TY - GEN
T1 - Study on Depression Classification Based on Electroencephalography Data Collected by Wearable Devices
AU - Cai, Hanshu
AU - Zhang, Yanhao
AU - Sha, Xiaocong
AU - Hu, Bin
N1 - Publisher Copyright:
© 2017, Springer International Publishing AG.
PY - 2017
Y1 - 2017
N2 - Depression has become a disease, which may threaten millions of families’ well-being. The current method of screening depression is subjective, labor-consuming and costly. Study on Electroencephalogram (EEG) has become a new direction to explore an objective, low-cost and accurate method to detect depression. In this paper, three-electrode EEG data of 158 subjects (90 depressed and 68 normal control) in resting state, and under audio stimulation (positive and negative) were collected and processed. After feature selection using Sequential Floating Forward Selection (SFFS), four popular classification methods were applied and classification accuracies were verified using 10-fold cross validation. Results have shown the accuracy of classification will be improved when male and female are classified separately. The highest accuracy of male and female classification are 91.98%, 79.76%, respectively, compare to 77.43% when the classification is processed as gender-free. The effective depressive features of male and female are also different, which may be caused by the differences of brain structure. This research suggests a possible pervasive method of depression classification for future clinical application.
AB - Depression has become a disease, which may threaten millions of families’ well-being. The current method of screening depression is subjective, labor-consuming and costly. Study on Electroencephalogram (EEG) has become a new direction to explore an objective, low-cost and accurate method to detect depression. In this paper, three-electrode EEG data of 158 subjects (90 depressed and 68 normal control) in resting state, and under audio stimulation (positive and negative) were collected and processed. After feature selection using Sequential Floating Forward Selection (SFFS), four popular classification methods were applied and classification accuracies were verified using 10-fold cross validation. Results have shown the accuracy of classification will be improved when male and female are classified separately. The highest accuracy of male and female classification are 91.98%, 79.76%, respectively, compare to 77.43% when the classification is processed as gender-free. The effective depressive features of male and female are also different, which may be caused by the differences of brain structure. This research suggests a possible pervasive method of depression classification for future clinical application.
KW - Depression
KW - EEG
KW - Health care
KW - Pervasive
UR - http://www.scopus.com/inward/record.url?scp=85034216471&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-70772-3_23
DO - 10.1007/978-3-319-70772-3_23
M3 - Conference contribution
AN - SCOPUS:85034216471
SN - 9783319707716
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 244
EP - 253
BT - Brain Informatics - International Conference, BI 2017, Proceedings
A2 - Zeng, Yi
A2 - Xu, Bo
A2 - Martone, Maryann
A2 - He, Yong
A2 - Peng, Hanchuan
A2 - Luo, Qingming
A2 - Kotaleski, Jeanette Hellgren
PB - Springer Verlag
T2 - International Conference on Brain Informatics, BI 2017
Y2 - 16 November 2017 through 18 November 2017
ER -