Deep Convolutional Neural Network for ECG-based Human Identification
AbstractIn this work, a deep convolutional neural network (CNN) is developed to identify individuals using their electrocardiogram (ECG) signals that are collected by OMsignal apparel from 33 women while doing their daily activities. The signature windows including 10 consecutive heart beats are extracted from the filtered ECG signal to be applied to the CNN model. The network performance is evaluated on validation and testing data sets. On validation and testing data sets created from different recordings of the same participants, an overall window accuracy of 95.25% and 95.95% are respectively achieved. Using majority voting classification across all collected windows, 100% of the participants with more than five ECG daily recordings are correctly identified. One of the main advantages of this work besides high accuracy, is to simplify the feature extraction process and to remove the need for extracting hand-crafted features unlike coventional methods available in the literature.
How to Cite
B. Pourbabaee, M. Howe-Patterson, E. Reiher, and F. Benard, “Deep Convolutional Neural Network for ECG-based Human Identification”, CMBES, vol. 41, May 2018.