Showing papers by "Choo Min Lim published in 2013"

Application of higher order cumulant features for cardiac health diagnosis using ECG signals.

Abstract: Electrocardiogram (ECG) is the electrical activity of the heart indicated by P, Q-R-S and T wave. The minute changes in the amplitude and duration of ECG depicts a particular type of cardiac abnormality. It is very difficult to decipher the hidden information present in this nonlinear and nonstationary signal. An automatic diagnostic system that characterizes cardiac activities in ECG signals would provide more insight into these phenomena thereby revealing important clinical information. Various methods have been proposed to detect cardiac abnormalities in ECG recordings. Application of higher order spectra (HOS) features is a seemingly promising approach because it can capture the nonlinear and dynamic nature of the ECG signals. In this paper, we have automatically classified five types of beats using HOS features (higher order cumulants) using two different approaches. The five types of ECG beats are normal (N), right bundle branch block (RBBB), left bundle branch block (LBBB), atrial premature contraction (APC) and ventricular premature contraction (VPC). In the first approach, cumulant features of segmented ECG signal were used for classification; whereas in the second approach cumulants of discrete wavelet transform (DWT) coefficients were used as features for classifiers. In both approaches, the cumulant features were subjected to data reduction using principal component analysis (PCA) and classified using three layer feed-forward neural network (NN) and least square — support vector machine (LS-SVM) classifiers. In this study, we obtained the highest average accuracy of 94.52%, sensitivity of 98.61% and specificity of 98.41% using first approach with NN classifier. The developed system is ready clinically to run on large datasets.

Evolutionary algorithm based classifier parameter tuning for automatic diabetic retinopathy grading: A hybrid feature extraction approach

Abstract: Human eye is one of the most sophisticated organ, with retina, pupil, iris cornea, lens and optic nerve. Automatic retinal image analysis is emerging as an important screening tool for early detection of eye diseases. Uncontrolled diabetes retinopathy (DR) and glaucoma may lead to blindness. DR is caused by damage to the small blood vessels of the retina in the posterior part of the eye of the diabetic patient. The main stages of DR are non-proliferate diabetes retinopathy (NPDR) and proliferate diabetes retinopathy (PDR). The retinal fundus photographs are widely used in the diagnosis and treatment of various eye diseases in clinics. It is also one of the main resources used for mass screening of DR. We present an automatic screening system for the detection of normal and DR stages (NPDR and PDR). The proposed systems involves processing of fundus images for extraction of abnormal signs, such as area of hard exudates, area of blood vessels, bifurcation points, texture and entropies. Our protocol uses total of 156 subjects consisting of two stages of DR and normal. In this work, we have fed thirteen statistically significant (p<0.0001) features for Probabilistic Neural Network (PNN), Decision Tree (DT) C4.5, and Support Vector Machine (SVM) to select the best classifier. The best model parameter (@s) for which the PNN classifier performed best was identified using global optimization techniques such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). We demonstrated an average classification accuracy of 96.15%, sensitivity of 96.27% and specificity of 96.08% for @s=0.0104 using threefold cross validation using PNN classifier. The computer-aided diagnosis (CAD) results were validated by comparing with expert ophthalmologists. The proposed automated system can aid clinicians to make a faster DR diagnosis during the mass screening of normal/DR images.

Automated diagnosis of epilepsy using CWT, HOS and texture parameters.

Abstract: Epilepsy is a chronic brain disorder which manifests as recurrent seizures. Electroencephalogram (EEG) signals are generally analyzed to study the characteristics of epileptic seizures. In this work, we propose a method for the automated classification of EEG signals into normal, interictal and ictal classes using Continuous Wavelet Transform (CWT), Higher Order Spectra (HOS) and textures. First the CWT plot was obtained for the EEG signals and then the HOS and texture features were extracted from these plots. Then the statistically significant features were fed to four classifiers namely Decision Tree (DT), K-Nearest Neighbor (KNN), Probabilistic Neural Network (PNN) and Support Vector Machine (SVM) to select the best classifier. We observed that the SVM classifier with Radial Basis Function (RBF) kernel function yielded the best results with an average accuracy of 96%, average sensitivity of 96.9% and average specificity of 97% for 23.6 s duration of EEG data. Our proposed technique can be used as an automatic seizure monitoring software. It can also assist the doctors to cross check the efficacy of their prescribed drugs.

