Toward improving ECG biometric identification using cascaded convolutional neural networks

Li, Yazhao; Pang, Yanwei; Wang, Kongqiao; Li, Xuelong

doi:10.1016/j.neucom.2020.01.019

Cited by 91 publications

(49 citation statements)

References 36 publications

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“…The author reported 100% accuracy with a 2.90% equal error rate (EER) while only healthy ECG from the PTB database has been studied. More work on deep learning can be found in [14], [20]- [23], [25], [26], [38], [41], [42] which the detail has been described in Table 1, and Table 2. The heavy computational and large size of database requirements for deep learning models still restrain it from being realistic.…”

Section: A Feature Extraction Categorymentioning

confidence: 99%

“…A neural network is especially applied in non-linear classification problems. Various types of these classifiers were used in ECG identification, especially the Multilayer Perceptron (MLP) [12]- [14], [20]- [23], [25]- [28], [32], [36], [38], [42], but also the Long short-term memory (LSTM) [17], the Dynamic Time Warping (DTW) [31], the Radial Basis Function Neural Network (RBFNN) [28], [32], and k nearest neighbor (KNN) [28]. Most of the aforementioned classifiers used a similar approach for the loss function, optimization with a different node activation function.…”

Section: B Classification Categorymentioning

confidence: 99%

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ECG Biometric Authentication: A Comparative Analysis

et al. 2020

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Robust authentication and identification methods become an indispensable urgent task to protect the integrity of the devices and the sensitive data. Passwords have provided access control and authentication, but have shown their inherent vulnerabilities. The speed and convenience factor are what makes biometrics the ideal authentication solution as they could have a low probability of circumvention. To overcome the limitations of the traditional biometric systems, electrocardiogram (ECG) has received the most attention from the biometrics community due to the highly individualized nature of the ECG signals and the fact that they are ubiquitous and difficult to counterfeit. However, one of the main challenges in ECG-based biometric development is the lack of large ECG databases. In this paper, we contribute to creating a new large gallery off-the-person ECG datasets that can provide new opportunities for the ECG biometric research community. We explore the impact of filtering type, segmentation, feature extraction, and health status on ECG biometric by using the evaluation metrics. Our results have shown that our ECG biometric authentication outperforms existing methods lacking the ability to efficiently extract features, filtering, segmentation, and matching. This is evident by obtaining 100% accuracy for PTB, MIT-BHI, CEBSDB, CYBHI, ECG-ID, and in-house ECG-BG database in spite of noisy, unhealthy ECG signals while performing five-fold cross-validation. In addition, an average of 2.11% EER among 1,694 subjects is obtained.

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Section: A Feature Extraction Categorymentioning

confidence: 99%

Section: B Classification Categorymentioning

confidence: 99%

ECG Biometric Authentication: A Comparative Analysis

et al. 2020

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“…AI techniques have recently shown significant potential in cardiology [ 22 , 23 , 24 , 25 , 26 , 27 ] owing to their ability to automatically learn effective features from data without the help of domain experts. When focusing on deep learning methods applying ECG data, various architectures have been proposed for disease detection [ 15 , 17 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 ], sleep staging [ 39 , 40 ], and biometric identification [ 41 , 42 , 43 , 44 ], among others (see a recent survey in [ 22 ]).…”

Section: Methodsmentioning

confidence: 99%

Artificial-Intelligence-Enhanced Mobile System for Cardiovascular Health Management

