A cross-sectional evaluation of meditation experience on electroencephalography data by artificial neural network and support vector machine classifiers.
机构：[a]Department of Electrical Engineering, National Cheng Kung University, Tainan,[b]Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan[c]Graduate Institute of Counseling Psychology and Rehabilitation Counseling, National Kaohsiung Normal University, Kaohsiung, Taiwan[d]Department of Medical Imaging, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan[e]Center for Infectious Disease and Cancer Research, Kaohsiung Medical University, Kaohsiung, Taiwan[f]Institute of Medical Science and Technology, National Sun Yat-sen University, Kaohsiung, Taiwan[g]Graduate Institute of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan[h]Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan,[i]Tibetan NyingmapaKathok Buddhist Organization, Sichuan, China,[j]Department of Neurology, Kaohsiung Medical University, Kaohsiung, Taiwan.
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.
语种：
外文
PubmedID：
中科院(CAS)分区：
出版当年[2017]版：
大类|3 区医学
小类|3 区医学：内科
最新[2023]版：
大类|4 区医学
小类|4 区医学：内科
第一作者：
第一作者机构：[a]Department of Electrical Engineering, National Cheng Kung University, Tainan,
共同第一作者：
通讯作者：
通讯机构：[b]Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan[h]Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan,[*1]Kaohsiung Medical University, Kaohsiung 807, Taiwan
推荐引用方式(GB/T 7714)：
Yu-Hao Lee,Ya-Ju Hsieh,Yung-Jong Shiah,et al.A cross-sectional evaluation of meditation experience on electroencephalography data by artificial neural network and support vector machine classifiers.[J].Medicine.2017,96(16):e6612.doi:10.1097/MD.0000000000006612.
APA：
Yu-Hao Lee,Ya-Ju Hsieh,Yung-Jong Shiah,Yu-Huei Lin,Chiao-Yun Chen...&Sharon Chia-Ju Chen.(2017).A cross-sectional evaluation of meditation experience on electroencephalography data by artificial neural network and support vector machine classifiers..Medicine,96,(16)
MLA：
Yu-Hao Lee,et al."A cross-sectional evaluation of meditation experience on electroencephalography data by artificial neural network and support vector machine classifiers.".Medicine 96..16(2017):e6612
