ECG-Based Biometrics Using a Deep Network Based on Independent Component Analysis

In this study, we propose a systematic deep network based on independent component analysis (ICA) called ICANet1, which is subsequently modified into an improved model called ICANet2. The existing principal component analysis network (PCANet) has a smaller computational complexity and is faster than deep learning. However, any modifications in the data lower the performance, which is a limitation. The ICANet algorithm has been proposed to solve this problem. Although ICANet2 does not use eigenvectors, it uses PCA feature vectors. To eliminate the correlation between the PCA feature vectors, the ICA algorithm is used to determine those that are statistically independent. The feature values obtained from the final histogram are vectorized and used as input to the classifier. On using ICANet2, the performance achieved is lower than that of deep learning; however, it is expected to be superior with respect to the recognition speed, especially for mobile devices. The classifier performance is demonstrated using extreme learning machine (ELM), artificial neural network (ANN), k-nearest Neighbor (KNN), and support vector machines (SVM). We use the MIT-ECG, Chosun University-electrocardiogram (CU-ECG), and noise ECG databases to verify the performance of the proposed method. Both ICANet1 and ICANet2 demonstrate better experimental performance than PCANet because the ECG noise data are affected by time. In addition, when noise ECG data are used, the ICANets achieved a better performance than PCANet, further proving its validity.

Automated summary

The study introduces a deep network based on ICA called ICANet1, which is later improved into ICANet2. ICANet2 uses the ICA algorithm to address the issue of decreased performance when data is modified. While it performs lower than deep learning, it is expected to excel in recognition speed, especially for mobile devices.