Hariharan Muthusamy

Technologies for Assessment of Motor Disorders in Parkinson’s Disease: A Review

Parkinson’s Disease (PD) is characterized as the commonest neurodegenerative illness that gradually degenerates the central nervous system. The goal of this review is to come out with a summary of the recent progress of numerous forms of sensors and systems that are related to diagnosis of PD in the past decades. The paper reviews the substantial researches on the application of technological tools (objective techniques) in the PD field applying different types of sensors proposed by previous researchers. In addition, this also includes the use of clinical tools (subjective techniques) for PD assessments, for instance, patient self-reports, patient diaries and the international gold standard reference scale, Unified Parkinson Disease Rating Scale (UPDRS). Comparative studies and critical descriptions of these approaches have been highlighted in this paper, giving an insight on the current state of the art. It is followed by explaining the merits of the multiple sensor fusion platform compared to single sensor platform for better monitoring progression of PD, and ends with thoughts about the future direction towards the need of multimodal sensor integration platform for the assessment of PD.

Improved Emotion Recognition Using Gaussian Mixture Model and Extreme Learning Machine in Speech and Glottal Signals

Recently, researchers have paid escalating attention to studying the emotional state of an individual from his/her speech signals as the speech signal is the fastest and the most natural method of communication between individuals. In this work, new feature enhancement using Gaussian mixture model (GMM) was proposed to enhance the discriminatory power of the features extracted from speech and glottal signals. Three different emotional speech databases were utilized to gauge the proposed methods. Extreme learning machine (ELM) and -nearest neighbor (NN) classifier were employed to classify the different types of emotions. Several experiments were conducted and results show that the proposed methods significantly improved the speech emotion recognition performance compared to research works published in the literature.

Particle swarm optimization based feature enhancement and feature selection for improved emotion recognition in speech and glottal signals.

In the recent years, many research works have been published using speech related features for speech emotion recognition, however, recent studies show that there is a strong correlation between emotional states and glottal features. In this work, Mel-frequency cepstralcoefficients (MFCCs), linear predictive cepstral coefficients (LPCCs), perceptual linear predictive (PLP) features, gammatone filter outputs, timbral texture features, stationary wavelet transform based timbral texture features and relative wavelet packet energy and entropy features were extracted from the emotional speech (ES) signals and its glottal waveforms(GW). Particle swarm optimization based clustering (PSOC) and wrapper based particle swarm optimization (WPSO) were proposed to enhance the discerning ability of the features and to select the discriminating features respectively. Three different emotional speech databases were utilized to gauge the proposed method. Extreme learning machine (ELM) was employed to classify the different types of emotions. Different experiments were conducted and the results show that the proposed method significantly improves the speech emotion recognition performance compared to previous works published in the literature.

A novel clinical decision support system using improved adaptive genetic algorithm for the assessment of fetal well-being.

A novel clinical decision support system is proposed in this paper for evaluating the fetal well-being from the cardiotocogram (CTG) dataset through an Improved Adaptive Genetic Algorithm (IAGA) and Extreme Learning Machine (ELM). IAGA employs a new scaling technique (called sigma scaling) to avoid premature convergence and applies adaptive crossover and mutation techniques with masking concepts to enhance population diversity. Also, this search algorithm utilizes three different fitness functions (two single objective fitness functions and multi-objective fitness function) to assess its performance. The classification results unfold that promising classification accuracy of 94% is obtained with an optimal feature subset using IAGA. Also, the classification results are compared with those of other Feature Reduction techniques to substantiate its exhaustive search towards the global optimum. Besides, five other benchmark datasets are used to gauge the strength of the proposed IAGA algorithm.

Empirical Wavelet Transform Based Features for Classification of Parkinson’s Disease Severity

