Automated characterization and classification of coronary artery disease and myocardial infarction by decomposition of ECG signals

The dynamics of brain area influenced by focal epilepsy can be studied using focal and non-focal electroencephalogram (EEG) signals. This paper presents a new method to detect focal and non-focal EEG signals based on an integrated index, termed the focal and non-focal index (FNFI), developed using discrete wavelet transform (DWT) and entropy features. The DWT decomposes the EEG signals up to six levels, and various entropy measures are computed from approximate and detail coefficients of sub-band signals. The computed entropy measures are average wavelet, permutation, fuzzy and phase entropies. The proposed FNFI developed using permutation, fuzzy and Shannon wavelet entropies is able to clearly discriminate focal and non-focal EEG signals using a single number. Furthermore, these entropy measures are ranked using different techniques, namely the Bhattacharyya space algorithm, Student’s t-test, the Wilcoxon test, the receiver operating characteristic (ROC) and entropy. These ranked features are fed to various classifiers, namely k-nearest neighbour (KNN), probabilistic neural network (PNN), fuzzy classifier and least squares support vector machine (LS-SVM), for automated classification of focal and non-focal EEG signals using the minimum number of features. The identification of the focal EEG signals can be helpful to locate the epileptogenic focus.

An Integrated Index for the Identification of Focal Electroencephalogram Signals Using Discrete Wavelet Transform and Entropy Measures

Diabetes detection using deep learning algorithms

Automated detection of diabetes using CNN and CNN-LSTM network and heart rate signals

Myocardial infarction (MI) is a silent condition that irreversibly damages the heart muscles. It expands rapidly and, if not treated timely, continues to damage the heart muscles. An electrocardiogram (ECG) is generally used by the clinicians to diagnose the MI patients. Manual identification of the changes introduced by MI is a time-consuming and tedious task, and there is also a possibility of misinterpretation of the changes in the ECG. Therefore, a method for automatic diagnosis of MI using ECG beat with flexible analytic wavelet transform (FAWT) method is proposed in this work. First, the segmentation of ECG signals into beats is performed. Then, FAWT is applied to each ECG beat, which decomposes them into subband signals. Sample entropy (SEnt) is computed from these subband signals and fed to the random forest (RF), J48 decision tree, back propagation neural network (BPNN), and least-squares support vector machine (LS-SVM) classifiers to choose the highest performing one. We have achieved highest classification accuracy of 99.31% using LS-SVM classifier. We have also incorporated Wilcoxon and Bhattacharya ranking methods and observed no improvement in the performance. The proposed automated method can be installed in the intensive care units (ICUs) of hospitals to aid the clinicians in confirming their diagnosis.

https://www.mdpi.com/1099-4300/19/9/488/pdf

Automated Diagnosis of Myocardial Infarction ECG Signals Using Sample Entropy in Flexible Analytic Wavelet Transform Framework

We propose new features for analysis of normal and diabetic RR-interval signals.Features are extracted from intrinsic mode functions of RR-interval signals.Two unique visual plots are proposed for diagnosis of diabetes.Proposed features are suitable for discrimination of normal and diabetic classes. Large number of people are affected by Diabetes Mellitus (DM) which is difficult to cure due to its chronic nature and genetic link. The uncontrolled diabetes may lead to heart related problems. Therefore, the diagnosis and monitoring of diabetes is of great importance. The automatic detection of diabetes can be performed using RR-interval signals. The RR-interval signals are nonlinear and non-stationary in nature. Hence linear methods may not be able to capture the hidden information present in the signal. In this paper, a new nonlinear method based on empirical mode decomposition (EMD) is proposed to discriminate between diabetic and normal RR-interval signals. The mean frequency parameter using Fourier-Bessel series expansion ( MF FB ) and the two bandwidth parameters namely, amplitude modulation bandwidth ( B AM ) and frequency modulation bandwidth ( B FM ) extracted from the intrinsic mode functions (IMFs) obtained from the EMD of RR-interval signals are used to discriminate the two groups. Unique representations such as analytic signal representation (ASR) and second order difference plot (SODP) for IMFs of RR-interval signals are also proposed to differentiate the two groups. The area parameters are computed from ASR and SODP of IMFs of RR-interval signals. Area computed from these representation as area corresponding to the 95% central tendency measure (CTM) of ASR of IMFs ( A ASR ) and 95% confidence ellipse area of SODP of IMF ( A SODP ) are also proposed to discriminate diabetic and normal RR-interval signals. Overall, five features are extracted from IMFs of RR-interval signals namely MF FB , B AM , B FM , A ASR and A SODP . Kruskal-Wallis statistical test is used to measure the discrimination ability of the proposed features for detection of diabetic RR-interval signals. Results obtained from proposed methodology indicate that these features provide the statistically significant difference between diabetic and normal classes.

Application of empirical mode decomposition for analysis of normal and diabetic RR-interval signals

Diabetes Mellitus (DM) which is a chronic disease and difficult to cure. If diabetes is not treated in a timely manner, it may cause serious complications. For timely treatment, an early detection of the disease is of great interest. Diabetes can be detected by analyzing the RR-interval signals. This work presents a methodology for classification of diabetic and normal RR-interval signals. Firstly, empirical mode decomposition (EMD) method is applied to decompose the RR-interval signals in to intrinsic mode functions (IMFs). Then five parameters namely, area of analytic signal representation (AASR), mean frequency computed using Fourier-Bessel series expansion (MFFB), area of ellipse evaluated from second-order difference plot (ASODP), bandwidth due to frequency modulation (BFM) and bandwidth due to amplitude modulation (BAM) are extracted from IMFs obtained from RR-interval signals. Statistically significant features are fed to least square-support vector machine (LS-SVM) classifier. The three kernels namely, Radial Basis Function (RBF), Morlet wavelet, and Mexican hat wavelet kernels have been studied to obtain the suitable kernel function for the classification of diabetic and normal RR-interval signals. In this work, we have obtained the highest classification accuracy of 95.63%, using Morlet wavelet kernel function with 10-fold cross-validation. The classification system proposed in this work can help the clinicians to diagnose diabetes using electrocardiogram (ECG) signals.

Pakala Avinash

Papers

Application of empirical mode decomposition for analysis of normal and diabetic RR-interval signals

An improved online paradigm for screening of diabetic patients using rr-interval signals