The Centre's research activities relating to cardiac disease diagnosis are decribed below:
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The electrocardiogram (ECG) is a representative signal containing useful information about the condition of the heart. The shape and size of the P-QRS-T wave, the R-R interval etc. may help to identify the nature of disease afflicting the heart. However, human observer can not directly monitor these subtle details. The techniques developed to diagnose different cardiac diseases are shown below: |
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a) VISUALIZATION - A novel visualization technique for voluminous ECG data acquired over several hours is presented. The classified data is displayed in a sector graph, with a menu driven hierarchical display strategy, which progressively unfolds greater details for chosen intervals. A color code is employed to identify different types of abnormalities. Provision is made for fine- tuning the classification.
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24 hour ECG Visualization |
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b) FUSION - The fusion of ECG, blood pressure, saturated oxygen content and respiratory data for achieving improved clinical diagnosis of patients in cardiac care units. The software developed demonstrates the use of fuzzy logic based data fusion of the heterogeneous signals for the detection of life threatening cardiac states. |
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Snap Shot of cardiac health diagnosis software using data fusion concept |
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c) WAVELET - To study and pinpoint abnormalities in voluminous heart rate data collected over several hours is strenuous and time consuming. This work presents the continuous time wavelet analysis and Poincare plot of heart rate variability signal for disease identification. |
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Plot of Continuous Wavelet Transform of a Normal Subject
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Poincare plot of Normal subject
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 Plot of Continuous Wavelet Transform of a subject with heart disorder (SSS III)
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Poincare plot of SSS III subject
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d) NEURAL NETWORK – Eight cardiac diseases have been classified using neural network with an accuracy of more than 90%. The heart rate signals have been used as the base signals. |
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Neural Network with two hidden layers
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