Resumen
This study concerns personal identification based on electrocardiogram (ECG) and photoplethysmogram (PPG) signals. We manufactured a bio-signal measurement system that can simultaneously measure ECG and PPG signals, using which three channels of ECG signal and one channel of PPG signal were acquired from the right-hand index finger of a total of 33 subsets for 3 minutes. Lead-I signal of the three-channel ECG signal and the one-channel PPG signal were selected for recognition. For each subject, 160 heartbeats were automatically separated from the acquired bio-signals, and a total of 21 features, comprising 15 ECG features, 4 ECG-related PPG features, and 2 features concerning PPG only, were extracted from each heartbeat. Letting the 21 features form a single data point, heartbeat features of each subset were used as the training data for a support vector machine (SVM) classifier, with the number of data points being adjusted from 10 to 80, and the data points (80 - 150) other than the training data were used as the testing data, in order to investigate the recognition performance indices. As a result, the proposed algorithm showed high recognition performance of 99.28% accuracy, 0.88% FRR, 0.85% FAR, 99.28% sensitivity, and 99.31% specificity, when there are 80 training data points. Moreover, even when there are 10 training data points, the proposed algorithm showed the performance of 92.77% accuracy, 7.23% FRR, 6.29% FAR, 92.77% sensitivity, and 93.21% specificity, which can be evaluated as an extremely high recognition performance considering that there was a total of 4,950 testing data points.