An LSTM-Based Model for Person Identification Using ECG Signal
Debasish Jyotishi, Samarendra Dandapat
Abstract
Electrocardiogram (ECG)-based biometrics are gaining popularity because of its robustness against falsification. In this letter, we have designed a new long short-term memory (LSTM) based framework for person identification using ECG signal. Our model learns the underlying temporal representation of an ECG signal by considering both intra-beat and inter-beat variations. We have shown that the LSTM model captures the intra-beat variations better for smaller ECG segments. Unlike most of the models in the literature, our model doesn't require the detection of any fiducial points. This is achieved by training the LSTM network with smaller segments of ECG signals, which are extracted by sliding a rectangular window. The effect of different window length on person identification accuracy is studied. The efficiency of the model is extensively tested on four databases; PTB, MIT-BIH arrhythmia database, ECG-ID, and CYBHi. An accuracy of 97.3% is obtained for 290 subjects of the PTB database. Similar results are also obtained for other databases, as well as 79.37% accuracy for CYBHi.