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VEP optimal channel selection using genetic algorithm for neural network classification of alcoholics

Palaniappan R, Raveendran P, Omatu S

IEEE Trans Neural Netw, 2002;13(2):486-91.
PMID: 18244450 DOI: 10.1109/72.991435

In this letter, neural networks (NNs) classify alcoholics and nonalcoholics using features extracted from visual evoked potential (VEP). A genetic algorithm (GA) is used to select the minimum number of channels that maximize classification performance. GA population fitness is evaluated using fuzzy ARTMAP (FA) NN, instead of the widely used multilayer perceptron (MLP). MLP, despite its effective classification, requires long training time (on the order of 10(3) times compared to FA). This causes it to be unsuitable to be used with GA, especially for on-line training. It is shown empirically that the optimal channel configuration selected by the proposed method is unbiased, i.e., it is optimal not only for FA but also for MLP classification. Therefore, it is proposed that for future experiments, these optimal channels could be considered for applications that involve classification of alcoholics.
Neural network classification of autoregressive features from electroencephalogram signals for brain-computer interface design

Huan NJ, Palaniappan R

J Neural Eng, 2004 Sep;1(3):142-50.
PMID: 15876633

In this paper, we have designed a two-state brain-computer interface (BCI) using neural network (NN) classification of autoregressive (AR) features from electroencephalogram (EEG) signals extracted during mental tasks. The main purpose of the study is to use Keirn and Aunon's data to investigate the performance of different mental task combinations and different AR features for BCI design for individual subjects. In the experimental study, EEG signals from five mental tasks were recorded from four subjects. Different combinations of two mental tasks were studied for each subject. Six different feature extraction methods were used to extract the features from the EEG signals: AR coefficients computed with Burg's algorithm, AR coefficients computed with a least-squares (LS) algorithm and adaptive autoregressive (AAR) coefficients computed with a least-mean-square (LMS) algorithm. All the methods used order six applied to 125 data points and these three methods were repeated with the same data but with segmentation into five segments in increments of 25 data points. The multilayer perceptron NN trained by the back-propagation algorithm (MLP-BP) and linear discriminant analysis (LDA) were used to classify the computed features into different categories that represent the mental tasks. We compared the classification performances among the six different feature extraction methods. The results showed that sixth-order AR coefficients with the LS algorithm without segmentation gave the best performance (93.10%) using MLP-BP and (97.00%) using LDA. The results also showed that the segmentation and AAR methods are not suitable for this set of EEG signals. We conclude that, for different subjects, the best mental task combinations are different and proper selection of mental tasks and feature extraction methods are essential for the BCI design.
Fulltext A comparative study of the SVM and K-nn machine learning algorithms for the diagnosis of respiratory pathologies using pulmonary acoustic signals

Palaniappan R, Sundaraj K, Sundaraj S

BMC Bioinformatics, 2014;15:223.
PMID: 24970564 DOI: 10.1186/1471-2105-15-223

Pulmonary acoustic parameters extracted from recorded respiratory sounds provide valuable information for the detection of respiratory pathologies. The automated analysis of pulmonary acoustic signals can serve as a differential diagnosis tool for medical professionals, a learning tool for medical students, and a self-management tool for patients. In this context, we intend to evaluate and compare the performance of the support vector machine (SVM) and K-nearest neighbour (K-nn) classifiers in diagnosis respiratory pathologies using respiratory sounds from R.A.L.E database.
Classification of mental tasks using fixed and adaptive autoregressive models of EEG signals

