Individual identification via electrocardiogram analysis

Biometric Recognition: A Systematic Review on Electrocardiogram Data Acquisition Methods

In the last decades, researchers have shown the potential of using Electrocardiogram (ECG) as a biometric trait due to its uniqueness and hidden nature. However, despite the great number of approaches found in the literature, no agreement exists on the most appropriate methodology. This paper presents a systematic review of data acquisition methods, aiming to understand the impact of some variables from the data acquisition protocol of an ECG signal in the biometric identification process. We searched for papers on the subject using Scopus, defining several keywords and restrictions, and found a total of 121 papers. Data acquisition hardware and methods vary widely throughout the literature. We reviewed the intrusiveness of acquisitions, the number of leads used, and the duration of acquisitions. Moreover, by analyzing the literature, we can conclude that the preferable solutions include: (1) the use of off-the-person acquisitions as they bring ECG biometrics closer to viable, unconstrained applications; (2) the use of a one-lead setup; and (3) short-term acquisitions as they required fewer numbers of contact points, making the data acquisition of benefit to user acceptance and allow faster acquisitions, resulting in a user-friendly biometric system. Thus, this paper reviews data acquisition methods, summarizes multiple perspectives, and highlights existing challenges and problems. In contrast, most reviews on ECG-based biometrics focus on feature extraction and classification methods.

Bilateral Ear Acoustic Authentication: A Biometric Authentication System Using Both Ears and a Special Earphone

In existing biometric authentication methods, the user must perform an authentication operation such as placing a finger in a scanner or facing a camera. With ear acoustic authentication, the acoustic characteristics of the ear canal can be used as biometric information. Therefore, a person wearing earphones does not need to perform any authentication operation. Existing studies which use the acoustic characteristics of the ear canal as biometric information only measure the characteristics of one ear. However, the acoustic characteristics of the human ear canal can be measured from both ears. Hence, we proposed a new method for acoustic authentication based on the ability to measure the acoustic characteristics of the ear canal from both ears. The acoustic characteristics of the ear canal of 52 subjects were measured. Comparing the acoustic characteristics of the left and right ear canals, a difference in the signal between the left and right ear was observed. To evaluate the authentication accuracy, we calculated the evaluation indices of biometric authentication, equal error rate (EER), and area under curve (AUC). The EER for bilateral ear acoustic authentication using signals from both ears was 0.39%, which was lower than that of a single ear. The AUC was 0.0016 higher for bilateral ear acoustic authentication. Therefore, the use of bilateral signals for ear acoustic authentication was proved to be effective in improving authentication accuracy.

Electrical Impedance of Upper Limb Enables Robust Wearable Identity Recognition against Variation in Finger Placement and Environmental Factors

Most biometric authentication technologies commercialized in various fields mainly rely on acquired images of structural information, such as fingerprints, irises, and faces. However, bio-recognition techniques using these existing physical features are always at risk of template forgery threats, such as fake fingerprints. Due to the risk of theft and duplication, studies have recently been attempted using the internal structure and biological characteristics of the human body, including our previous works on the ratiometric biological impedance feature. However, one may still question its accuracy in real-life use due to the artifacts from sensing position variability and electrode-skin interfacing noise. Moreover, since the finger possesses more severe thermoregulatory vasomotion and large variability in the tissue properties than the core of the body, it is necessary to mitigate the harsh changes occurring at the peripheral extremities of the human body. To address these challenges, we propose a biometric authentication method through robust feature extraction from the upper-limb impedance acquired based on a portable wearable device. In this work, we show that the upper limb impedance features obtained from wearable devices are robust against undesirable factors such as finger placement deviations and day-to-day physiological changes, along with ratiometric impedance features. Overall, our upper-limb impedance-based analysis in a dataset of 1627 measurement from 33 subjects lowered the classification error rate from 22.38% to 4.3% (by a factor of 5), and further down to 2.4% (by a factor of 9) when combined with the ratiometric features.

