Linear and nonlinear analysis of normal and CAD-affected heart rate signals

Coronary Artery Disease (CAD) is one of the dangerous cardiac disease, often may lead to sudden cardiac death. It is difficult to diagnose CAD by manual inspection of electrocardiogram (ECG) signals. To automate this detection task, in this study, we extracted the Heart Rate (HR) from the ECG signals and used them as base signal for further analysis. We then analyzed the HR signals of both normal and CAD subjects using (i) time domain, (ii) frequency domain and (iii) nonlinear techniques. The following are the nonlinear methods that were used in this work: Poincare plots, Recurrence Quantification Analysis (RQA) parameters, Shannon entropy, Approximate Entropy (ApEn), Sample Entropy (SampEn), Higher Order Spectra (HOS) methods, Detrended Fluctuation Analysis (DFA), Empirical Mode Decomposition (EMD), Cumulants, and Correlation Dimension. As a result of the analysis, we present unique recurrence, Poincare and HOS plots for normal and CAD subjects. We have also observed significant variations in the range of these features with respect to normal and CAD classes, and have presented the same in this paper. We found that the RQA parameters were higher for CAD subjects indicating more rhythm. Since the activity of CAD subjects is less, similar signal patterns repeat more frequently compared to the normal subjects. The entropy based parameters, ApEn and SampEn, are lower for CAD subjects indicating lower entropy (less activity due to impairment) for CAD. Almost all HOS parameters showed higher values for the CAD group, indicating the presence of higher frequency content in the CAD signals. Thus, our study provides a deep insight into how such nonlinear features could be exploited to effectively and reliably detect the presence of CAD

A Review

Ovarian cancer is the most common cause of death among gynecological malignancies. We discuss different types of clinical and nonclinical features that are used to study and analyze the differences between benign and malignant ovarian tumors. Computer-aided diagnostic (CAD) systems of high accuracy are being developed as an initial test for ovarian tumor classification instead of biopsy, which is the current gold standard diagnostic test. We also discuss different aspects of developing a reliable CAD system for the automated classification of ovarian cancer into benign and malignant types. A brief description of the commonly used classifiers in ultrasound-based CAD systems is also given

Ovarian tumor characterization and classification: A class of GyneScan™ systems

Abstract: In this work, we have developed an adjunct Computer Aided Diagnostic (CAD) technique that uses 3D acquired ultrasound images of the ovary and data mining algorithms to accurately characterize and classify benign and malignant ovarian tumors. In this technique, we extracted image-texture based and Higher Order Spectra (HOS) based features from the images. The significant features were then selected and used to train and test the Decision Tree (DT) classifier. The proposed technique was validated using 1000 benign and 1000 malignant images, obtained from 10 patients with benign and 10 with malignant disease, respectively. On evaluating the classifier with 10-fold stratified cross validation, we observed that the DT classifier presented a high accuracy of 95.1%, sensitivity of 92.5% and specificity of 97.7%. Thus, the four significant features could adequately quantify the subtle changes and nonlinearities in the pixel intensities. The preliminary results presented in this paper indicate that the proposed technique can be reliably used as an adjunct tool for ovarian tumor classification since the system is accurate, completely automated, cost-effective, and can be easily written as a software application for use in any compute

Carotid ultrasound symptomatology using atherosclerotic plaque characterization: A class of Atheromatic systems

Abstract: In this paper, we present a Computer Aided Diagnosis (CAD) based technique (Atheromatic system) for classification of carotid plaques in B-mode ultrasound images into symptomatic or asymptomatic classes. This system, called Atheromatic, has two steps: (i) feature extraction using a combination of Discrete Wavelet Transform (DWT) and averaging algorithms and (ii) classification using Support Vector Machine (SVM) classifier for automated decision making. The CAD system was built and tested using a database consisting of 150 asymptomatic and 196 symptomatic plaque regions of interests which were manually segmented. The ground truth of each plaque was determined based on the presence or absence of symptoms. Three-fold cross-validation protocol was adapted for developing and testing the classifiers. The SVM classifier with a polynomial kernel of order 2 recorded the highest classification accuracy of 83.7%. In the clinical scenario, such a technique, after much more validation, can be used as an adjunct tool to aid physicians by giving a second opinion on the nature of the plaque (symptomatic/asymptomatic) which would help in the more confident determination of the subsequent treatment regime for the patien

Automated diagnosis of epileptic EEG using entropies

Epilepsy is a neurological disorder characterized by the presence of recurring seizures. Like many other neurological disorders, epilepsy can be assessed by the electroencephalogram (EEG). The EEG signal is highly non-linear and non-stationary, and hence, it is difficult to characterize and interpret it. However, it is a well-established clinical technique with low associated costs. In this work, we propose a methodology for the automatic detection of normal, pre-ictal, and ictal conditions from recorded EEG signals. Four entropy features namely Approximate Entropy (ApEn), Sample Entropy (SampEn), Phase Entropy 1 (S1), and Phase Entropy 2 (S2) were extracted from the collected EEG signals. These features were fed to seven different classifiers: Fuzzy Sugeno Classifier (FSC), Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Probabilistic Neural Network (PNN), Decision Tree (DT), Gaussian Mixture Model (GMM), and Naive Bayes Classifier (NBC). Our results show that the Fuzzy classifier was able to differentiate the three classes with a high accuracy of 98.1%. Overall, compared to previous techniques, our proposed strategy is more suitable for diagnosis of epilepsy with higher accuracy

Evolutionary algorithm-based classifier parameter tuning for automatic ovarian cancer tissue characterization and classification

Purpose: Ovarian cancer is one of the most common gynecological cancers in women. It is difficult to accurately and objectively diagnose benign and malignant ovarian tumors using ultrasound and other tests. Hence, there is an imperative need to develop a computer-aided diagnostic (CAD) system for ovarian tumor classification in order to reduce patient anxiety and the cost of unnecessary biopsies. In this paper, we present an automatic CAD system for the detection of benign and malignant ovarian tumors using advanced image processing and data mining techniques. Materials and Methods: In the proposed system, Hu's invariant moments, Gabor transform parameters and entropies are first extracted from the acquired ultrasound images. Significant features are then used to train a probabilistic neural network (PNN) classifier for classifying the images into benign and malignant categories. The model parameter (σ) for which the PNN classifier performs the best is identified using a genetic algorithm (GA). Results: The proposed system was validated using 1300 benign images and 1300 malignant images, obtained from 10 patients with a benign disease and 10 with a malignant disease. We used 23 statistically significant (p < 0.0001) features. By evaluating the classifier using a ten-fold cross-validation technique, we were able to achieve an average classification accuracy of 99.8 %, sensitivity of 99.2 % and specificity of 99.6 % with a σ of 0.264. Conclusion: The proposed system is automated and hence is more objective, can be easily deployed in any computer, is fast and accurate and can act as an adjunct tool in helping physicians make a confident call about the nature of the ovarian tumor under evaluatio