Critical Heart Condition Analysis through Diagnostic Agent of e-Healthcare System using Spectral Domain Transfom

Background/Objectives: As multi-agent system requirement is high in e-health care, this module for analysis and diagnosis of cardiac patients has been designed using transformation method in this work. Methods/Statistical Analysis: The baseline drift of the ECG has been eliminated and the beat rate is evaluated from the peaks of the signals using Fast Fourier Transform. It helps for analysis of the patients’ cardiac condition. Further for an adaptive window size, wavelet transform is utilized for both time and frequency component of a signal. This makes it as a potential technique for baseline drift removal and detection more accurate than FFT. Findings: FT analysis provides only the frequency component of any signal. The analysis will be more versatile and informative if both time and frequency information are investigated. For an adaptive window size, wavelet analysis can take care of both time and frequency component of a signal adequately. This makes it as a potential technique for this work. Subsequently an attempt is made to estimate the base line drift removal of the signal to obtain the main signal using wavelet decomposition. Next to it discrete wavelet transform have been applied to enrich the result. Application/Improvements: This can be well utilized in e-healthcare systems for the benefits of both patients and hospitals irrespective of distance. Also this module can be improved for other diseases.