Performance issues in wireless body area networks for the healthcare application: a survey and future prospects

This study presents a survey of the current issues, application areas, findings, and performance challenges in wireless body area networks (WBAN). The survey discusses selected areas in WBAN signal processing, network reliability, spectrum management, security, and WBAN integration with other technologies for highly efficient future healthcare applications. The foundation of the study bases on the recent growing advances in microelectronic technology and commercialization, which ease device availability, miniaturization, and communication. The survey considers a systemic review conducted using reports, standard documents, and peer-reviewed articles. Based on the comprehensive review, we find WBANs faces several operational, standardization, and security issues, affecting performance and maintenance of user safety and privacy. We envision the increasing dependency of future healthcare on WBAN for medical and non-medical applications due to internet connectivity advances. In this view, despite the WBAN advantages in remote health monitoring, further studies need to be conducted for performance optimization. Therefore we finalize our study by proposing various current and future research directions and open issues in WBAN’s performance enhancement.

Non-Segmented ECG bio-identification using Short Time Fourier Transform and Fréchet Mean Distance

In the recent years, the Electrocardiogram (ECG) based biometric identification has been a subject of considerable research interest. In this paper, we present non-fiducial method for ECG-identification using the short time Fourier transform (STFT), and Frechet mean distance-based algorithms to find the similarity between the STFTs of different people. In this study, we select randomly the training and test data of the ECG in order to test the stability of the method. We apply our proposed method on 124 ECG records of 62 subjects from the publicly available ECG ID database from physionet website. Our preliminary results indicate that the Frechet mean based ECG identification has 96.45% average identification accuracy and therefore can be potentially useful in various applications.

Interval-Based LDA Algorithm for Electrocardiograms for Individual Verification

This paper presents an interval-based LDA (Linear Discriminant Analysis) algorithm for individual verification using ECG (Electrocardiogram). In this algorithm, at first, unwanted noise and power-line interference are removed from the ECG signal. Then, the autocorrelation profile (ACP) of the ECG signal, which is a mathematical representation of the degree of similarity between a given time series and a lagged version of itself over successive time intervals, is calculated. Finally, the interval-based LDA algorithm is applied to extract unique individual feature vectors that represent distance and angle characteristics on short ACP segments. These feature vectors are used during the processes of enrollment and verification of individual identification. To validate our algorithm, we conducted experiments using the MIT-BIH ECG and achieved EERs (Equal Error Rate) of 0.143%, showing that the proposed algorithm is practically effective and robust in verifying the individual’s identity.

ECG Identification For Personal Authentication Using LSTM-Based Deep Recurrent Neural Networks

Securing personal authentication is an important study in the field of security. Particularly, fingerprinting and face recognition have been used for personal authentication. However, these systems suffer from certain issues, such as fingerprinting forgery, or environmental obstacles. To address forgery or spoofing identification problems, various approaches have been considered, including electrocardiogram (ECG). For ECG identification, linear discriminant analysis (LDA), support vector machine (SVM), principal component analysis (PCA), deep recurrent neural network (DRNN), and recurrent neural network (RNN) have been conventionally used. Certain studies have shown that the RNN model yields the best performance in ECG identification as compared with the other models. However, these methods require a lengthy input signal for high accuracy. Thus, these methods may not be applied to a real-time system. In this study, we propose using bidirectional long short-term memory (LSTM)-based deep recurrent neural networks (DRNN) through late-fusion to develop a real-time system for ECG-based biometrics identification and classification. We suggest a preprocessing procedure for the quick identification and noise reduction, such as a derivative filter, moving average filter, and normalization. We experimentally evaluated the proposed method using two public datasets: MIT-BIH Normal Sinus Rhythm (NSRDB) and MIT-BIH Arrhythmia (MITDB). The proposed LSTM-based DRNN model shows that in NSRDB, the overall precision was 100%, recall was 100%, accuracy was 100%, and F1-score was 1. For MITDB, the overall precision was 99.8%, recall was 99.8%, accuracy was 99.8%, and F1-score was 0.99. Our experiments demonstrate that the proposed model achieves an overall higher classification accuracy and efficiency compared with the conventional LSTM approach.

