Detecting Manic State of Bipolar Disorder Based on Support Vector Machine and Gaussian Mixture Model Using Spontaneous Speech

Abstract

ObjectiveThis 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. Methods21 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. ResultsLPCC 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. ConclusionSVM 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.

DL4DED: Deep Learning for Depressive Episode Detection on Mobile Devices

“…Pan et al [13] propose analysis approaches for detecting manic episodes in BD. The proposed approaches are based on the use of Support Vector Machines (SVM) and Generalized Markov Models (GMM).…”

“…Several mental health monitoring approaches using mobile devices have been proposed. Most of them [1][2][3][5][6][7][8][9][10][11]13,14] are based on (1) collecting and analyzing smartphone features such as activity, localization, and phone calls, and (2) launching interactive questionnaires such as PHQ-9 3 and BDI 4 . "Active" monitoring approaches (i.e., requiring a patient's intervention) are less used and less effective than "passive" ones (i.e., not requiring a patient's intervention) in practice.…”

“…Passive monitoring approaches [3,[5][6][7][8]10,11,13,14] can be divided into two categories. The first category tries to use most of the smartphone's sensors, such as accelerometer and camera, to collect related features [5][6][7]11,14].…”

“…This makes these approaches difficult to use in practice. The second category of approaches [3,8,10,13] focuses on analyzing "only" voice sounds, recorded from phone calls. Indeed, acoustic changes in speech allow us to detect depressive episodes [8].…”

Development of an Emotion-Sensitive mHealth Approach for Mood-State Recognition in Bipolar Disorder

“…So far, mobile-based approaches have analyzed the keyboard activity of patients with bipolar disorder [ 22 ] or even ambient sound samples [ 31 , 32 , 50 ] or voice features during phone calls [ 23 , 51 ]. However, to our knowledge, none of the referenced approaches have analyzed the emotional content of audio data or social interactions.…”