ECG data optimization for biometric human recognition using statistical distributed machine learning algorithm

Currently, security plays a crucial role in military, forensic and other industry applications. Traditional biometric authentication methods such as fingerprint, voice, face, iris and signature may not meet the demand for higher security. At present, the utility of biological signals in the area of security became popular. ECG signal is getting wide attention to use it as a tool for biometric recognition in authentication applications. ECG signals can provide more accurate biometrics for personal identity recognition. In machine learning, over-fitting is one of the major problems when working with a large data set of features so that an effective statistical technique is needed to control it. In this research, ECG signals are acquired from 20 individuals over 6 months in the MIT-BIH ECG-ID database. Altogether, a high-dimensional (N = 72) set of ECG features are extracted. These features are further fed to an algorithm, which reduces the feature space by classifying vital features and avoiding random, correlated and over-fitted features to increase the prediction accuracy. In this paper, a new intelligent statistical learning method, namely least absolute shrinkage and selection operator (LASSO), is proposed to select appropriate features for identification. The refined features thus obtained are trained with popular machine learning algorithms such as artificial neural networks, multi-class one-against-all support vector machine and K-nearest neighbour (K-NN). Finally, the performance of the proposed method with and without LASSO is compared using performance metrics. From the experimental results, it is observed that the proposed method of LASSO with K-NN classifier is effective with a recognition accuracy of 99.1379%.