ELEKTRA: ELEKTRokardiomatrix application to biometric identification with convolutional neural networks

Fuster-Barceló, Caterina; Peris-López, Pedro; Cámara, Carmen

doi:10.1016/j.neucom.2022.07.059

Cited by 6 publications

(5 citation statements)

References 175 publications

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“…Ref. [ 25 ], in their work, generated an electrocardiomatrix (EKM), which is a matrix that is composed of several aligned R peaks from an EKG trace and then transformed. Furthermore, afterwards, CNN is applied for further evaluation.…”

Section: Resultsmentioning

confidence: 99%

“…The accuracies achieved utilizing 3, 10 and 20 classes on the ECG-ID [ 4 ] dataset were of 98.24%, 96.24% and 94%, respectively. The author proposed a new identification method in [ 25 ] based on electrocardiograms (EKGs) transformed into a heatmap of a collection of aligned R-peaks (heartbeats), which tends to result in an electrocardiomatrix. A simple CNN model with high accuracy and a low bit error rate is used as a verification solution to illustrate the feasibility of using EKMs (obtained from ECG traces) for identification.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

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: Resultsmentioning

confidence: 99%

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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“…To cope with the aforementioned issues, many methods have been presented for crop disease identification (Asad, 2022;Fuster-Barcelóet al, 2022). Specifically, these existing methods can be categorized as ´traditional, machine learning, and deep learning methods Cong et al, 2022a).…”

Section: Introductionmentioning

confidence: 99%

Dual-branch collaborative learning network for crop disease identification

et al. 2023

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Crop diseases seriously affect the quality, yield, and food security of crops. redBesides, traditional manual monitoring methods can no longer meet intelligent agriculture’s efficiency and accuracy requirements. Recently, deep learning methods have been rapidly developed in computer vision. To cope with these issues, we propose a dual-branch collaborative learning network for crop disease identification, called DBCLNet. Concretely, we propose a dual-branch collaborative module using convolutional kernels of different scales to extract global and local features of images, which can effectively utilize both global and local features. Meanwhile, we embed a channel attention mechanism in each branch module to refine the global and local features. Whereafter, we cascade multiple dual-branch collaborative modules to design a feature cascade module, which further learns features at more abstract levels via the multi-layer cascade design strategy. Extensive experiments on the Plant Village dataset demonstrated the best classification performance of our DBCLNet method compared to the state-of-the-art methods for the identification of 38 categories of crop diseases. Besides, the Accuracy, Precision, Recall, and F-score of our DBCLNet for the identification of 38 categories of crop diseases are 99.89%, 99.97%, 99.67%, and 99.79%, respectively. 811

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“…They can detect even small changes in the heart's electrical activity, making them a susceptible diagnostic tool. ECG equipment is relatively inexpensive compared to other diagnostic tools, making it accessible to a wide range of healthcare providers and patients 24 . Additionally, several studies suggest that heart rate variation is directly linked to sleep quality.…”

Section: Introductionmentioning

confidence: 99%

Sleep disorder identification using wavelet scattering on ECG signals

Sharma,

Lodhi,

Yadav

et al. 2023

Int J Imaging Syst Tech

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Sleep affects the functioning of all biological processes in the human body. Sleep disorders disrupt the well‐being of an individual and can potentially lead to other health complications. It affects a large part of the population, and their timely and efficient detection is crucial for improving the quality of health. Sleep disorders are generally analyzed using polysomnogram (PSG) signals, which must be captured in sophisticated laboratory settings. However, electrocardiogram (ECG) signals provide information about the electrical activity of the heart, which is strongly linked with disturbances in sleep and can be used as an indicator of disordered sleep. Along with that, the recording of ECG signals is much easier than other signals and can be done remotely without causing any significant disturbance to the subject. This brings practicality to the systems that are already being employed for sleep disorder detection. In this paper, we present a method for identifying types of sleep disorders using ECG signals. The objective of this paper is to investigate the use of ECG signals for the automated identification of insomnia, narcolepsy, periodic leg movement (PLM), rapid eye movement (REM) behavior disorder (RBD), and nocturnal frontal lobe epilepsy (NFLE) against healthy subjects. We aim to develop a machine learning‐based algorithm that automatically classifies ECG signals into these sleep disorder categories. The cyclic alternating pattern (CAP) sleep database was used in this study, which contains PSG recordings from individuals with and without sleep disorders, including insomnia, narcolepsy, PLM, RBD, and NFLE. A wavelet scattering network has been used to extract features from the ECG signals. Various classifiers were tested, and the ensemble bag of trees classifier provided the optimum performance. An overall accuracy of 98% was obtained for the identification of sleep disorders, along with a classification accuracy of 99.37%, 99.45%, 99.23%, 99.4%, and 99.65% for insomnia, narcolepsy, NFLE, PLM, and RBD, respectively, for binary classification against healthy subject data. Our results show that wavelet scattering network and ensemble of bagged tree (EbagT) classification can accurately identify various sleep disorders. They provided evidence that ECG signals can be used to identify and diagnose sleep disorders, which could lead to the development of accurate detection of these sleep disorders. Also, our study demonstrated the effectiveness of the proposed algorithm in identifying multiple sleep disorders, which could lead to more efficient and cost‐effective diagnosis and treatment of sleep disorders.

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ELEKTRA: ELEKTRokardiomatrix application to biometric identification with convolutional neural networks

Cited by 6 publications

References 175 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

Dual-branch collaborative learning network for crop disease identification

Sleep disorder identification using wavelet scattering on ECG signals

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