ResNet1D-Based Personal Identification with Multi-Session Surface Electromyography for Electronic Health Record Integration.

Personal identification is an important aspect of managing electronic health records (EHRs), ensuring secure access to patient information, and maintaining patient privacy. Traditionally, biometric, signature, username/password, photo identity, etc., are employed for user authentication.

User identification system based on 2D CQT spectrogram of EMG with adaptive frequency resolution adjustment.

User identification systems based on electromyogram (EMG) signals, generated inside the body in different signal patterns and exhibiting individual characteristics based on muscle development and activity, are being actively researched. However, nonlinear and abnormal signals constrain conventional user identification using EMG signals in improving accuracy by using the 1-D feature from each time and frequency domain. Therefore, multidimensional features containing time-frequency information extracted from EMG signals have attracted much attention to improving identification accuracy.

Intelligent Deep Models Based on Scalograms of Electrocardiogram Signals for Biometrics.

This paper conducts a comparative analysis of deep models in biometrics using scalogram of electrocardiogram (ECG). A scalogram is the absolute value of the continuous wavelet transform coefficients of a signal. Since biometrics using ECG signals are sensitive to noise, studies have been conducted by transforming signals into a frequency domain that is efficient for analyzing noisy signals.

An EigenECG Network Approach Based on PCANet for Personal Identification from ECG Signal.

We herein propose an EigenECG Network (EECGNet) based on the principal component analysis network (PCANet) for the personal identification of electrocardiogram (ECG) from human biosignal data. The EECGNet consists of three stages. In the first stage, ECG signals are preprocessed by normalization and spike removal.