K, P.P. K, D.V. S. Autoencoder for ecg signal outlier processing in system of biometric authentication

A novel method for ECG signal outlier processing based on autoencoder neural networks is presented in the article. Typically, heartbeats with serious waveform distortions are treated as outliers and are skipped from the authentication pipeline. The main idea of the paper is to correct these waveform distortions rather them in order to provide the system with better statistical base. During the experiments, the optimum autoencoder architecture was selected. An open Physionet ECGID database was used to verify the proposed method. The results of the studies were compared with previous studies that considered the correction of anomalies based on a statistical approach. On the one hand, the autoencoder shows slightly lower accuracy than the statistical method, but it greatly simplifies the construction of biometric identification systems, since it does not require precise tuning of hyperparameters.

A Novel Electrocardiogram Biometric Identification Method Based on Temporal-Frequency Autoencoding

For good performance, most existing electrocardiogram (ECG) identification methods still need to adopt a denoising process to remove noise interference beforehand. This specific signal preprocessing technique requires great efforts for algorithm engineering and is usually complicated and time-consuming. To more conveniently remove the influence of noise interference and realize accurate identification, a novel temporal-frequency autoencoding based method is proposed. In particular, the raw data is firstly transformed into the wavelet domain, where multi-level time-frequency representation is achieved. Then, a prior knowledge-based feature selection is proposed and applied to the transformed data to discard noise components and retain identity-related information simultaneously. Afterward, the stacked sparse autoencoder is introduced to learn intrinsic discriminative features from the selected data, and Softmax classifier is used to perform the identification task. The effectiveness of the proposed method is evaluated on two public databases, namely, ECG-ID and Massachusetts Institute of Technology-Biotechnology arrhythmia (MIT-BIH-AHA) databases. Experimental results show that our method can achieve high multiple-heartbeat identification accuracies of 98.87%, 92.3%, and 96.82% on raw ECG signals which are from the ECG-ID (Two-recording), ECG-ID (All-recording), and MIT-BIH-AHA database, respectively, indicating that our method can provide an efficient way for ECG biometric identification.

ECG Signal as Robust and Reliable Biometric Marker: Datasets and Algorithms Comparison

In this paper, the possibility of using the ECG signal as an unequivocal biometric marker for authentication and identification purposes has been presented. Furthermore, since the ECG signal was acquired from 4 sources using different measurement equipment, electrodes positioning and number of patients as well as the duration of the ECG record acquisition, we have additionally provided an estimation of the extent of information available in the ECG record. To provide a more objective assessment of the credibility of the identification method, some selected machine learning algorithms were used in two combinations: with and without compression. The results that we have obtained confirm that the ECG signal can be acclaimed as a valid biometric marker that is very robust to hardware variations, noise and artifacts presence, that is stable over time and that is scalable across quite a solid (~100) number of users. Our experiments indicate that the most promising algorithms for ECG identification are LDA, KNN and MLP algorithms. Moreover, our results show that PCA compression, used as part of data preprocessing, does not only bring any noticeable benefits but in some cases might even reduce accuracy.

ECG-RNG: A Random Number Generator Based on ECG Signals and Suitable for Securing Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are a promising technology with applications in many areas such as environment monitoring, agriculture, the military field or health-care, to name but a few. Unfortunately, the wireless connectivity of the sensors opens doors to many security threats, and therefore, cryptographic solutions must be included on-board these devices and preferably in their design phase. In this vein, Random Number Generators (RNGs) play a critical role in security solutions such as authentication protocols or key-generation algorithms. In this article is proposed an avant-garde proposal based on the cardiac signal generator we carry with us (our heart), which can be recorded with medical or even low-cost sensors with wireless connectivity. In particular, for the extraction of random bits, a multi-level decomposition has been performed by wavelet analysis. The proposal has been tested with one of the largest and most publicly available datasets of electrocardiogram signals (202 subjects and 24 h of recording time). Regarding the assessment, the proposed True Random Number Generator (TRNG) has been tested with the most demanding batteries of statistical tests (ENT, DIEHARDERand NIST), and this has been completed with a bias, distinctiveness and performance analysis. From the analysis conducted, it can be concluded that the output stream of our proposed TRNG behaves as a random variable and is suitable for securing WSNs.

Perspectives of human verification via binary QRS template matching of single-lead and 12-lead electrocardiogram

Objective

This study aims to validate the 12-lead electrocardiogram (ECG) as a biometric modality based on two straightforward binary QRS template matching characteristics. Different perspectives of the human verification problem are considered, regarding the optimal lead selection and stability over sample size, gender, age, heart rate (HR).

