I. Kamarulafizam

Heart Sound Analysis Using MFCC and Time Frequency Distribution

This paper presents heart sound analysis method based on Time-Frequency Distribution (TFD) analysis and Mel Frequency Cepstrum Coefficient (MFCC). TFD represents the heart sound in term of time and frequency simultaneously which while the MFCC defines a signal in term of frequency coefficient corresponding to the Mel filter scale. There are 100 normal data and 100 data with disease obtained from the hospital which consists of various kinds of problems including mitral regurgitation and stenosis, tricuspid regurgitation and stenosis, ventricular septal defect and other structural related disease. B-Distribution is chosen from a number of time-frequency analysis methods due its capability to represent the signal in the most efficient way in term of noise and cross term reduction. The advantage of MFCC is that it is good in error reduction and able to produce a robust feature when the signal is affected by noise. SVD/PCA technique is used to extract the important features out of the B-Distribution representation. The coefficient obtained from SVD-PCA and MFCC is later used for classification Artificial Neural Network. The results show that the system is able to produce the accuracy up to 90.0% using the TFD and 80.0% using the MFCC.

An expectation-maximization algorithm based Kalman smoother approach for single-trial estimation of event-related potentials

This paper applies an expectation-maximization (EM) based Kalman smoother (KS) approach for single-trial event-related potential (ERP) estimation. Existing studies assume a Markov diffusion process for the dynamics of ERP parameters which is recursively estimated by optimal filtering approaches such as Kalman filter (KF). However, these studies only consider estimation of ERP state parameters while the model parameters are pre-specified using manual tuning, which is time-consuming for practical usage besides giving suboptimal estimates. We extend the KF approach by adding EM based maximum likelihood estimation of the model parameters to obtain more accurate ERP estimates automatically. We also introduce different model variants by allowing flexibility in the covariance structure of model noises. Optimal model selection is performed based on Akaike Information Criterion (AIC). The method is applied to estimation of chirp-evoked auditory brainstem responses (ABRs) for detection of wave V critical for assessment of hearing loss. Results shows that use of more complex covariances are better estimating inter-trial variability.

Signal Processing Application for Telemedicine

This paper describes the architecture and implementation of telemedicine via Internet for heart sounds and hearing screening diagnosis. Web based application are used as a medium for interaction between patients and doctors. Using ActiveX technology and Internet protocol, the biomedical signals are captured and sent to server. To strengthen analyses of heart sounds, time-frequency method using B-distribution technique has been applied. Features of heart sounds were obtained using singular value decomposition. The appointed doctors then view the signals and analyze heart condition. Recommendation of further action can be made and sent to the users.

Speaker Verification Using Gaussian Mixture Model (GMM)

This paper applies GMM for SV on Malay speech. The speaker models were trained on maximum likelihood estimated. The system was evaluated with 23 client speakers with 56 imposters. Malay clean speech data was used. 20 training of 3.5s utterances are used. The best performance achieved using 256-Gaussian imposter model and 32-Gaussian client model gave 3.01% of EER.

Shrinkage estimation of high-dimensional vector autoregressions for effective connectivity in fMRI

We consider the challenge of estimating effective brain connectivity network with a large number of nodes from fMRI data. This involves estimation of a very-high dimensional vector autoregressive (VAR) models commonly used to identify directed brain networks. The conventional least-squares (LS) estimator is not longer consistent when applied on the high-dimensional fMRI data compared to sample size due to large number of fitted parameters, and thus produces unreliable estimates of the brain connectivity. In this paper, we propose an well-conditioned large-dimensional VAR estimator based on shrinkage approach, by incorporating a Ledoit-Wolf (LW) shrinkage-based estimator of the Gramian matrix in the LS-based linear regression fitting of VAR. This allows better-conditioned and invertible Gramian matrix estimate which is an important ingredient in generating a reliable LS estimator, when the data dimension is larger than the sample size. Simulation results show significant superiority of the proposed LW-shrinkage-VAR estimator over the conventional LS estimator under the high-dimensional settings. Application to real resting-state fMRI dataset shows the capability of the proposed method in identifying resting-state brain connectivity networks, with directionality of connections and interesting modular structure, which potentially provide useful insights to neuroscience studies of human brain connectome.