Nai-Jen H, Palaniappan R

Conf Proc IEEE Eng Med Biol Soc, 2007 2 3;2006:507-10.
PMID: 17271724

Classification of EEG signals extracted during mental tasks is a technique for designing brain computer interfaces (BCI). We classify EEG signals that were extracted during mental tasks using fixed autoregressive (FAR) and adaptive AR (AAR) models. Five different mental tasks from 4 subjects were used in the experimental study and combinations of 2 different mental tasks are studied for each subject. Four different feature extraction methods were used to extract features from these EEG signals: FAR coefficients computed with Burg's algorithm using 125 data points, without segmentation and with segmentation of 25 data points, AAR coefficients computed with least-mean-square (LMS) algorithm using 125 data points, without segmentation and with segmentation of 25 data points. Multilayer perception (MLP) neural network (NN) trained by the backpropagation (BP) algorithm is used to classify these features into the different categories representing the mental tasks. The best results for FAR was 92.70% while for AAR was only 81.80%. The results obtained here indicated that FAR using 125 data points without segmentation gave better classification performance as compared to AAR, with all other parameters constant.
Adaptive neuro-fuzzy inference system for breath phase detection and breath cycle segmentation

Palaniappan R, Sundaraj K, Sundaraj S

Comput Methods Programs Biomed, 2017 Jul;145:67-72.
PMID: 28552127 DOI: 10.1016/j.cmpb.2017.04.013

BACKGROUND: The monitoring of the respiratory rate is vital in several medical conditions, including sleep apnea because patients with sleep apnea exhibit an irregular respiratory rate compared with controls. Therefore, monitoring the respiratory rate by detecting the different breath phases is crucial.
OBJECTIVES: This study aimed to segment the breath cycles from pulmonary acoustic signals using the newly developed adaptive neuro-fuzzy inference system (ANFIS) based on breath phase detection and to subsequently evaluate the performance of the system.
METHODS: The normalised averaged power spectral density for each segment was fuzzified, and a set of fuzzy rules was formulated. The ANFIS was developed to detect the breath phases and subsequently perform breath cycle segmentation. To evaluate the performance of the proposed method, the root mean square error (RMSE) and correlation coefficient values were calculated and analysed, and the proposed method was then validated using data collected at KIMS Hospital and the RALE standard dataset.
RESULTS: The analysis of the correlation coefficient of the neuro-fuzzy model, which was performed to evaluate its performance, revealed a correlation strength of r = 0.9925, and the RMSE for the neuro-fuzzy model was found to equal 0.0069.
CONCLUSION: The proposed neuro-fuzzy model performs better than the fuzzy inference system (FIS) in detecting the breath phases and segmenting the breath cycles and requires less rules than FIS.
On the binaural brain entrainment indicating lower heart rate variability

Palaniappan R, Phon-Amnuaisuk S, Eswaran C

Int J Cardiol, 2015;190:262-3.
PMID: 25932800 DOI: 10.1016/j.ijcard.2015.04.175
A new brain-computer interface design using fuzzy ARTMAP

Palaniappan R, Paramesran R, Nishida S, Saiwaki N

IEEE Trans Neural Syst Rehabil Eng, 2002 Sep;10(3):140-8.
PMID: 12503778

This paper proposes a new brain-computer interface (BCI) design using fuzzy ARTMAP (FA) neural network, as well as an application of the design. The objective of this BCI-FA design is to classify the best three of the five available mental tasks for each subject using power spectral density (PSD) values of electroencephalogram (EEG) signals. These PSD values are extracted using the Wiener-Khinchine and autoregressive methods. Ten experiments employing different triplets of mental tasks are studied for each subject. The findings show that the average BCI-FA outputs for four subjects gave less than 6% of error using the best triplets of mental tasks identified from the classification performances of FA. This implies that the BCI-FA can be successfully used with a tri-state switching device. As an application, a proposed tri-state Morse code scheme could be utilized to translate the outputs of this BCI-FA design into English letters. In this scheme, the three BCI-FA outputs correspond to a dot and a dash, which are the two basic Morse code alphabets and a space to denote the end (or beginning) of a dot or a dash. The construction of English letters using this tri-state Morse code scheme is determined only by the sequence of mental tasks and is independent of the time duration of each mental task. This is especially useful for constructing letters that are represented as multiple dots or dashes. This combination of BCI-FA design and the tri-state Morse code scheme could be developed as a communication system for paralyzed patients.
A survey on automated wheeze detection systems for asthmatic patients

