A Review on: ECG Signal Analysis Using Artificial Neural Network

Abstract: The most difficult task in cardiovascular disease diagnosis is doing precise electrocardiogram analyses (ECG). In order to classify and interpret ECG data automatically, numerous studies are being carried out. Wavelet Packet Decomposition (WPD) and Feature extraction are used to decompose the patient's raw ECG data. Artificial Neural Networks (ANNs) are used to refine the classification (ANN). A unique approach for the automatic analysis of ECG data has been developed that successfully distinguishes between abnormal and normal ECG signals. This bio-electrical signal is used to record the heart's electrical activity over time, and is known as an electrocardiogram (ECG). Detecting cardiac illness early and accurately is critical to a patient's recovery and treatment. Cardiac illnesses are detected using ECG signals, which are obtained mostly from PhysioDataNet and the MIT-BIH database. Wavelet toolbox is used for pre-processing the ECG signal and also for feature extraction from the ECG signal. The whole project may be done in MATLAB. The algorithm's efficiency is measured using the MIT�BIH Database. In this paper we are reviewing technique of ECG signal analysis and classification.

Keywords: ECG, ECG signal analysis, ECG Signal Classification, ANN