria. EEG was recorded using 256-channel high-density equipment. Eight priori regions-of-interest were selected. Six-level wavelet decomposition and Kernel-Support Vector Machine (SVM) method were used for feature extraction and data classification.Statistical analysisMann-Whitney test was used for comparison of machine learning-features. Accuracy, sensitivity, specificity, and area under receiver operating characteristics-curve were measured as discriminatory indices of classifications.ResultsAccuracy of classifying SCZ from healthy and PS from NS SCZ, were 78.95% and 89.29%, respectively. While beta and gamma frequency related features most accurately classified SCZ from healthy controls, delta and theta frequency related features most accurately classified positive from negative SCZ. Inferior frontal gyrus features most accurately contributed to both the classificatory instances.ConclusionsSVM-based classification and sub-classification of SCZ using EEG data is optimal and might help in improving the "validity" and reducing the "heterogeneity" in the diagnosis of SCZ. These results might only be generalized to acute and moderately ill male SCZ patients.

Dataset Information

A cross-sectional evaluation of meditation experience on electroencephalography data by artificial neural network and support vector machine classifiers.

Publications

A cross-sectional evaluation of meditation experience on electroencephalography data by artificial neural network and support vector machine classifiers.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure