Personal Identification Using an Ensemble Approach of 1D-LSTM and 2D-CNN with Electrocardiogram Signals

Abstract

Conventional personal identification methods (ID, password, authorization certificate, etc.) entail various issues, including forgery or loss. Technological advances and the diffusion across industries have enhanced convenience; however, privacy risks due to security attacks are increasing. Hence, personal identification based on biometrics such as the face, iris, fingerprints, and veins has been used widely. However, biometric information including faces and fingerprints is difficult to apply in industries requiring high-level security, owing to tampering or forgery risks and recognition errors. This paper proposes a personal identification technique based on an ensemble of long short-term memory (LSTM) and convolutional neural network (CNN) that uses electrocardiograms (ECGs). An ECG uses internal biometric information, representing the heart rate in signals using microcurrents and thereby including noises during measurements. This noise is removed using filters in a preprocessing step, and the signals are divided into cycles with respect to R-peaks for extracting features. LSTM is used to perform personal identification using ECG signals; 1D ECG signals are transformed into the time–frequency domain using STFT, scalogram, FSST, and WSST; and a 2D-CNN is used to perform personal identification. This ensemble of two models is used to attain higher performances than LSTM or 2D-CNN. Results reveal a performance improvement of 1.06–3.75%.