Lim Choo Min

ECG beat classification using PCA, LDA, ICA and Discrete Wavelet Transform

Abstract Electrocardiogram (ECG) is the P-QRS-T wave, representing the cardiac function. The information concealed in the ECG signal is useful in detecting the disease afflicting the heart. It is very difficult to identify the subtle changes in the ECG in time and frequency domains. The Discrete Wavelet Transform (DWT) can provide good time and frequency resolutions and is able to decipher the hidden complexities in the ECG. In this study, five types of beat classes of arrhythmia as recommended by Association for Advancement of Medical Instrumentation (AAMI) were analyzed namely: non-ectopic beats, supra-ventricular ectopic beats, ventricular ectopic beats, fusion betas and unclassifiable and paced beats. Three dimensionality reduction algorithms; Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Independent Component Analysis (ICA) were independently applied on DWT sub bands for dimensionality reduction. These dimensionality reduced features were fed to the Support Vector Machine (SVM), neural network (NN) and probabilistic neural network (PNN) classifiers for automated diagnosis. ICA features in combination with PNN with spread value ( σ ) of 0.03 performed better than the PCA and LDA. It has yielded an average sensitivity, specificity, positive predictive value (PPV) and accuracy of 99.97%, 99.83%, 99.21% and 99.28% respectively using ten-fold cross validation scheme.

Identification of different stages of diabetic retinopathy using retinal optical images

Diabetes is a disease which occurs when the pancreas does not secrete enough insulin or the body is unable to process it properly. This disease affects slowly the circulatory system including that of the retina. As diabetes progresses, the vision of a patient may start to deteriorate and lead to diabetic retinopathy. In this study on different stages of diabetic retinopathy, 124 retinal photographs were analyzed. As a result, four groups were identified, viz., normal retina, moderate non-proliferative diabetic retinopathy, severe non-proliferative diabetic retinopathy and proliferative diabetic retinopathy. Classification of the four eye diseases was achieved using a three-layer feedforward neural network. The features are extracted from the raw images using the image processing techniques and fed to the classifier for classification. We demonstrate a sensitivity of more than 90% for the classifier with the specificity of 100%.

ANALYSIS AND AUTOMATIC IDENTIFICATION OF SLEEP STAGES USING HIGHER ORDER SPECTRA

Electroencephalogram (EEG) signals are widely used to study the activity of the brain, such as to determine sleep stages. These EEG signals are nonlinear and non-stationary in nature. It is difficult to perform sleep staging by visual interpretation and linear techniques. Thus, we use a nonlinear technique, higher order spectra (HOS), to extract hidden information in the sleep EEG signal. In this study, unique bispectrum and bicoherence plots for various sleep stages were proposed. These can be used as visual aid for various diagnostics application. A number of HOS based features were extracted from these plots during the various sleep stages (Wakefulness, Rapid Eye Movement (REM), Stage 1-4 Non-REM) and they were found to be statistically significant with p-value lower than 0.001 using ANOVA test. These features were fed to a Gaussian mixture model (GMM) classifier for automatic identification. Our results indicate that the proposed system is able to identify sleep stages with an accuracy of 88.7%.

Automatic identification of epileptic and background EEG signals using frequency domain parameters.

The analysis of electroencephalograms continues to be a problem due to our limited understanding of the signal origin. This limited understanding leads to ill-defined models, which in turn make it hard to design effective evaluation methods. Despite these shortcomings, electroencephalogram analysis is a valuable tool in the evaluation of neurological disorders and the evaluation of overall cerebral activity. We compared different model based power spectral density estimation methods and different classification methods. Specifically, we used the autoregressive moving average as well as from Yule-Walker and Burgs methods, to extract the power density spectrum from representative signal samples. Local maxima and minima were detected from these spectra. In this paper, the locations of these extrema are used as input to different classifiers. The three classifiers we used were: Gaussian mixture model, artificial neural network, and support vector machine. The classification results are documented with confusion matrices and compared with receiver operating characteristic curves. We found that Burgs method for spectrum estimation together with a support vector machine classifier yields the best classification results. This combination reaches a classification rate of 93.33%, the sensitivity is 98.33% and the specificy is 96.67%.

Transmission and storage of medical images with patient information

Digital watermarking is a technique of hiding specific identification data for copyright authentication. This technique is adapted here for interleaving patient information with medical images, to reduce storage and transmission overheads. The text data is encrypted before interleaving with images to ensure greater security. The graphical signals are interleaved with the image. Two types of error control-coding techniques are proposed to enhance reliability of transmission and storage of medical images interleaved with patient information. Transmission and storage scenarios are simulated with and without error control coding and a qualitative as well as quantitative interpretation of the reliability enhancement resulting from the use of various commonly used error control codes such as repetitive, and (7,4) Hamming code is provided.

