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

Negative Giant Longitudinal Magnetoresistance in NiMnSb/InSb: An interface effect

We report on the electrical and magneto-transport properties of the contact formed between polycrystalline NiMnSb thin films grown using pulsed laser deposition (PLD) and n-type degenerate InSb (100) substrates. A negative giant magnetoresistance (GMR) effect is observed when the external magnetic field is parallel to the surface of the film and to the current direction. We attribute the observed phenomenon to magnetic precipitates formed during the magnetic film deposition and confined to a narrow layer at the interface. The effect of these precipitates on the magnetoresistance depends on the thermal processing of the system.Comment: 14 pages, 4 figure

Using Simple Neural Networks to Correct Errors in Optical Data Transmission.

We have demonstrated the applicability of neural-network-based systems to the problem of reducing the effects of signal distortion, and shown that such a system has the potential to reduce the bit-error-rate in the digitized version of the analogue electrical signal derived from an optical data stream by a substantial margin over existing techniques

The electronic transport properties and microstructure of carbon nanofiber/epoxy composites

Carbon nanofibres (CNF) were dispersed into an epoxy resin using a combination of ultrasonication and mechanical mixing. The electronic transport properties of the resulting composites were investigated by means of impedance spectroscopy. It was found that a very low critical weight fraction (pc = 0.064 wt %) which may be taken to correspond to the formation of a tunneling conductive network inside the matrix. The insulator-to-conductor transition region spanned about one order of magnitude from 0.1 to 1 wt %. Far from the transition, the conductivity increased by two orders of magnitude. This increase and the low value of the conductivity were explained in terms of the presence of an epoxy film at the contact between CNF. A simple model based on the CNF-CNF contact network inside the matrix was proposed in order to evaluate the thickness of that film.Comment: 7 page