Dr. Chusak Thanawattano

University: National Electronics and Technology Center
Department: Biosignal processing Lab.
Research Fields: Biosignal processing, signal processing, embedded system

Publications

1 Comparative Study of Filter Characteristics as Statistical Vocal Correlates of Clinical Psychiatric State in Human

Authors: Thaweesak Yingthawornsuk, Chusak Thanawattano

Abstract:

Acoustical properties of speech have been shown to be related to mental states of speaker with symptoms: depression and remission. This paper describes way to address the issue of distinguishing depressed patients from remitted subjects based on measureable acoustics change of their spoken sound. The vocal-tract related frequency characteristics of speech samples from female remitted and depressed patients were analyzed via speech processing techniques and consequently, evaluated statistically by cross-validation with Support Vector Machine. Our results comparatively show the classifier's performance with effectively correct separation of 93% determined from testing with the subjectbased feature model and 88% from the frame-based model based on the same speech samples collected from hospital visiting interview sessions between patients and psychiatrists.

Keywords: Depression, SVM, Vocal tract, Vocal Extract

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Abstracts

1 The Classification of Parkinson Tremor and Essential Tremor Based on Frequency Alteration of Different Activities

Authors: Chusak Thanawattano, Roongroj Bhidayasiri

Abstract:

This paper proposes a novel feature set utilized for classifying the Parkinson tremor and essential tremor. Ten ET and ten PD subjects are asked to perform kinetic, postural and resting tests. The empirical mode decomposition (EMD) is used to decompose collected tremor signal to a set of intrinsic mode functions (IMF). The IMFs are used for reconstructing representative signals. The feature set is composed of peak frequencies of IMFs and reconstructed signals. Hypothesize that the dominant frequency components of subjects with PD and ET change in different directions for different tests, difference of peak frequencies of IMFs and reconstructed signals of pairwise based tests (kinetic-resting, kinetic-postural and postural-resting) are considered as potential features. Sets of features are used to train and test by classifier including the quadratic discriminant classifier (QLC) and the support vector machine (SVM). The best accuracy, the best sensitivity and the best specificity are 90%, 87.5%, and 92.86%, respectively.

Keywords: Parkinson, Tremor, Spectrum Estimation, support vector machine, essential tremor, empirical mode decomposition, quadratic discriminant, peak frequency, auto-regressive

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