Racial differences in the ECG--selected aspects.

INTRODUCTION: Racial differences in the ECG have been known about for many years but there has been no significant comparison of large population groups. This study set out to remedy this shortcoming. METHODS: Digital ECG data were available for four population samples gathered in Scotland, Taiwan, Nigeria and India. All ECGs were recorded in the different countries and processed centrally by the University of Glasgow ECG Analysis Program. Measurements were analysed statistically to look for significant differences. RESULTS: There were 4223 individuals in the study (2559 males and 1664 females). In general terms, findings such as QRS duration being longer in males than females applied to all four races. More specifically, QRS voltages were higher in young black males compared to others, while ST amplitudes, as in V2, were higher in Chinese and Nigerian males than in Caucasians. CONCLUSION: Race requires to be taken into account to enhance automated interpretation of the ECG.

ECG feature extraction using principal component analysis for studying the effect of diabetes.

The condition of cardiac health is given by Electrocardiogram (ECG). ECG analysis is one of the most important aspects of research in the field of Biomedical and healthcare. The precision in the identification of various parameters in ECG is of great importance. Many algorithms have been developed in the last few years for this purpose. Since diabetes is the major chronic illness prevailing today, recently there has been increasing interest in the study of the relationship between diabetes and cardiac health. This paper presents an algorithm based on Principal Component Analysis (PCA) for 12 lead ECG feature extraction and the estimation of diabetes-related ECG parameters. The data used for our purpose is acquired by XBio Aqulyser unit from TMI systems. The baseline wander is removed from the acquired data using the FFT approach and the signal is de-noised using wavelet transform and then the PCA method is employed to extract the R-wave. The other waves are then extracted using the window method. Later, using these primary features, the diabetes mellitus (DM)-related features like corrected QT interval (QTc), QT dispersion (QTd), P wave dispersion (PD) and ST depression (STd) are estimated. This study has taken 25 diabetic patients data for study.

Statistical analysis of RR series variability in spinal cord injured persons.

Disabled persons with spinal cord injury are prone to cardiovascular dysfunction and an increased risk of cardiovascular disease. Rehabilitation of the disabled person is a critical task as it involves multiple therapies. Physical exercise is an important component of rehabilitation, and depends on cardiovascular health. Reduced RR variability is a marker of poor cardiac health. Time domain RR variability analysis of 38 normal healthy subjects and 20 spinal cord injured subjects has been carried out and compared. In this study, RR intervals were recorded in three different modes or positions: supine, sitting and five-second rhythm respiration. At a time of 150 s RR interval data were acquired in each mode and analysed. Statistical parameters (mean, HR, STD, NN50 and pNN50) were calculated. It was observed that most of the indices were significantly and substantially altered in spinal cord injured persons.

ECG feature extraction and disease diagnosis.

An important factor to consider when using findings on electrocardiograms for clinical decision making is that the waveforms are influenced by normal physiological and technical factors as well as by pathophysiological factors. In this paper, we propose a method for the feature extraction and heart disease diagnosis using wavelet transform (WT) technique and LabVIEW (Laboratory Virtual Instrument Engineering workbench). LabVIEW signal processing tools are used to denoise the signal before applying the developed algorithm for feature extraction. First, we have developed an algorithm for R-peak detection using Haar wavelet. After 4th level decomposition of the ECG signal, the detailed coefficient is squared and the standard deviation of the squared detailed coefficient is used as the threshold for detection of R-peaks. Second, we have used daubechies (db6) wavelet for the low resolution signals. After cross checking the R-peak location in 4th level, low resolution signal of daubechies wavelet P waves and T waves are detected. Other features of diagnostic importance, mainly heart rate, R-wave width, Q-wave width, T-wave amplitude and duration, ST segment and frontal plane axis are also extracted and scoring pattern is applied for the purpose of heart disease diagnosis. In this study, detection of tachycardia, bradycardia, left ventricular hypertrophy, right ventricular hypertrophy and myocardial infarction have been considered. In this work, CSE ECG data base which contains 5000 samples recorded at a sampling frequency of 500 Hz and the ECG data base created by the S.G.G.S. Institute of Engineering and Technology, Nanded (Maharashtra) have been used.

QRS detection using new wavelets.

This paper deals with a new wavelet (WVT) which has been developed and very effectively and efficiently used for the detection of QRS segments from the ECG signal. After carrying out the detection using five existing wavelets (two symmetric--WT1 and WT2--and three asymmetric--WT3, WT4 and WT5), two new wavelets (WT6 and WT7) were constructed and used for QRS detection. WT6 is a symmetric wavelet and has been constructed by a trial-and-error method. WT7 is an adaptive symmetric wavelet and adjusts its threshold as per the amplitude of the ECG signal. The accuracy of QRS detection obtained from WT6 is 99.8 % and from WT7 100%. The CSE DS-3 database has been used for tests. Both WT6 and WT7 have been proved to be superior in performance to the existing wavelets. Out of WT6 and WT7, WT7 holds high promise for error-free reliable QRS detection in computer-aided feature extraction and disease diagnostics.