Shaharum SM, Sundaraj K, Palaniappan R

Bosn J Basic Med Sci, 2012 Nov;12(4):249-55.
PMID: 23198941

The purpose of this paper is to present an evidence of automated wheeze detection system by a survey that can be very beneficial for asthmatic patients. Generally, for detecting asthma in a patient, stethoscope is used for ascertaining wheezes present. This causes a major problem nowadays because a number of patients tend to delay the interpretation time, which can lead to misinterpretations and in some worst cases to death. Therefore, the development of automated system would ease the burden of medical personnel. A further discussion on automated wheezes detection system will be presented later in the paper. As for the methodology, a systematic search of articles published as early as 1985 to 2012 was conducted. Important details including the hardware used, placement of hardware, and signal processing methods have been presented clearly thus hope to help and encourage future researchers to develop commercial system that will improve the diagnosing and monitoring of asthmatic patients.
A novel approach to detect respiratory phases from pulmonary acoustic signals using normalised power spectral density and fuzzy inference system

Palaniappan R, Sundaraj K, Sundaraj S, Huliraj N, Revadi SS

Clin Respir J, 2016 Jul;10(4):486-94.
PMID: 25515741 DOI: 10.1111/crj.12250

BACKGROUND: Monitoring respiration is important in several medical applications. One such application is respiratory rate monitoring in patients with sleep apnoea. The respiratory rate in patients with sleep apnoea disorder is irregular compared with the controls. Respiratory phase detection is required for a proper monitoring of respiration in patients with sleep apnoea.
AIMS: To develop a model to detect the respiratory phases present in the pulmonary acoustic signals and to evaluate the performance of the model in detecting the respiratory phases.
METHODS: Normalised averaged power spectral density for each frame and change in normalised averaged power spectral density between the adjacent frames were fuzzified and fuzzy rules were formulated. The fuzzy inference system (FIS) was developed with both Mamdani and Sugeno methods. To evaluate the performance of both Mamdani and Sugeno methods, correlation coefficient and root mean square error (RMSE) were calculated.
RESULTS: In the correlation coefficient analysis in evaluating the fuzzy model using Mamdani and Sugeno method, the strength of the correlation was found to be r = 0.9892 and r = 0.9964, respectively. The RMSE for Mamdani and Sugeno methods are RMSE = 0.0853 and RMSE = 0.0817, respectively.
CONCLUSION: The correlation coefficient and the RMSE of the proposed fuzzy models in detecting the respiratory phases reveals that Sugeno method performs better compared with the Mamdani method.
Characterization and classification of asthmatic wheeze sounds according to severity level using spectral integrated features

Nabi FG, Sundaraj K, Lam CK, Palaniappan R

Comput Biol Med, 2019 01;104:52-61.
PMID: 30439599 DOI: 10.1016/j.compbiomed.2018.10.035

OBJECTIVE: This study aimed to investigate and classify wheeze sounds of asthmatic patients according to their severity level (mild, moderate and severe) using spectral integrated (SI) features.
METHOD: Segmented and validated wheeze sounds were obtained from auscultation recordings of the trachea and lower lung base of 55 asthmatic patients during tidal breathing manoeuvres. The segments were multi-labelled into 9 groups based on the auscultation location and/or breath phases. Bandwidths were selected based on the physiology, and a corresponding SI feature was computed for each segment. Univariate and multivariate statistical analyses were then performed to investigate the discriminatory behaviour of the features with respect to the severity levels in the various groups. The asthmatic severity levels in the groups were then classified using the ensemble (ENS), support vector machine (SVM) and k-nearest neighbour (KNN) methods.
RESULTS AND CONCLUSION: All statistical comparisons exhibited a significant difference (p 
Analysis of wheeze sounds during tidal breathing according to severity levels in asthma patients

Nabi FG, Sundaraj K, Lam CK, Palaniappan R

J Asthma, 2020 04;57(4):353-365.
PMID: 30810448 DOI: 10.1080/02770903.2019.1576193