Methods

A clinical 12-lead resting ECG database, including a population of 460 subjects with two-session recordings (>1 year apart) is used. Cost-effective strategies for extraction of personalized QRS patterns (100ms) and binary template matching estimate similarity in the time scale (matching time) and dissimilarity in the amplitude scale (mismatch area). The two-class person verification task, taking the decision to validate or to reject the subject identity is managed by linear discriminant analysis (LDA). Non-redundant LDA models for different lead configurations (I,II,III,aVF,aVL,aVF,V1-V6) are trained on the first half of 230 subjects by stepwise feature selection until maximization of the area under the receiver operating characteristic curve (ROC AUC). The operating point on the training ROC at equal error rate (EER) is tested on the independent dataset (second half of 230 subjects) to report unbiased validation of test-ROC AUC and true verification rate (TVR = 100-EER). The test results are further evaluated in groups by sample size, gender, age, HR.

Results and discussion

The optimal QRS pattern projection for single-lead ECG biometric modality is found in the frontal plane sector (60°-0°) with best (Test-AUC/TVR) for lead II (0.941/86.8%) and slight accuracy drop for -aVR (-0.017/-1.4%), I (-0.01/-1.5%). Chest ECG leads have degrading accuracy from V1 (0.885/80.6%) to V6 (0.799/71.8%). The multi-lead ECG improves verification: 6-chest (0.97/90.9%), 6-limb (0.986/94.3%), 12-leads (0.995/97.5%). The QRS pattern matching model shows stable performance for verification of 10 to 230 individuals; insignificant degradation of TVR in women by (1.2–3.6%), adults ≥70 years (3.7%), younger <40 years (1.9%), HR<60bpm (1.2%), HR>90bpm (3.9%), no degradation for HR change (0 to >20bpm).

Anti-deception: reliable EEG-based biometrics with real-time capability from the neural response of face rapid serial visual presentation

Background

The electroencephalogram (EEG) signal represents a subject’s specific brain activity patterns and is considered as an ideal biometric given its superior invisibility, non-clonality, and non-coercion. In order to enhance its applicability in identity authentication, a novel EEG-based identity authentication method is proposed based on self- or non-self-face rapid serial visual presentation.

Results

In contrast to previous studies that extracted EEG features from rest state or motor imagery, the designed paradigm could obtain a distinct and stable biometric trait with a lower time cost. Channel selection was applied to select specific channels for each user to enhance system portability and improve discriminability between users and imposters. Two different imposter scenarios were designed to test system security, which demonstrate the capability of anti-deception. Fifteen users and thirty imposters participated in the experiment. The mean authentication accuracy values for the two scenarios were 91.31 and 91.61%, with 6 s time cost, which illustrated the precision and real-time capability of the system. Furthermore, in order to estimate the repeatability and stability of our paradigm, another data acquisition session is conducted for each user. Using the classification models generated from the previous sessions, a mean false rejected rate of 7.27% has been achieved, which demonstrates the robustness of our paradigm.

Conclusions

Experimental results reveal that the proposed paradigm and methods are effective for EEG-based identity authentication.

Individual Biometric Identification Using Multi-Cycle Electrocardiographic Waveform Patterns

The electrocardiogram (ECG) waveform conveys information regarding the electrical property of the heart. The patterns vary depending on the individual heart characteristics. ECG features can be potentially used for biometric recognition. This study presents a new method using the entire ECG waveform pattern for matching and demonstrates that the approach can potentially be employed for individual biometric identification. Multi-cycle ECG signals were assessed using an ECG measuring circuit, and three electrodes can be patched on the wrists or fingers for considering various measurements. For biometric identification, our-fold cross validation was used in the experiments for assessing how the results of a statistical analysis will generalize to an independent data set. Four different pattern matching algorithms, i.e., cosine similarity, cross correlation, city block distance, and Euclidean distances, were tested to compare the individual identification performances with a single channel of ECG signal (3-wire ECG). To evaluate the pattern matching for biometric identification, the ECG recordings for each subject were partitioned into training and test set. The suggested method obtained a maximum performance of 89.9% accuracy with two heartbeats of ECG signals measured on the wrist and 93.3% accuracy with three heartbeats for 55 subjects. The performance rate with ECG signals measured on the fingers improved up to 99.3% with two heartbeats and 100% with three heartbeats of signals for 20 subjects.

