Vol. 3 Issue 2
Year:2015
Issue:Apr-Jun
Title:ST Interval Measurement Using HT, PCA And ICA
Author Name:S. Thulasi prasad and S. Varadarajan
Synopsis:
The cardiovascular diseases such as Arrhythmia and Myocardial Infarction are becoming more alarming in causing heart attacks. The early detection of cardiac related deceases has become an essential activity to save a patient from death. For detecting these cardiovascular diseases useful information is hidden in the ECG waves, it have to be extracted from the ECG signal. In this paper the authors used the Hilbert Transform (HT), Principle Component Analysis (PCA) and Independent Component Analysis (ICA). The Hilbert Transform is useful in providing good resolutions to the ECG and it is able to easily interpret the unknown difficulties in the ECG. The Principal Component Analysis (PCA) and Independent Component Analysis (ICA) were independently applied on Hilbert Transformed ECG signal to enhance the hidden complexities in ECG signal by eliminating non-Gaussian noise elements. Latter the suitable algorithms were applied to detect the fiducial points such as PQRST and perform statistical analysis on ST Interval variability. The authors noticed that the ICA has better performance than the PCA.
Year:2015
Issue:Apr-Jun
Title:ST Interval Measurement Using HT, PCA And ICA
Author Name:S. Thulasi prasad and S. Varadarajan
Synopsis:
The cardiovascular diseases such as Arrhythmia and Myocardial Infarction are becoming more alarming in causing heart attacks. The early detection of cardiac related deceases has become an essential activity to save a patient from death. For detecting these cardiovascular diseases useful information is hidden in the ECG waves, it have to be extracted from the ECG signal. In this paper the authors used the Hilbert Transform (HT), Principle Component Analysis (PCA) and Independent Component Analysis (ICA). The Hilbert Transform is useful in providing good resolutions to the ECG and it is able to easily interpret the unknown difficulties in the ECG. The Principal Component Analysis (PCA) and Independent Component Analysis (ICA) were independently applied on Hilbert Transformed ECG signal to enhance the hidden complexities in ECG signal by eliminating non-Gaussian noise elements. Latter the suitable algorithms were applied to detect the fiducial points such as PQRST and perform statistical analysis on ST Interval variability. The authors noticed that the ICA has better performance than the PCA.
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