  This paper describe the features extraction algorithm for electrocardiogram
(ECG) signal using Huang Hilbert Transform and Wavelet Transform. ECG signal
for an individual human being is different due to unique heart structure. The
purpose of feature extraction of ECG signal would allow successful abnormality
detection and efficient prognosis due to heart disorder. Some major important
features will be extracted from ECG signals such as amplitude, duration,
pre-gradient, post-gradient and so on. Therefore, we need a strong mathematical
model to extract such useful parameter. Here an adaptive mathematical analysis
model is Hilbert-Huang transform (HHT). This new approach, the Hilbert-Huang
transform, is implemented to analyze the non-linear and nonstationary data. It
is unique and different from the existing methods of data analysis and does not
require an a priori functional basis. The effectiveness of the proposed scheme
is verified through the simulation.