Non-Uniform Sample Assignment in Training Set Improving Recognition of Hand Gestures Dominated with Similar Muscle Activities

So far, little is known how the sample assignment of surface electromyogram (sEMG) features in training set influences the recognition efficiency of hand gesture, and the aim of this study is to explore the impact of different sample arrangements in training set on the classification of hand gestures dominated with similar muscle activation patterns. Seven right-handed healthy subjects (24.2 ± 1.2 years) were recruited to perform similar grasping tasks (fist, spherical, and cylindrical grasping) and similar pinch tasks (finger, key, and tape pinch). Each task was sustained for 4 s and followed by a 5-s rest interval to avoid fatigue, and the procedure was repeated 60 times for every task. sEMG were recorded from six forearm hand muscles during grasping or pinch tasks, and 4-s sEMG from each channel was segmented for empirical mode decomposition analysis trial by trial. The muscle activity was quantified with zero crossing (ZC) and Wilson amplitude (WAMP) of the first four resulting intrinsic mode function. Thereafter, a sEMG feature vector was constructed with the ZC and WAMP of each channel sEMG, and a classifier combined with support vector machine and genetic algorithm was used for hand gesture recognition. The sample number for each hand gesture was designed to be rearranged according to different sample proportion in training set, and corresponding recognition rate was calculated to evaluate the effect of sample assignment change on gesture classification. Either for similar grasping or pinch tasks, the sample assignment change in training set affected the overall recognition rate of candidate hand gesture. Compare to conventional results with uniformly assigned training samples, the recognition rate of similar pinch gestures was significantly improved when the sample of finger-, key-, and tape-pinch gesture were assigned as 60, 20, and 20%, respectively. Similarly, the recognition rate of similar grasping gestures also rose when the sample proportion of fist, spherical, and cylindrical grasping was 40, 30, and 30%, respectively. Our results suggested that the recognition rate of hand gestures can be regulated by change sample arrangement in training set, which can be potentially used to improve fine-gesture recognition for myoelectric robotic hand exoskeleton control.

Modeling of sectionally continuous communication channel with inhomogeneously distributed tissues

BACKGROUND

This study aimed to investigate effects on the transmission channel caused by heterogeneous distribution in tissues and joint characteristics.

METHOD

Human arm section scans were taken using CT technology, and zoned, following which, a circumference measurement experiment was performed to analyze the effect of inhomogeneous distribution of tissues. In order to analyze the arm joint's effect on channel transmission, we proposed a piecewise modeling method in combination with connection conditions.

CONCLUSIONS

It can be seen from the experiment that, in the quasi-static mode, the communication channel error caused by the inhomogeneous distribution of tissues is small enough to be negligible. The error between calculated and experimental results is reduced by 3.93 dB in this experiment relative to models that did not include joint characteristics, and the average error is lowered by 0.73 dB. The variation curve fit to experimental data is also improved in this method. As such, it can be quantitatively determined that a channel model with joint characteristics is superior to models excluding joint characteristics.

Bivariate empirical mode decomposition for ECG-based biometric identification with emotional data

Emotions modulate ECG signals such that they might affect ECG-based biometric identification in real life application. It motivated in finding good feature extraction methods where the emotional state of the subjects has minimum impacts. This paper evaluates feature extraction based on bivariate empirical mode decomposition (BEMD) for biometric identification when emotion is considered. Using the ECG signal from the Mahnob-HCI database for affect recognition, the features were statistical distributions of dominant frequency after applying BEMD analysis to ECG signals. The achieved accuracy was 99.5% with high consistency using kNN classifier in 10-fold cross validation to identify 26 subjects when the emotional states of the subjects were ignored. When the emotional states of the subject were considered, the proposed method also delivered high accuracy, around 99.4%. We concluded that the proposed method offers emotion-independent features for ECG-based biometric identification. The proposed method needs more evaluation related to testing with other classifier and variation in ECG signals, e.g. normal ECG vs. ECG with arrhythmias, ECG from various ages, and ECG from other affective databases.