Ensemble Deep Learning Models for ECG-based Biometrics

Byeon, Yeong-Hyeon; Pan, Sung Bum; Kwak, Keun-Chang

doi:10.1109/ki48306.2020.9039871

Cited by 12 publications

(13 citation statements)

References 10 publications

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“…Technische Bundesanstalt (PTB) ECG database to compare the performance of ResNet, DenseNet and Xception in ECG [12]. The conclusion is that Xception and ResNet perform better than DenseNet in most experiments and ensemble CNNs have shown higher than single CNN.…”

Section: Byeon Et Al Have Done Experiments By Using Physikalischmentioning

confidence: 99%

Multi-ECGNet for ECG Arrythmia Multi-Label Classification

et al. 2020

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With the development of various deep learning algorithms, the importance and potential of AI + medical treatment are increasingly prominent. Electrocardiogram (ECG) as a common auxiliary diagnostic index of heart diseases, has been widely applied in the pre-screening and physical examination of heart diseases due to its low price and non-invasive characteristics. Currently, the multi-lead ECG equipments have been used in the clinic, and some of them have the automatic analysis and diagnosis functions. However, the automatic analysis is not accurate enough for the discrimination of abnormal events of ECG, which needs to be further checked by doctors. We therefore develop a deep-learning-based approach for multi-label classification of ECG named Multi-ECGNet, which can effectively identify patients with multiple heart diseases at the same time. The experimental results show that the performance of our methods can get a high score of 0.863 (micro-F1-score) in classifying 55 kinds of arrythmias, which is beyond the level of ordinary human experts.

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Section: Byeon Et Al Have Done Experiments By Using Physikalischmentioning

confidence: 99%

Multi-ECGNet for ECG Arrythmia Multi-Label Classification

et al. 2020

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“…In addition, ECG-based detection of driver drowsiness and stress level systems have been proposed in the literature to aid in reducing the rate of accidents. The ECG has also been used to predict the size and location of the heart as well as to locate the wound in the heart, and to ascertain the effectiveness of a drug (Byeon et al 2020).…”

Section: Autoencodersmentioning

confidence: 99%

“…Most of these papers mainly work on the applications of DL in physiological signals analysis as a whole including ECG and for various medical (and/or healthcare) applications. However, evidence from the literature reveals the applications of DL in ECG-based biometric systems for human identification Bajare and Ingale 2019; Byeon et al 2020;P.-L. Hong et al 2019) and authentication (Hammad et al 2018(Hammad et al , 2019Hammad and Wang 2019). Also, there are applications of DL in ECG-based driver drowsiness detection (Abbas 2020), stress level classification (Rastgoo et al 2019) and pilot load prediction towards mitigating the risks of accidents ).…”

Section: Introductionmentioning

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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“…A better way of this is the use of spectrograms, which can highlight features much more effectively. Work done in [13] and [14] shows the effectiveness of the use of spectrograms such as Mel Spectrogram, STFT and CWT to detect and classify any arrhythmia episodes.…”

Section: Motivationmentioning

confidence: 99%

A Spectrogram Based Novel Approach for Arrhythmia Detection with Convolutional Neural Networks

Rao

Singh

Kulkarni

et al. 2022

Preprint

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ECG is one of the most important medical scans which is used for diagnosis of various heart related conditions and diseases. One of the most common of these is arrhythmia, which is caused by the irregularity of the heart beats. Artificial Intelligence has had a major impact in the field of vital monitoring and autonomous medical diagnosis. Therefore, a lot of work has demonstrated its effectiveness in arrhythmia detection. In this paper, we propose a method that tries to improve upon the accuracy of such models with the help of a light weight deep learning architecture that utilized 2D Separable CNN with a group of graphical representations of the ECG signals like the STFT, CWT and MFCC. Our model has achieved an accuracy of 97.41 and an F1 score of 88.20 on a processed version of the MIT-BIH dataset and takes on an average 7.93 times less calculations compared to a simple 2D Convolution model.

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Ensemble Deep Learning Models for ECG-based Biometrics

Cited by 12 publications

References 10 publications

Multi-ECGNet for ECG Arrythmia Multi-Label Classification

Multi-ECGNet for ECG Arrythmia Multi-Label Classification

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

A Spectrogram Based Novel Approach for Arrhythmia Detection with Convolutional Neural Networks

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