ECG Based Biometric for Human Identification using Convolutional Neural Network

Bajare, Shraddha R.; Ingale, Vaishali

doi:10.1109/icccnt45670.2019.8944895

Cited by 7 publications

(7 citation statements)

References 14 publications

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“…The biometric system can be based on physiological characteristics, such as fingerprint (Bansal, Sehgal, & Bedi, 2011), iris (Bowyer, Hollingsworth, & Flynn, 2008 and hand veins (Sarkar, Alisherov, Kim, & Bhattacharyya, 2010) or behavioural traits, such as keystroke dynamics (Monrose & Rubin, 2000) and signature (Hafemann, Sabourin, & Oliveira, 2017). The ECG was found applicable as a biometric trait for human identification (Biel, Pettersson, Philipson, & Wide, 2001;Irvine, Israel, Wiederhold, & Wiederhold, 2003) and it falls under physiological characteristics (Bajare & Ingale, 2019). The features extracted from ECG signals were found to be by Tan et al (Tan et al, 2018).…”

Section: Hybrid Of DL Techniquesmentioning

confidence: 99%

“…The former involves describing the peaks, boundary and intervals of P, the QRS and T waves and the main three ECG waves. These methods depend on ECG signals segmentation and feature engineering that makes their generalization ability poor and inefficient (Bajare & Ingale, 2019). The latter instead of applying fiducial localization, which is computational cost, the complete signal processing is applied.…”

Section: Hybrid Of DL Techniquesmentioning

confidence: 99%

“…However, the study suggested employing multi-session scenario to improve the model performance. A study byBajare and Ingale (Bajare & Ingale, 2019) used 1D-CNN model for human identification. The…”

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confidence: 99%

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A systematic review and Meta-data analysis on the applications of Deep Learning in Electrocardiogram

Musa

Gital

Aljojo

et al. 2022

J Ambient Intell Human Comput

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The success of deep learning over the traditional machine learning techniques in handling artificial intelligence application tasks such as image processing, computer vision, object detection, speech recognition, medical imaging and so on, has made deep learning the buzz word that dominates Artificial Intelligence applications. From the last decade, the applications of deep learning in physiological signals such as electrocardiogram (ECG) have attracted a good number of research. However, previous surveys have not been able to provide a systematic comprehensive review including biometric ECG based systems of the applications of deep learning in ECG with respect to domain of applications. To address this gap, we conducted a systematic literature review on the applications of deep learning in ECG including biometric ECG based systems. The study analyzed systematically, 150 primary studies with evidence of the application of deep learning in ECG.The study shows that the applications of deep learning in ECG have been applied in different domains. We presented a new taxonomy of the domains of application of the deep learning in ECG. The paper also presented discussions on biometric ECG based systems and meta-data analysis of the studies based on the domain, area, task, deep learning models, dataset sources and preprocessing methods. Challenges and potential research opportunities were highlighted to enable novel research. We believe that this study will be useful to both new researchers and expert researchers who are seeking to add knowledge to the already existing body of knowledge in ECG signal processing using deep learning algorithm.

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Section: Hybrid Of DL Techniquesmentioning

confidence: 99%

Section: Hybrid Of DL Techniquesmentioning

confidence: 99%

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A systematic review and Meta-data analysis on the applications of Deep Learning in Electrocardiogram

Musa

Gital

Aljojo

et al. 2022

J Ambient Intell Human Comput

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“…The detection rate from two datasets was 98.4% (one-arm ECG) or 91.1% (two-arm ECG) (ear ECG). A deep learning-based 1D-CNN was used in this [ 22 ] attempt to categorize ECG data for biometrics. The NSRDB and ECG-ID [ 4 ] databases are used in the experiments.…”

Section: Related Workmentioning

confidence: 99%

“…These results are summarized in Table 11. In [22], that work, signals are decomposed to the multilevel for analysis and then 1D-CNN is applied to the ECG-ID dataset with 10 subjects containing two records and NSRDB [4] with 18 subjects. In [56], the MIT-BIH arrhythmia database as well as the delayed long short-term memory (DLSTM) were used to predict heartbeats from five classes.…”

Section: Ablation Analysismentioning

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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