Wavelet-Based Kernel Construction for Heart Disease Classification

© 2019 ADVANCES IN ELECTRICAL AND ELECTRONIC ENGINEERINGHeart disease classification plays an important role in clinical diagnoses. The performance improvement of an Electrocardiogram classifier is therefore of great relevance, but it is a challenging task too. This paper proposes a novel classification algorithm using the kernel method. A kernel is constructed based on wavelet coefficients of heartbeat signals for a classifier with high performance. In particular, a wavelet packet decomposition algorithm is applied to heartbeat signals to obtain the Approximation and Detail coefficients, which are used to calculate the parameters of the kernel. A principal component analysis algorithm with the wavelet-based kernel is employed to choose the main features of the heartbeat signals for the input of the classifier. In addition, a neural network with three hidden layers in the classifier is utilized for classifying five types of heart disease. The electrocardiogram signals in nine patients obtained from the MIT-BIH database are used to test the proposed classifier. In order to evaluate the performance of the classifier, a multi-class confusion matrix is applied to produce the performance indexes, including the Accuracy, Recall, Precision, and F1 score. The experimental results show that the proposed method gives good results for the classification of the five mentioned types of heart disease.Peer reviewedFinal Published versio

A Review Of R Peak Detection Techniques Of Electrocardiogram (ECG)

Heart disease is one of the trivial issues regarding health problem over the last few decades in India. Numerous methods have been developed with still-ongoing modifications and ideas to observe and evaluate ECG signals based on each heart beat. Majority of research revolves around arrhythmia classification, heart rate monitoring and blood pressure measurements that require highly accurate assessments of rhythm disorders which can be possible by measuring QRS complex of ECG signal, so accurate QRS detection methods are very important to be utilized. There have been proposed many approaches to find out the R peak detection to analyze the ECG signals in past few years. Most recent and efficient techniques of R peak detection have been reviewed in this paper. Techniques which have been reviewed in this paper are Pan and Tompkins, Wavelet Transform, Empirical Mode Decomposition, Hilbert-Huang Transform, Fuzzy logic systems, Artificial neural networks

Motor Imagery EEG Recognition using Deep Generative Adversarial Network with EMD for BCI Applications

The activities for motor imagery (MI) movements in Electroencephalography (EEG) are still interesting and challenging. BCI (Brain Computer Interface) allows the brain signals to control the external devices and also helps a disabled person suffering from neuromuscular disorders. In any BCI system, the two most essential steps are feature extraction and classification method. However, in this paper, the MI classification is improved by the performance of Deep Learning (DL) concept. In this proposed system two-moment imagination of right hand and right foot from the BCI competition three datasets IVA has been taken and classification methods utilizing Conventional neural network (CNN) and Generative Adversarial Network (GAN) are developed. The training time is reduced and non-stationary problem is managed by applying Empirical mode decomposition (EMD) and mixing their intrinsic mode functions (IMFs) in feature extraction technique. The experimental result indicates the proposed GAN classification technique achieves better classification accuracy in terms of 95.29% than the CNN of 89.38%. The proposed GAN method achieves an average positive rate of 62% and average false positive rate of 3.4% on BCI competition three datasets IVA whose EEG facts were resulting from the similar C3, C4, and Cz channels of the motor cortex

Human lower limb activity recognition techniques, databases, challenges and its applications using sEMG signal: an overview

Human lower limb activity recognition (HLLAR) has grown in popularity over the last decade mainly because to its applications in the identification and control of neuromuscular disorders, security, robotics, and prosthetics. Surface electromyography (sEMG) sensors provide various advantages over other wearable or visual sensors for HLLAR applications, including quick response, pervasiveness, no medical monitoring, and negligible infection. Recognizing lower limb activity from sEMG signals is also challenging owing to the noise in the sEMG signal. Pre- processing of sEMG signals is extremely desirable before the classification because they allow a more consistent and precise evaluation in the above applications. This article provides a segment-by-segment overview of: (1) Techniques for eliminating artifacts from sEMG signals from the lower limb. (2) A survey of existing datasets of lower limb sEMG. (3) A concise description of the various techniques for processing and classifying sEMG data for various applications involving lower limb activity. Finally, an open discussion is presented, which may result in the identification of a variety of future research possibilities for human lower limb activity recognition. Therefore, it is possible to anticipate that the framework presented in this study can aid in the advancement of sEMG-based recognition of human lower limb activity

Analysis of Signal Decomposition and Stain Separation methods for biomedical applications

Nowadays, the biomedical signal processing and classification and medical image interpretation play an essential role in the detection and diagnosis of several human diseases. The problem of high variability and heterogeneity of information, which is extracted from digital data, can be addressed with signal decomposition and stain separation techniques which can be useful approaches to highlight hidden patterns or rhythms in biological signals and specific cellular structures in histological color images, respectively. This thesis work can be divided into two macro-sections. In the first part (Part I), a novel cascaded RNN model based on long short-term memory (LSTM) blocks is presented with the aim to classify sleep stages automatically. A general workflow based on single-channel EEG signals is developed to enhance the low performance in staging N1 sleep without reducing the performances in the other sleep stages (i.e. Wake, N2, N3 and REM). In the same context, several signal decomposition techniques and time-frequency representations are deployed for the analysis of EEG signals. All extracted features are analyzed by using a novel correlation-based timestep feature selection and finally the selected features are fed to a bidirectional RNN model. In the second part (Part II), a fully automated method named SCAN (Stain Color Adaptive Normalization) is proposed for the separation and normalization of staining in digital pathology. This normalization system allows to standardize digitally, automatically and in a few seconds, the color intensity of a tissue slide with respect to that of a target image, in order to improve the pathologist’s diagnosis and increase the accuracy of computer-assisted diagnosis (CAD) systems. Multiscale evaluation and multi-tissue comparison are performed for assessing the robustness of the proposed method. In addition, a stain normalization based on a novel mathematical technique, named ICD (Inverse Color Deconvolution) is developed for immunohistochemical (IHC) staining in histopathological images. In conclusion, the proposed techniques achieve satisfactory results compared to state-of-the-art methods in the same research field. The workflow proposed in this thesis work and the developed algorithms can be employed for the analysis and interpretation of other biomedical signals and for digital medical image analysis