Characterization of ECG beats from cardiac arrhythmia using discrete cosine transform in PCA framework

Abstract: Electrocardiogram is the P-QRS-T wave representing the cardiac depolarization and re-polarization, recorded at the body surface. The subtle changes in amplitude and duration of these waves indicate various pathological conditions. It is very difficult to decipher minute changes in the ECG wave by naked eye. Hence a computer aided diagnosis tool to classify various cardiac diseases will assist the doctors in their ECG reading. In this paper, five types of ECG beats (ANSI/AAMI EC57:1998 standard) of MIT-BIH arrhythmia database were automatically classified. Our proposed methodology involves computation of Discrete Cosine Transform (DCT) coefficients from the segmented beats of ECG, which were then subjected for principal component analysis for dimensionality reduction. Then the clinically significant principal components were fed to (i) feed forward neural network, (ii) least square support vector machine with different kernel functions, and (iii) Probabilistic Neural Network (PNN) for automatic classification. We have obtained the highest average sensitivity of 98.69%, specificity of 99.91%, and classification accuracy of 99.52% with the developed knowledge based system. The developed system is clinically ready to deploy for mass screening programs.

Automated detection of atrial fibrillation using Bayesian paradigm

Abstract: Electrocardiogram (ECG) is widely used as a diagnostic tool to identify atrial tachyarrhythmias such as atrial fibrillation. The ECG signal is a P-QRS-T wave representing the cardiac function. The minute variations in the durations and amplitude of these waves cannot be easily deciphered by the naked eye. Hence, there is a need for computer aided diagnosis (CAD) of cardiac healthcare. The current paper presents a methodology for ECG based pattern analysis of normal sinus rhythm and atrial fibrillation (AF) beats. The denoised and registered ECG beats were subjected to independent component analysis (ICA) for data reduction. The weights of ICA were used as features for classification using Naive Bayes and Gaussian mixture model (GMM) classifiers. The performance and the upper bound on probability of error in classification were analyzed using Chernoff and Bhattacharyya bounds. The Naive Bayes classifier provided an average sensitivity of 99.32%, specificity of 99.33% and accuracy of 99.33%, while the GMM provided an average sensitivity of 100%, specificity of 99% and accuracy of 99.42%. The probability of error during classification was less for GMM compared to Naive Bayes classifier (NBC) as GMM provided higher performance than the NBC.

Automated diagnosis of maculopathy stages using texture features

Abstract: Objective: Diabetic maculopathy is the main cause of visual loss in diabetic subjects. It affects the central vision from the early stage of diabetic retinopathy (DR). Regular eye screening of diabetes patients helps to detect the maculopathy at early stage and hence prevent the loss of vision. Manual screening of retinal images by ophthalmologists is time consuming, tedious and may cause inter/intra observer variability. Objective of our study is to develop an automated system to classify retinal fundus images into normal, non-clinically significant macular edema (NCSME) and clinically significant macular edema (CSME) classes. Methods: The normal and DR images has various granular structures at different scale termed as ‘‘texture’’. In this work, texture features are extracted from the image. The statistically significant features are then fed to Fuzzy-Sugeno (FS) classifier for automated diagnosis. Results: The proposed technique is validated using 300 images, 100 images of each normal, NCSME and CSME. We have obtained the best results using FS classifier with accuracy of 86.67%, sensitivity and specificity of 100%, and specificity of 100%. Conclusion: This proposed automated system can aid clinicians in cross checking their diagnosis of diabetic maculopathy during the mass screening of DR subjects.

Comparison of walking parameters obtained from the young, elderly and adults with support

Abstract: Data mining techniques are highly useful in the study of various medical signals and images in order to obtain useful information to better predict the diagnosis or prognosis or treatment options for the patient Study of the human walking pattern helps us understand the variability of motion during activities such as high performance walking and normal walking A comparison of the parameters quantifying this variability in motion in normal young and elderly subjects and the subjects who need support will aid in better understanding of the relationship among walking patterns, age and disabilities In this study, we measured the tri-axial acceleration along three directions: anteroposterior, lateral and vertical We also measured gyrational pitch, roll and yaw These parameters were obtained using sensors attached to the back, left thigh and right thigh of the three classes of subjects (normal, elderly and adults with support) during the three types of exercises: 10-m normal walk, 10-m high performance walk and stepping These recorded signals were then subjected to wavelet packet decomposition, and three entropies, namely approximate entropy and two bispectral entropies, were obtained from the resultant wavelet coefficients On analysing these entropies, we could observe the following: (1) the entropy steadily decreases with the increase in age and with the presence of impairments, and (2) the entropy decreases among all the three types of exercises, namely normal walking and high performance walking We feel that the results of this work can help in the design of supporting devices for elderly subjects