Simultaneous storage of patient information with medical images in the frequency domain

Digital watermarking is a technique of hiding specific identification data for copyright authentication. Most of the medical images are compressed by joint photographic experts group (JPEG) standard for storage. The watermarking is adapted here for interleaving patient information with medical images during JPEG compression, to reduce storage and transmission overheads. The text data is encrypted before interleaving with images in the frequency domain to ensure greater security. The graphical signals are also interleaved with the image. The result of this work is tabulated for a specific example and also compared with the spatial domain interleaving.

Decision support system for the glaucoma using Gabor transformation

Abstract Increase in intraocular pressure (IOP) is one of the causes of glaucoma which can lead to blindness if not detected and treated at an early stage. Glaucoma symptoms are not always obvious; hence patients seek treatment only when the condition progressed significantly. Early detection and treatment will decrease the chances of vision loss due to glaucoma. This paper proposes a novel automated glaucoma diagnosis method using various features extracted from Gabor transform applied on digital fundus images. In this work, we have used 510 images to classify into normal and glaucoma classes. Various features namely mean, variance, skewness, kurtosis, energy, and Shannon, Renyi, and Kapoor entropies are extracted from the Gabor transform coefficients. These extracted features are subjected to principal component analysis (PCA) to reduce the dimensionality of the features. Then these features are ranked using various ranking methods namely: Bhattacharyya space algorithm, t-test, Wilcoxon test, Receiver Operating Curve (ROC), and entropy. In this work, t-test ranking method yielded the highest performance with an average accuracy of 93.10%, sensitivity of 89.75% and specificity of 96.20% using 23 features with Support Vector Machine (SVM) classifier. Also, we have proposed a Glaucoma Risk Index (GRI) developed using principal components to classify the two classes using just one number.

Pectoral muscle segmentation: A review

Mammograms are X-ray images of breasts which are used to detect breast cancer. The pectoral muscle is a mass of tissue on which the breast rests. During routine mammographic screenings, in medio-lateral oblique (MLO) views, the pectoral muscle turns up in the mammograms along with the breast tissues. The pectoral muscle has to be segmented from the mammogram for an effective automated computer aided diagnosis (CAD). This is due to the fact that pectoral muscles have pixel intensities and texture similar to that of breast tissues which can result in awry CAD results. As a result, a lot of effort has been put into the segmentation of pectoral muscles and finding its contour with the breast tissues. To the best of our knowledge, currently there is no definitive literature available which provides a comprehensive review about the current state of research in this area of pectoral muscle segmentation. We try to address this shortcoming by providing a comprehensive review of research papers in this area. A conscious effort has been made to avoid deviating into the area of automated breast cancer detection.

Cardiac health diagnosis using higher order spectra and support vector machine.

The Electrocardiogram (ECG) is an important bio-signal representing the sum total of millions of cardiac cell depolarization potentials. It contains important insight into the state of health and nature of the disease afflicting the heart. Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart by the sympathetic and parasympathetic branches of the autonomic nervous system. The HRV signal can be used as a base signal to observe the hearts functioning. These signals are non-linear and non-stationary in nature. So, higher order spectral (HOS) analysis, which is more suitable for non-linear systems and is robust to noise, was used. An automated intelligent system for the identification of cardiac health is very useful in healthcare technology. In this work, we have extracted seven features from the heart rate signals using HOS and fed them to a support vector machine (SVM) for classification. Our performance evaluation protocol uses 330 subjects consisting of five different kinds of cardiac disease conditions. We demonstrate a sensitivity of 90% for the classifier with a specificity of 87.93%. Our system is ready to run on larger data sets.

Data Fusion of Multimodal Cardiovascular Signals

The electrocardiogram (ECG) is a representative signal containing useful information about the condition of the heart. The shape and size of the P-QRS-T wave, the R-R interval etc. may help to identify the nature of disease afflicting the heart. However, human observer can not directly monitor these subtle details. Hence, the fusion of ECG, blood pressure, saturated oxygen content and respiratory data for achieving improved clinical diagnosis of patients in cardiac care units. Therefore, computer based analysis and display, is highly useful in diagnostics. The study demonstrates the feasibility of fuzzy logic based data fusion of the heterogeneous signals for the detection of life threatening states. Important parameters are derived from multimodal data and rule based approaches have been used. Fuzzified region for various abnormality conditions have been obtained which demonstrate the efficacy of the approach in various test cases. Comprehensive pictures showing the condition of the patient in various states will help physician in making a timely assessment in an intensive care set up