ECG Based Biometric Human Identification Using Convolutional Neural Network in Smart Health Applications

Deshmane, Madhura; Madhe, Swati

doi:10.1109/iccubea.2018.8697579

Cited by 17 publications

(10 citation statements)

References 14 publications

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“…These challenges remain open issues (Abdeldayem & Bourlai, 2019;Carreiras, Lourenço, Silva, Fred, & Ferreira, 2016;Pinto et al, 2018). These psycho-physiological factors should be considered during the acquisition of ECG A study by (Deshmane & Madhe, 2018) proposed 1D-CNN model for human identification and tested the model over four databases. The performance accuracies of 81.33% (MITDB), 96.95% (Fantasia database), 94.73% (NSRDB) and 92.85% (QT database) were obtained.…”

Section: Hybrid Of DL Techniquesmentioning

confidence: 99%

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%

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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“…Salloum and Kuo [10] used recurrent neural networks (RNNs) with long short-term memory (LSTM) and gated recurrent units (GRUs), reaching a 100% identification rate on 90 subjects from the public ECG-ID database. Deshmane and Madhe [11] proposed a CNN-based approach that achieved 81.33%, 96.95%, 94.73% and 92.85% accuracies on the MITBIH (47 subjects), FANTASIA (40 subjects), NSRDB (18 subjects) and QT databases (105 subjects), respectively. Eduardo et al [12] used autoencoders for denoising and feature extraction in an ECG biometric system.…”

Section: Literature Reviewmentioning

confidence: 99%

Assessing the Reidentification Risks Posed by Deep Learning Algorithms Applied to ECG Data

et al. 2022

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ECG (Electrocardiogram) data analysis is one of the most widely used and important tools in cardiology diagnostics. In recent years the development of advanced deep learning techniques and GPU hardware have made it possible to train neural network models that attain exceptionally high levels of accuracy in complex tasks such as heart disease diagnoses and treatments. We investigate the use of ECGs as biometrics in human identification systems by implementing state-of-the-art deep learning models. We train convolutional neural network models on approximately 81k patients from the US, Germany and China. Currently, this is the largest research project on ECG identification. Our models achieved an overall accuracy of 95.69%. Furthermore, we assessed the accuracy of our ECG identification model for distinct groups of patients with particular heart conditions and combinations of such conditions. For example, we observed that the identification accuracy was the highest (99.7%) for patients with both ST changes and supraventricular tachycardia. We also found that the identification rate was the lowest for patients diagnosed with both atrial fibrillation and complete right bundle branch block (49%). We discuss the implications of these findings regarding the reidentification risks of patients based on ECG data and how seemingly anonymized ECG datasets can cause privacy concerns for the patients.

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“…In other words, instead of using personnel identification cards, magnetic cards, keys or passwords, biometrics can be used to determine the individual's fingerprints, face, iris, handprints, signature, DNA and retina with easy and convenient verification methods. One study includes the design of a biometric system that classifies ECG signals using deep learning methods [22]. The ECG can be used as a biometrics for verification purposes because it provides detailed information about the electrical operation of the heart and this information is extremely personalized.…”

Section: Purpose Of the Thesismentioning

confidence: 99%

Driver Profiling Using Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) Methods

Cura¹,

Küçük

Ergen³

et al. 2021

IEEE Trans. Intell. Transport. Syst.

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ECG Based Biometric Human Identification Using Convolutional Neural Network in Smart Health Applications

Cited by 17 publications

References 14 publications

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

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

Assessing the Reidentification Risks Posed by Deep Learning Algorithms Applied to ECG Data

Driver Profiling Using Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) Methods

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