Hardware-efficient robust biometric identification from 0.58 second template and 12 features of limb (Lead I) ECG signal using logistic regression classifier

Abstract

The electrocardiogram (ECG), widely known as a cardiac diagnostic signal, has recently been proposed for biometric identification of individuals; however reliability and reproducibility are of research interest. In this paper, we propose a template matching technique with 12 features using logistic regression classifier that achieved high reliability and identification accuracy. Non-invasive ECG signals were captured using our custom-built ambulatory EEG/ECG embedded device (NeuroMonitor). ECG data were collected from healthy subjects (10), between 25-35 years, for 10 seconds per trial. The number of trials from each subject was 10. From each trial, only 0.58 seconds of Lead I ECG data were used as template. Hardware-efficient fiducial point detection technique was implemented for feature extraction. To obtain repeated random sub-sampling validation, data were randomly separated into training and testing sets at a ratio of 80:20. Test data were used to find the classification accuracy. ECG template data with 12 extracted features provided the best performance in terms of accuracy (up to 100%) and processing complexity (computation time of 1.2ms). This work shows that a single limb (Lead I) ECG can robustly identify an individual quickly and reliably with minimal contact and data processing using the proposed algorithm.

NeuroMonitor: a low‐power, wireless, wearable EEG device with DRL‐less AFE

“…Moreover it depicts significant variation of results among different subjects, since no two people are identical in terms of brain activities. From the study shown here, and other related study reported elsewhere [19,[21][22][23][24], the functionality and usability of the small and wearable NeuroMonitor device has been demonstrated for neuro-physiological data acquisition and applications.…”

“…The device was configured for two channel referential montage with online mode. PSD of Alpha (8-13 Hz), Beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and Theta (4-8 Hz) rhythms from FP2 location are plotted in Fig. 8 for four subjects (denoted with different colours).…”

“…In this paper, we present a new design of the analogue front end (AFE) with a unipolar battery-operated, small, low-power, wearable ambulatory EEG data collection device, referred herein as 'NeuroMonitor'. We achieved the independent channel design of EEG device by eliminating the DRL circuit using our new AFE, and replacing DRL electrode with the reference (mid-rail) of the circuit [19][20][21][22][23][24]. DRL-less EEG systems can be designed for each channel independently without any reliance of the total number of EEG channels to be deployed.…”

New Heart Features for More Effective Human Identification

“…This recording method is referred to as lead-1 ECG configuration recording. It is the most practical ECG recording method for biometric applications [28]. MD100E handheld ECG machine as shown in Figure 3 is used to record the ECG signal in our study.…”

Fusing depth local dual-view features and dual-input transformer framework for improving the recognition ability of motion artifact-contaminated electrocardiogram

“…To evaluate the performance of the DLDV features extracted by our method, we employ four traditional classifiers (Gaussian Kernel Support Vector Machines (G-SVM) [43], Logistic Regression (LR) [44], Random Forests (RF) [45], and K-Nearest Neighbors (KNN) [46], and the parameter settings of each classifier are shown in Table 3) and six time-frequency dependent SQIs [10,47,48], such as sSQI and kSQI, pSQI, LpSQI, MpSQI, HpSQI. Table 4 shows the binary classification results of ECG signal quality using Fig.…”