Certified Organization, Volume3, Special Issue 6

ABSTRACT: The paper describes the development of a low cost and simple amplifier circuit for ECG acquisition from a single lead. The acquisition circuit uses clip-type flat metal plate limb electrodes to sense the heart signals and a basic amplifier circuit is designed using JFET OP-AMP IC LF-353 with the required gain to suitably amplify the signal. The amplified data fed into a computer using USB-6009 is then denoised, processed and displayed using LabVIEW software. The developed ECG acquisition module is evaluated by visual comparison of simultaneously recorded data acquired by the module with and by the MP-150 amplifier system from BIOPAC Systems Inc. Tests have been performed in the laboratory on several volunteers in the age group of 28-60 and the results were quiet satisfactory

Abnormality Detection in ECG Signal applying Poincare and Entropy-based Approaches

Detection of abnormality in heart is of major importance for early and appropriate clinical medication. In this work, we have proposed two models for detection of abnormality in ECG signals. The normal ECG signals are closely repetitive in nature to a large extent, whereas ECG signals with abnormalities tend to differ from cycle to cycle. Hence, repetitive plot like the Poincare is efficient to detect such non-repetitiveness of the signal; thereby, indicating abnormalities. Hence, we have used Poincare plot to develop the two proposed models. One of the models uses direct analysis of the binary image of the plot to detect the difference in retracing, between the healthy and unhealthy samples. The other model uses entropy of the Poincare plot to detect the difference in randomness of plots between the two classes. Most importantly, we have used only lead II ECG signal for analysis. This ensures ease of computation as it uses signal of only a single lead instead of the 12 leads of the complete ECG signal. We have validated the proposed models using ECG signals from the ‘ptb database’. We have observed that the entropy analysis of the Poincare plots gives the best results with 90% accuracy of abnormality detection. This high accuracy of classification, combined with less computational burden enables its practical implementation for the development of a real life abnormality detection schem