amsys 16(11): e1

Research Article

Diagnosing Bipolar Disorders in a Wearable Device

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  • @ARTICLE{10.4108/eai.28-9-2015.2261428,
        author={Chao Gui and Jie Zhu},
        title={Diagnosing Bipolar Disorders in a Wearable Device},
        journal={EAI Endorsed Transactions on Ambient Systems},
        volume={3},
        number={11},
        publisher={ACM},
        journal_a={AMSYS},
        year={2015},
        month={12},
        keywords={bipolar disorder, support vector machine (svm), gaussian mixture model (gmm), wearable device},
        doi={10.4108/eai.28-9-2015.2261428}
    }
    
  • Chao Gui
    Jie Zhu
    Year: 2015
    Diagnosing Bipolar Disorders in a Wearable Device
    AMSYS
    EAI
    DOI: 10.4108/eai.28-9-2015.2261428
Chao Gui1, Jie Zhu1,*
  • 1: Shanghai Jiao Tong University
*Contact email: zhujie@sjtu.edu.cn

Abstract

Bipolar disorder is a common chronic recurrent psychosis and it mainly relies on doctors’ experience to determine the patient’s condition currently. We aimed to find a useful methodology to diagnose the mental state and guide medical treatment by using speech signal processing. Methods: Firstly, the feature classes were extracted (e.g., Pitch, Formant, MFCC, GT). Secondly, class separability criterion based on distance (the Between-class Variance and Within-class Variance) was adopted as an evaluation criteria to get the features assessment, and then, we found LPC played a core role on the all features. According to the experiment, the SVM have a good performance for the single patient up to 90%, and the GMM classifier yields the best performance with a classification rate of 72% for multi patients. The newly proposed methodology provide a good method for helping diagnose bipolar disorder.