Parkinson’s disease (PD) is a type of progressive neurodegenerative disorder that has affected a large part of the population till now. Several symptoms of PD include tremor, rigidity, slowness of movements and vocal impairments. In order to develop an effective diagnostic system, a number of algorithms were proposed mainly to distinguish healthy individuals from the ones with PD. However, most of the previous works were conducted based on a binary classification, with the early PD stage and the advanced ones being treated equally. Therefore, in this work, we propose a multiclass classification with three classes of PD severity level (mild, moderate, severe) and healthy control. The focus is to detect and classify PD using signals from wearable motion and audio sensors based on both empirical wavelet transform (EWT) and empirical wavelet packet transform (EWPT) respectively. The EWT/EWPT was applied to decompose both speech and motion data signals up to five levels. Next, several features are extracted after obtaining the instantaneous amplitudes and frequencies from the coefficients of the decomposed signals by applying the Hilbert transform. The performance of the algorithm was analysed using three classifiers – K-nearest neighbour (KNN), probabilistic neural network (PNN) and extreme learning machine (ELM). Experimental results demonstrated that our proposed approach had the ability to differentiate PD from non-PD subjects, including their severity level – with classification accuracies of more than 90% using EWT/EWPT-ELM based on signals from motion and audio sensors respectively. Additionally, classification accuracy of more than 95% was achieved when EWT/EWPT-ELM is applied to signals from integration of both signal’s information.

Dual tree complex Wavelet Packet Transform based infant cry classification

A new method has been implemented based on Dual Tree Complex Wavelet Packet Transform (DT-CWPT) feature extraction for infant cry signal classification. The infant cry signals were decomposed into five levels using DT-CWPT. A total of 124 energy features and 124 Shannon entropy features were extracted from each sub-band. Two classifiers Extreme Learning Machine (ELM) and Support Vector Machine (SVM) were used to classify the infant cry signal based on the extracted features. Three category of two-class experiments were conducted in this paper (asphyxia versus normal, hunger versus pain, and deaf versus normal). The results demonstrate that the DT-CWPT feature extraction and classification methods give a high accuracy of 97.87%, 87.26%, 100.00% for asphyxia versus normal, hunger versus pain, and deaf versus normal respectively.

Analysis of the residual between the model and the data using autocorrelation function for satellite attitude estimation

Objective of this paper is to investigate whether the noise in attitude dynamics model and measurement model is Gaussian white noise process or not. This is important because if the assumption regarding the noise is wrong, this will lead to unreliable and inaccurate estimation. The residual between the standard model and the real data is computed and is analyzed using autocorrelation function via Minitab software. The result shows that the error in the attitude dynamics model is not Gaussian white noise, while the error in measurement model is Gaussian white noise.

Optimal Selection of Long Time Acoustic Features Using GA for the Assessment of Vocal Fold Disorders

In recent times, vocal fold problems have been increasing dramatically due to unhealthy social habits and voice abuse. Non-invasive methods like acoustic analysis of voice signals can be used to investigate such problems. Various feature extraction techniques are used to classify the voice signals into normal and pathological. Among them, long-time acoustical parameters are used by many researchers. The selection of best long-time acoustical parameters is very important to reduce the computational complexity, as well as to achieve better accuracy with minimum number of features. In order to select best long-time acoustical parameters, different feature reduction methods or feature selection methods are proposed by researchers. In this work, genetic algorithm (GA) based optimal selection of long-time acoustical parameters is proposed to achieve higher accuracy with minimum number of features. The classification is carried out using k-nearest neighbourhood (k-NN) classifier. In comparison with other works in the literature, the simulation results show that a minimum of 5 features are required to classify the voice signals by GA and a better accuracy of 94.29% is achieved.

Unmodeled disturbances torque exerted on RazakSAT’s attitude during sun tracking mode

In space, environmental torques exerts continuously on a satellite and may influence and affect the satellite’s attitude dynamics. The relative strength of the various torques will depend on both spacecraft environment and the structure of the spacecraft itself. RazakSAT is the world first remote sensing satellite launched into Near Equatorial Orbit (NEqO). Hence, the objective of this paper is to study the properties and behaviour of disturbance torque that perturb RazakSAT’s attitude during orbiting in NEqO in sun tracking mode using in-flight attitude data. The results show that the environmental torques exerted on RazakSAT during sun tracking mode is periodic and the magnitude of the torques vary from 10−4 to 10−7. Understanding the behaviour of disturbance torque can be utilized in spacecraft design to improve the satellite’s performance for future mission.

Updated model of RazakSAT’s attitude during sun tracking mode using time series

The accuracy of control and estimation tasks can strongly depend on the accuracy of the underlying model. In space, there are many sources that contribute to the uncertainty in the dynamics model of satellite attitude. Hence, the aim of this paper is to update the dynamical attitude model using grey modeling technique. In this paper, the residual error between the nominal dynamics model and in-flight attitude data is modeled using time series data analysis. Then the time series model of the residual error is augmented in the nominal dynamics model. The updated model is simulated and its performance is analyzed. The results show that the updated model is adequate describing the data.