Objective: This study aimed to statistically analyze the behavior of time-frequency features in digital recordings of wheeze sounds obtained from patients with various levels of asthma severity (mild, moderate, and severe), and this analysis was based on the auscultation location and/or breath phase. Method: Segmented and validated wheeze sounds were collected from the trachea and lower lung base (LLB) of 55 asthmatic patients during tidal breathing maneuvers and grouped into nine different datasets. The quartile frequencies F25, F50, F75, F90 and F99, mean frequency (MF) and average power (AP) were computed as features, and a univariate statistical analysis was then performed to analyze the behavior of the time-frequency features. Results: All features generally showed statistical significance in most of the datasets for all severity levels [χ2 = 6.021-71.65, p
Optimal set of EEG features for emotional state classification and trajectory visualization in Parkinson's disease

Yuvaraj R, Murugappan M, Ibrahim NM, Sundaraj K, Omar MI, Mohamad K, et al.

Int J Psychophysiol, 2014 Dec;94(3):482-95.
PMID: 25109433 DOI: 10.1016/j.ijpsycho.2014.07.014

In addition to classic motor signs and symptoms, individuals with Parkinson's disease (PD) are characterized by emotional deficits. Ongoing brain activity can be recorded by electroencephalograph (EEG) to discover the links between emotional states and brain activity. This study utilized machine-learning algorithms to categorize emotional states in PD patients compared with healthy controls (HC) using EEG. Twenty non-demented PD patients and 20 healthy age-, gender-, and education level-matched controls viewed happiness, sadness, fear, anger, surprise, and disgust emotional stimuli while fourteen-channel EEG was being recorded. Multimodal stimulus (combination of audio and visual) was used to evoke the emotions. To classify the EEG-based emotional states and visualize the changes of emotional states over time, this paper compares four kinds of EEG features for emotional state classification and proposes an approach to track the trajectory of emotion changes with manifold learning. From the experimental results using our EEG data set, we found that (a) bispectrum feature is superior to other three kinds of features, namely power spectrum, wavelet packet and nonlinear dynamical analysis; (b) higher frequency bands (alpha, beta and gamma) play a more important role in emotion activities than lower frequency bands (delta and theta) in both groups and; (c) the trajectory of emotion changes can be visualized by reducing subject-independent features with manifold learning. This provides a promising way of implementing visualization of patient's emotional state in real time and leads to a practical system for noninvasive assessment of the emotional impairments associated with neurological disorders.
Inter-hemispheric EEG coherence analysis in Parkinson's disease: assessing brain activity during emotion processing

Yuvaraj R, Murugappan M, Ibrahim NM, Sundaraj K, Omar MI, Mohamad K, et al.

J Neural Transm (Vienna), 2015 Feb;122(2):237-52.
PMID: 24894699 DOI: 10.1007/s00702-014-1249-4

Parkinson's disease (PD) is not only characterized by its prominent motor symptoms but also associated with disturbances in cognitive and emotional functioning. The objective of the present study was to investigate the influence of emotion processing on inter-hemispheric electroencephalography (EEG) coherence in PD. Multimodal emotional stimuli (happiness, sadness, fear, anger, surprise, and disgust) were presented to 20 PD patients and 30 age-, education level-, and gender-matched healthy controls (HC) while EEG was recorded. Inter-hemispheric coherence was computed from seven homologous EEG electrode pairs (AF3-AF4, F7-F8, F3-F4, FC5-FC6, T7-T8, P7-P8, and O1-O2) for delta, theta, alpha, beta, and gamma frequency bands. In addition, subjective ratings were obtained for a representative of emotional stimuli. Interhemispherically, PD patients showed significantly lower coherence in theta, alpha, beta, and gamma frequency bands than HC during emotion processing. No significant changes were found in the delta frequency band coherence. We also found that PD patients were more impaired in recognizing negative emotions (sadness, fear, anger, and disgust) than relatively positive emotions (happiness and surprise). Behaviorally, PD patients did not show impairment in emotion recognition as measured by subjective ratings. These findings suggest that PD patients may have an impairment of inter-hemispheric functional connectivity (i.e., a decline in cortical connectivity) during emotion processing. This study may increase the awareness of EEG emotional response studies in clinical practice to uncover potential neurophysiologic abnormalities.
Emotion classification in Parkinson's disease by higher-order spectra and power spectrum features using EEG signals: a comparative study

Yuvaraj R, Murugappan M, Ibrahim NM, Omar MI, Sundaraj K, Mohamad K, et al.

