Extraction of ECG Features in Time and Frequency Domains

Electrocardiogram (ECG) analysis plays an important role in the diagnosis and monitoring of various cardiovascular conditions. An electrocardiogram records the electrical signals in the heart. It's a common and painless test used to quickly detect heart problems and monitor the heart's health. ECG is mainly generated by the SA node. The SA node, also known as the sinus node, represents a crescent-like shaped cluster of myositis divided by connective tissue, spreading over a few square millimeters. It is located at the junction of the crista terminals in the upper wall of the right atrium and the opening of the superior vena cava.

 An electrocardiogram is a painless, noninvasive way to help diagnose many common heart problems. A health care provider might use an electrocardiogram to determine or detect: irregular heart rhythms (arrhythmias), if blocked or narrowed arteries in the heart (coronary artery disease) are causing chest pain and a heart attack, whether you have had a previous heart attack, or how well certain heart disease treatments, such as a pacemaker, are working.

 The extraction of ECG features in both time and frequency domains have been widely studied to provide valuable insights into the disease diagnosis. This research aims to present a comprehensive review of the methods and techniques used for the extraction of ECG features in these domains.

 In the time domain, ECG features are derived from the temporal characteristics of the ECG waveform. Commonly extracted features include P,Q,R,S,T values, and ST segment morphology. These features provide valuable information about the regularity and conduction abnormalities of the heart. Various methods, such as peak detection algorithms, and statistical analysis, have been employed to accurately extract time-domain features.

 In the frequency domain, ECG signals are transformed into the spectral domain using techniques like the Fourier transform. The resulting frequency components can reveal abnormalities associated with arrhythmias, including changes in power distribution. Power spectral analysis, spectrogram analysis techniques are commonly used for extracting frequency-domain features.

 The extraction of ECG features in both the time and frequency domains offers valuable insights into the cardiac system's functioning and aids in the diagnosis and management of cardiovascular disorders, this research aims to contribute to the advancement of ECG analysis and foster future research in the field of cardiovascular medicine.