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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2018.1
- 수록면
- 695 - 700 (6page)
이용수
초록· 키워드
오류제보하기
Objective This study was aimed to compare the accuracy of Support Vector Machine (SVM) and Gaussian Mixture Model (GMM) in the detection of manic state of bipolar disorders (BD) of single patients and multiple patients. Methods 21 hospitalized BD patients (14 females, average age 34.5±15.3) were recruited after admission. Spontaneous speech was collected through a preloaded smartphone. Firstly, speech features [pitch, formants, mel-frequency cepstrum coefficients (MFCC), linear prediction cepstral coefficient (LPCC), gamma-tone frequency cepstral coefficients (GFCC) etc.] were preprocessed and extracted. Then, speech features were selected using the features of between-class variance and within-class variance. The manic state of patients was then detected by SVM and GMM methods. Results LPCC demonstrated the best discrimination efficiency. The accuracy of manic state detection for single patients was much better using SVM method than GMM method. The detection accuracy for multiple patients was higher using GMM method than SVM method. Conclusion SVM provided an appropriate tool for detecting manic state for single patients, whereas GMM worked better for multiple patients’ manic state detection. Both of them could help doctors and patients for better diagnosis and mood state monitoring in different situations.
목차
등록된 정보가 없습니다.
참고문헌 (31)
참고문헌 신청
Schmitt A / 2014 / The impact of environmental factors in severe psychiatric disorders / Front Neurosci 8:19
Boland EM / 2013 / Sleep disturbance and cognitive deficits in bipolar disorder: toward an integrated examination of disorder maintenance and functional impairment / Clin Psychol Rev 33:33~44
Inoue T / 2015 / Prevalence and predictors of bipolar disorders in patients with a major depressive episode: the Japanese epidemiological trial with latest measure of bipolar disorder (JET-LMBP) / J Affect Disord 174:535~541
Park CK / 2011 / Autonomic function, voice, and mood states / Clin Auton Res 21:103~110
Cummins N / 2015 / A review of depression and suicide risk assessment using speech analysis / Speech Commun 71:10~49
Boland EM / 2013 / Sleep disturbance and cognitive deficits in bipolar disorder: toward an integrated examination of disorder maintenance and functional impairment / Clin Psychol Rev 33:33~44
Inoue T / 2015 / Prevalence and predictors of bipolar disorders in patients with a major depressive episode: the Japanese epidemiological trial with latest measure of bipolar disorder (JET-LMBP) / J Affect Disord 174:535~541
Park CK / 2011 / Autonomic function, voice, and mood states / Clin Auton Res 21:103~110
Cummins N / 2015 / A review of depression and suicide risk assessment using speech analysis / Speech Commun 71:10~49
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