J Integr Neurosci, 2014 Mar;13(1):89-120.
PMID: 24738541 DOI: 10.1142/S021963521450006X

Deficits in the ability to process emotions characterize several neuropsychiatric disorders and are traits of Parkinson's disease (PD), and there is need for a method of quantifying emotion, which is currently performed by clinical diagnosis. Electroencephalogram (EEG) signals, being an activity of central nervous system (CNS), can reflect the underlying true emotional state of a person. This study applied machine-learning algorithms to categorize EEG emotional states in PD patients that would classify six basic emotions (happiness and sadness, fear, anger, surprise and disgust) in comparison with healthy controls (HC). Emotional EEG data were recorded from 20 PD patients and 20 healthy age-, education level- and sex-matched controls using multimodal (audio-visual) stimuli. The use of nonlinear features motivated by the higher-order spectra (HOS) has been reported to be a promising approach to classify the emotional states. In this work, we made the comparative study of the performance of k-nearest neighbor (kNN) and support vector machine (SVM) classifiers using the features derived from HOS and from the power spectrum. Analysis of variance (ANOVA) showed that power spectrum and HOS based features were statistically significant among the six emotional states (p < 0.0001). Classification results shows that using the selected HOS based features instead of power spectrum based features provided comparatively better accuracy for all the six classes with an overall accuracy of 70.10% ± 2.83% and 77.29% ± 1.73% for PD patients and HC in beta (13-30 Hz) band using SVM classifier. Besides, PD patients achieved less accuracy in the processing of negative emotions (sadness, fear, anger and disgust) than in processing of positive emotions (happiness, surprise) compared with HC. These results demonstrate the effectiveness of applying machine learning techniques to the classification of emotional states in PD patients in a user independent manner using EEG signals. The accuracy of the system can be improved by investigating the other HOS based features. This study might lead to a practical system for noninvasive assessment of the emotional impairments associated with neurological disorders.
Fulltext On the analysis of EEG power, frequency and asymmetry in Parkinson's disease during emotion processing

Yuvaraj R, Murugappan M, Mohamed Ibrahim N, Iqbal M, Sundaraj K, Mohamad K, et al.

Behav Brain Funct, 2014;10:12.
PMID: 24716619 DOI: 10.1186/1744-9081-10-12

While Parkinson's disease (PD) has traditionally been described as a movement disorder, there is growing evidence of disruption in emotion information processing associated with the disease. The aim of this study was to investigate whether there are specific electroencephalographic (EEG) characteristics that discriminate PD patients and normal controls during emotion information processing.
Hybrid markerless tracking of complex articulated motion in golf swings

Fung SK, Sundaraj K, Ahamed NU, Kiang LC, Nadarajah S, Sahayadhas A, et al.

J Bodyw Mov Ther, 2014 Apr;18(2):220-7.
PMID: 24725790 DOI: 10.1016/j.jbmt.2013.05.011

Sports video tracking is a research topic that has attained increasing attention due to its high commercial potential. A number of sports, including tennis, soccer, gymnastics, running, golf, badminton and cricket have been utilised to display the novel ideas in sports motion tracking. The main challenge associated with this research concerns the extraction of a highly complex articulated motion from a video scene. Our research focuses on the development of a markerless human motion tracking system that tracks the major body parts of an athlete straight from a sports broadcast video. We proposed a hybrid tracking method, which consists of a combination of three algorithms (pyramidal Lucas-Kanade optical flow (LK), normalised correlation-based template matching and background subtraction), to track the golfer's head, body, hands, shoulders, knees and feet during a full swing. We then match, track and map the results onto a 2D articulated human stick model to represent the pose of the golfer over time. Our work was tested using two video broadcasts of a golfer, and we obtained satisfactory results. The current outcomes of this research can play an important role in enhancing the performance of a golfer, provide vital information to sports medicine practitioners by providing technically sound guidance on movements and should assist to diminish the risk of golfing injuries.

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