ECG signals for human identification based on fiducial and non-fiducial approaches

Ibrahim, Anwar E.; Abdel-Mageid, Salah; Nada, Nadra; Elshahed, Marwa A.

doi:10.19101/ijacr.2019.940129

Cited by 11 publications

(3 citation statements)

References 7 publications

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“…The accuracy on the MIT-BIH arrhythmia [ 5 ], PTB STDB and NSRDB [ 4 ], respectively, was 99.5%, 98.1%, 98.2% and 100%. Methods for feature extraction including such a Dwt wavelet (‘db8’, ‘db3’ and ‘db10’), Biorthogonal wavelet ‘bior2.6’ and symlets wavelet ‘sym7’ are used in [ 43 ] this work. A backpropagation (BP) neural network and radial basis function neural network are combined to serve as a classifier.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Deep Learning and Discrete Wavelet Transform-Based ECG Biometric Recognition for Arrhythmic Patients and Healthy Controls

Asif

Faisal

Dar

et al. 2023

Sensors

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The intrinsic and liveness detection behavior of electrocardiogram (ECG) signals has made it an emerging biometric modality for the researcher with several applications including forensic, surveillance and security. The main challenge is the low recognition performance with datasets of large populations, including healthy and heart-disease patients, with a short interval of an ECG signal. This research proposes a novel method with the feature-level fusion of the discrete wavelet transform and a one-dimensional convolutional recurrent neural network (1D-CRNN). ECG signals were preprocessed by removing high-frequency powerline interference, followed by a low-pass filter with a cutoff frequency of 1.5 Hz for physiological noises and by baseline drift removal. The preprocessed signal is segmented with PQRST peaks, while the segmented signals are passed through Coiflets’ 5 Discrete Wavelet Transform for conventional feature extraction. The 1D-CRNN with two long short-term memory (LSTM) layers followed by three 1D convolutional layers was applied for deep learning-based feature extraction. These combinations of features result in biometric recognition accuracies of 80.64%, 98.81% and 99.62% for the ECG-ID, MIT-BIH and NSR-DB datasets, respectively. At the same time, 98.24% is achieved when combining all of these datasets. This research also compares conventional feature extraction, deep learning-based feature extraction and a combination of these for performance enhancement, compared to transfer learning approaches such as VGG-19, ResNet-152 and Inception-v3 with a small segment of ECG data.

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Section: Related Workmentioning

confidence: 99%

Hybrid Deep Learning and Discrete Wavelet Transform-Based ECG Biometric Recognition for Arrhythmic Patients and Healthy Controls

Asif

Faisal

Dar

et al. 2023

Sensors

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“…Among the studies considering some heart rate, many previous investigations resampled the entire ECG waveform by substituting the RR-interval into a linear equation. Additionally, it is usually used to remove outlier beats by analyzing the correlation between a representative waveform and other waveforms [20][21][22][23][24]. Arteaga-Falconi et al [21] derived a normalization technique that divides the duration of the P wave, QRS complex, and T wave of the ECG by the duration of the RR-interval.…”

Section: Introductionmentioning

confidence: 99%

ECG Authentication Based on Non-Linear Normalization under Various Physiological Conditions

Hwang

Kwon

Chung

et al. 2021

Sensors

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The development and use of wearable devices require high levels of security and have sparked interest in biometric authentication research. Among the available approaches, electrocardiogram (ECG) technology is attracting attention because of its strengths in spoofing. However, morphological changes of ECG, which are affected by physical and psychological factors, can make authentication difficult. In this paper, we propose authentication using non-linear normalization of ECG beats that is robust to changes in ECG waveforms according to heart rate fluctuations in various daily activities. We performed a non-linear normalization method through the analysis of ECG alongside heart rate, evaluating similarities and authenticating the performance of our new method compared to existing methods. Compared with beats before normalization, the average similarity of the proposed method increased 23.7% in the resting state and 43% in the non-resting state. After learning in the resting state, authentication performance reached 99.05% accuracy for the resting state and 88.14% for the non-resting state. The proposed method can be applicable to an ECG-based authentication system under various physiological conditions.

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“…voice, electrocardiography [ECG], electroencephalography [EEG], photoplethysmography [PPG], etc. ), [9][10][11][12][13] behavior-based determined by the way of acting or the way of using a device (e.g. gait, keystroke, way of writing, etc.…”

Section: Introductionmentioning

confidence: 99%

Biosignal classification for human identification based on convolutional neural networks

Siam

Sedik

El‐Shafai

et al. 2021

Int J Communication

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Human identification is considered as a serious challenge for several applications such as cybersecurity and access control. Recently, the trend of human identification has been directed to human biometrics, which can be used to recognize persons based on some physiological or behavioral characteristics that they own, such as fingerprint, iris, and biosignals. There are several types of human biosignals including electroencephalography (EEG), electrocardiography (ECG), and photoplethysmography (PPG) signals. This paper presents a human identification system based on PPG signals. The proposed system consists of three main phases: signal acquisition, signal pre‐processing, and feature extraction/classification. The pre‐processing phase involves denoising of the acquired signal, transformation of the 1D signal sequence into a 2D image, and computation of the spectrogram. Feature extraction is carried out on the images obtained from the pre‐processing phase. Features are extracted from the images based on convolutional neural networks (CNNs). The proposed CNN model consists of a sequence of convolutional (CNV) and pooling layers. Finally, the obtained feature maps are fed to the classifier to discriminate human identities. The proposed identification algorithm is applied on signals with and without an additive white Gaussian noise (AWGN). The simulation results reveal that the proposed algorithm achieves an accuracy of 99.5% with the spectrogram representation and 89.8% with the 2D image representation, in the absence of noise. In addition, the paper gives a discussion of the efficiency of denoising techniques such as wavelet denoising, Savitzky–Golay and Kalman filtering, when involved with the proposed algorithm. The simulation results prove that the wavelet dencoising technique has a best performance among the discussed noise reduction techniques.

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ECG signals for human identification based on fiducial and non-fiducial approaches

Cited by 11 publications

References 7 publications

Hybrid Deep Learning and Discrete Wavelet Transform-Based ECG Biometric Recognition for Arrhythmic Patients and Healthy Controls

Hybrid Deep Learning and Discrete Wavelet Transform-Based ECG Biometric Recognition for Arrhythmic Patients and Healthy Controls

ECG Authentication Based on Non-Linear Normalization under Various Physiological Conditions

Biosignal classification for human identification based on convolutional neural networks

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