Feature extraction of electrocardiogram signal using machine learning classification

In the various field of life person identification is an essential and important task. This helps for the investigation of criminal activities and used in various type of forensic applications like surveillance. For biometric recognition iris, face, voice and fingerprint have a limited fabrication and from there the exact decision regarding liveliness of the subject can be drawn. The aim of the approach is to construct a biometric recognition system based on ECG which processes the raw ECG signal. The entire process is supported by different filters for noise elimination and ECG characteristics waves gone through time domain analysis. Based on the analysis an efficient feature extraction model is developed where several best P-QRS-T signal parts are taken and the positions of the fragmented signals are normalized depends on the priorities of their positions. The calculation of domain features done 72 times. It checks the data sets (train and test) and from feature vector matching to each of the individual signal, separately. The performance and utility of the system are analyzed and feature vectors are examined by different classification algorithms of machine learning. The leading algorithms like K-nearest neighbor, artificial neural network and support vector machine are used to classify different features of ECG, and it is tested using standard cardiac database i.e. the MIT-BIH ECG -ID database