Hong

Zhang

et al. 2021

Sensors

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The number of patients with cardiovascular diseases is rapidly increasing in the world. The workload of existing clinicians is consequently increasing. However, the number of cardiovascular clinicians is declining. In this paper, we aim to design a mobile and automatic system to improve the abilities of patients’ cardiovascular health management while also reducing clinicians’ workload. Our system includes both hardware and cloud software devices based on recent advances in Internet of Things (IoT) and Artificial Intelligence (AI) technologies. A small hardware device was designed to collect high-quality Electrocardiogram (ECG) data from the human body. A novel deep-learning-based cloud service was developed and deployed to achieve automatic and accurate cardiovascular disease detection. Twenty types of diagnostic items including sinus rhythm, tachyarrhythmia, and bradyarrhythmia are supported. Experimental results show the effectiveness of our system. Our hardware device can guarantee high-quality ECG data by removing high-/low-frequency distortion and reverse lead detection with 0.9011 Area Under the Receiver Operating Characteristic Curve (ROC–AUC) score. Our deep-learning-based cloud service supports 20 types of diagnostic items, 17 of them have more than 0.98 ROC–AUC score. For a real world application, the system has been used by around 20,000 users in twenty provinces throughout China. As a consequence, using this service, we could achieve both active and passive health management through a lightweight mobile application on the WeChat Mini Program platform. We believe that it can have a broader impact on cardiovascular health management in the world.

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“…The works of [2]- [5], [11], [15], [51], [52], on ECG biometric focus on heartbeats classification in healthy and non-healthy utilizing methods like CNNs, autoencoders, or DBNs. The authors in [33] employed the autocorrelation features with a non-overlapping window to build features, while the approach in [30] applied the autocorrelation/discrete cosine transform (DCT) feature of the ECG signal without fiducial detection.…”

Section: A Background and Prior Workmentioning

confidence: 99%

“…Non-handcrafted features based algorithm are recently being explored [5], [11], [18], [37], [42], [51], [312]. Specifically, the non-handcrafted feature-based algorithms are designed using three foremost approaches: deep feature extraction from a CNN [5], [11], [51], PCA network, and the compact binary descriptor (CBD). Thus, deep learning methods aids promote performance by neglecting the handcrafted feature extraction approaches, which require separate preprocessing steps as feature transforms and ECG noise removal, thus improves performance.…”

Section: ) Non-handcrafted Fuducial Feature-based Algorithmsmentioning

confidence: 99%

A Comprehensive Survey on ECG Signals as New Biometric Modality for Human Authentication: Recent Advances and Future Challenges

Uwaechia

Ramli

2021

IEEE Access

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Electrocardiogram (ECG) has extremely discriminative characteristics in the biometric field and has recently received significant interest as a promising biometric trait. However, ECG signals are susceptible to several types of noises, such as baseline wander, powerline interference, and high/lowfrequency noises, making it challenging to realize biometric identification systems precisely and robustly. Therefore, ECG signal denoising is a major pre-processing step and plays a crucial role in ECG-based biometric human identification. ECG signal analysis for biometric recognition can combine several steps, such as preprocessing, feature extraction, feature selection, feature transformation, and classification which is a very challenging task. Moreover, the selected success measures and proper structure of the ECG signal database play significant roles in biometric system analysis, considering that publicly available databases are essential by the research community to evaluate the performance of their proposed algorithms. In this survey, we review most of the techniques employed for the ECG as biometrics for human authentication. Firstly, we present an overview and discussion on ECG signal preprocessing, feature extraction, feature selection, and feature transformation for ECG-based biometric systems. Secondly, we present a survey of the available ECG databases to evaluate and compare the acquisition protocol, acquisition hardware, and acquisition resolution (bits) for ECG-based biometric systems. Thirdly, we also present a survey on different techniques, including deep learning methods: deep supervised learning, deep semi-supervised learning, and deep unsupervised learning, for ECG signal classification. Lastly, we present the state-of-art approaches of information fusion in multimodal biometric systems.

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Toward improving ECG biometric identification using cascaded convolutional neural networks

Cited by 91 publications

References 36 publications

ECG Biometric Authentication: A Comparative Analysis

ECG Biometric Authentication: A Comparative Analysis

Artificial-Intelligence-Enhanced Mobile System for Cardiovascular Health Management

A Comprehensive Survey on ECG Signals as New Biometric Modality for Human Authentication: Recent Advances and Future Challenges

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