An Efficient Optimized Feature Selection with Machine Learning Approach for ECG Biometric Recognition

Abstract

In machine learning, an efficient classifier model design is mostly based on effective feature extraction and appropriate feature selection. This work mainly focused on different optimized feature selection algorithms for automatic biometric recognition system with the use of electrocardiogram (ECG) signals. Initially, the features are extracted from P-QRS-T segments of the ECG signal with position normalization. The extracted features are processed through different optimization algorithms for quality assessment prior to the classification stage. In this work, two different methods are proposed based on wrapper feature selection and embedded feature selection to get optimized feature datasets for the perfect identification of human biometrics. In the first proposed method, wrapper-oriented feature selection is implemented with genetic algorithm (GA) and particle swarm optimization. In the second proposed method, the embedded method is included as the least absolute shrinkage selection operator and elastic net (EN). The processed optimized feature vectors from the optimization phase are fed to popular machine learning techniques, such as support vector machine and random forest (RF) classifiers, for automatic biometric human recognition. Classifier performances are investigated using two publicly available open-source databases, Electrocardiogram identification and large Physikalisch-Technische Bundesanstalt diagnostic database with a dynamic number of subjects. The experimental results indicate that the proposed combination of feature selection with machine learning algorithms has enhanced the classification accuracy. Finally, GA and EN with RF classifier provide improved recognition rates of 95.30% and 94.90%, respectively.

KEYWORDS:

• Biometric
• Electrocardiogram (ECG)
• ECG ID database
• Embedded feature selection
• Random forest (RF)
• Support vector machine (SVM)
• Wrapper feature selection

Additional information

Notes on contributors

Kiran Kumar Patro

Kiran Kumar Patro received his ME and PhD degree from the Department of Electronics and Communication Engineering, Andhra University, Visakhapatnam. His research interests include biomedical signal processing, image processing, and machine learning. He is currently working as an assistant professor in the Department of ECE, Aditya Institute of Technology and Management.

Allam Jaya Prakash

Allam Jaya Prakash received his MTech degree from the Department of Electronics and Communication Engineering, Jawaharlal Nehru Technological University, Kakinada. His research areas include ECG signal processing, pattern recognition, and machine learning. He is currently working as a research fellow in the Department of ECE, National Institute of Technology, Rourkela, Odisha, India. E-mail: [email protected]

M. Jayamanmadha Rao

M Jayamanmadha Rao received his MTech and PhD degree from the Department of Electronics and Communication Engineering, Andhra University, Visakhapatnam. His research areas include image processing and signal processing. He is currently working as a professor in the Department of ECE, Aditya Institute of Technology and Management. E-mail: [email protected]

P. Rajesh Kumar

P Rajesh Kumar is a professor in Electronics and Communication Engineering Department, College of Engineering, Andhra University, Visakhapatnam, India. He received his ME and a PhD degree from Andhra University. He has published more than hundred research papers in national and international journals; his research interests are in the areas of digital image processing, digital signal processing, radar signal processing, and biomedical signal processing. E-mail: [email protected]