Fast and Accurate Algorithm for ECG Authentication Using Residual Depthwise Separable Convolutional Neural Networks

Eko Ihsanto,Kalamullah Ramli,Dodi Sudiana,Teddy Surya Gunawan

doi:10.3390/app10093304

Abstract

The electrocardiogram (ECG) is relatively easy to acquire and has been used for reliable biometric authentication. Despite growing interest in ECG authentication, there are still two main problems that need to be tackled, i.e., the accuracy and processing speed. Therefore, this paper proposed a fast and accurate ECG authentication utilizing only two stages, i.e., ECG beat detection and classification. By minimizing time-consuming ECG signal pre-processing and feature extraction, our proposed two-stage algorithm can authenticate the ECG signal around 660 μs. Hamilton’s method was used for ECG beat detection, while the Residual Depthwise Separable Convolutional Neural Network (RDSCNN) algorithm was used for classification. It was found that between six and eight ECG beats were required for authentication of different databases. Results showed that our proposed algorithm achieved 100% accuracy when evaluated with 48 patients in the MIT-BIH database and 90 people in the ECG ID database. These results showed that our proposed algorithm outperformed other state-of-the-art methods.

Highlights

As early as 1977, electrocardiogram (ECG) was identified for its potential for biometric authentication [1]
Using principal components analysis (PCA) and soft independent modeling of class analogy (SIMCA), the ECG identification results vary from 90% to 100%
Instead, a PCA neural network (PCANet) was used, accuracies of 100% and

Summary

Introduction

As early as 1977, electrocardiogram (ECG) was identified for its potential for biometric authentication [1]. Many methods have been implemented of ECG identification using feature extraction based on time, amplitude, and frequency [3,4,5,6], as well as using machine learning [7,8,9]. Using principal components analysis (PCA) and soft independent modeling of class analogy (SIMCA), the ECG identification results vary from 90% to 100%. The accuracy of this method depends on two main factors, including the number of features and the type of lead used to obtain an ECG signal. In [3], they used 12 leads and 30 types of time-based ECG features.

Methods

Results

Conclusion