Browsing by Author "Saminu, Sani"

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Citation - Scopus: 3
Analysis of Cardiac Beats using Higher Order Spectra
(IEEE, 2014) Ibrahim Abdullahi Karaye; Sani Saminu; Nalan Ozkurt; Karaye, Ibrahim Abdullahi; Saminu, Sani; Ozkurt, Nalan; S Misra; C Ayo; N Omoregbe; B Odusote; A Adewumi
For early diagnosis of the heart failures the electrocardiography (ECG) is the most common method because of its simplicity and cost. Computer based analysis of ECG provides reliable and efficient tools in diagnostics of arrhythmias. With this objective there are lots of studies on automatic and semi-automatic ECG analysis. Like many biosignals ECG signals are nonlinear in nature higher order spectral analysis (HOS) is known to be a very good tool for the analysis of nonlinear systems producing good noise immunity. Thus in this study HOS analysis of ECG signals of normal heart rate right bundle branch block paced beat left bundle block branch and atrial premature beats have been studied in order to reveal the complex dynamics of ECG signals using the tools of nonlinear systems theory. Some of the general characteristics for each of these classes in the bispectrum and bicoherence plot for visual observation have been presented. For the extraction of R-R intervals well known Pan-Tompkins algorithm has been used and three higher order statistical parameters of skewness kurtosis and variance from these features have been computed. These features with statistical parameters fed into artificial neural network classifier (ANN) and obtained an average accuracy of 94.9%.
Citation - Scopus: 5
Wavelet Feature Extraction for ECG Beat Classification
(IEEE, 2014) Sani Saminu; Nalan Ozkurt; Ibrahim Abdullahi Karaye; Karaye, Ibrahim Abdullahi; Sarvan, Cagla; Ozkurt, Nalan; Saminu, Sani; S Misra; C Ayo; N Omoregbe; B Odusote; A Adewumi
Electrocardiography (ECG) signal is a bioelectrical signal which depicts the cardiac activity of the heart. It is a technique used primarily as a diagnostic tool for various cardiac diseases. ECG provides necessary information on the electrophysiology and changes that may occur in the heart. Due to the increase in mortality rate associated with cardiac diseases worldwide despite recent technological advancement early detection of these diseases is of paramount importance. This paper has proposed a robust ECG feature extraction technique suitable for mobile devices by extracting only 200 samples between R-R intervals as equivalent R-T interval using Pan Tompkins algorithm at preprocessing stage. The discrete wavelet transform (DWT) of R-T interval samples are calculated and the statistical parameters of wavelet coefficients such as mean median standard deviation maximum minimum energy and entropy are used as a time-frequency domain feature. The proposed hybrid technique has been tested by classifying three ECG beats as normal right bundle branch block (Rbbb) and paced beat using the signals from Massachusetts Institute of Technology Beth Israel Hospital (MIT-BIH) arrhythmia database and processed using Matlab 2013 environment. Classification has been performed using neural network backpropagation algorithm because of its simplicity. While equivalent R-T interval features gives average accuracy of 98.22% the proposed hybrid method gives a promising result with average accuracy of 99.84% with reduced classifier computational complexity.