Pan Zhongde, Gui Chao, Zhang Jing, Zhu Jie, Cui Donghong
Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Shanghai Key Laboratory of Forensic Medicine, Institute of Forensic Science, Ministry of Justice, Shanghai, China.
Psychiatry Investig. 2018 Jul;15(7):695-700. doi: 10.30773/pi.2017.12.15. Epub 2018 Jul 4.
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.
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.
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.
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.
本研究旨在比较支持向量机（SVM）和高斯混合模型（GMM）在检测单例和多例双相情感障碍（BD）患者躁狂状态时的准确性。
招募21例住院的BD患者（14例女性，平均年龄34.5±15.3岁），入院后通过预加载的智能手机收集自发语音。首先，对语音特征[音高、共振峰、梅尔频率倒谱系数（MFCC）、线性预测倒谱系数（LPCC）、伽马音调频率倒谱系数（GFCC）等]进行预处理和提取。然后，利用类间方差和类内方差特征选择语音特征。随后采用支持向量机和高斯混合模型方法检测患者的躁狂状态。
线性预测倒谱系数表现出最佳的判别效率。单例患者躁狂状态检测的准确率，支持向量机方法比高斯混合模型方法要好得多。多例患者的检测准确率，高斯混合模型方法比支持向量机方法更高。
支持向量机为检测单例患者的躁狂状态提供了合适的工具，而高斯混合模型在多例患者的躁狂状态检测中效果更好。两者都有助于医生和患者在不同情况下进行更好的诊断和情绪状态监测。
