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Abnormalities and alterations in brain connectivity networks as measured using neuroimaging data has been increasingly used as biomarkers for various neuropsychiatric disorders. Schizophrenia (SCZ) is a complex neuropsychiatric disorder associated with dysconnectivity in brain networks. In this paper, we develop a framework for automatic classification of healthy control and SCZ patient based on electroencephalogram (EEG) connectivity and compare the classification performance with conventional artificial neural network (ANN). We propose to use convolutional neural network (CNN) for the classification of brain functional connectivity between healthy control and SCZ groups. Vector autoregression (VAR) model is used to extract connectivity features from schizophrenia EEG signals and directed connectivity at different EEG frequency bands is computed via partial directed coherence (PDC). Results show that the classification with high accuracy is achievable using VAR model. From the result, the performance of CNN reaches 86.9% over five-fold cross validation that considered to be good accuracy for the CNN to do a good prediction. The results also show that time-domain VAR features performed better than frequency domain PDC features. CNN provides a more practical method in classification between healthy and schizophrenic brain connectivity.

The application of human identification and verification has widely been used for over the past few decades.  Drawbacks of such system however, are inevitable as forgery sophisticatedly developed alongside the technology advancement.  Thus, this study proposed a research on the possibility of using heart sound as biometric. The main aim is to find an optimal auscultation point of heart sounds from either aortic, pulmonic, tricuspid or mitral that will most suitable to be used as the sound pattern for personal identification.  In this study, the heart sound was recorded from 92 participants using a Welch Allyn Meditron electronic stethoscope whereas Meditron Analyzer software was used to capture the signal of heart sounds and ECG simultaneously for duration of 1 minute.  The system is developed by a combination Mel Frequency Cepstrum Coefficients (MFCC) and Hidden Markov Model (HMM).  The highest recognition rate is obtained at aortic area with 98.7% when HMM has 1 state and 32 mixtures, the lowest Equal Error Rate (EER) achieved was 0.9% which is also at aortic area.  In contrast, the best average performance of HMM for every location is obtained at mitral area with 99.1% accuracy and 17.7% accuracy of EER at tricuspid area.

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