A Novel Two-Level Fusion Feature for Mixed ECG Identity Recognition

Liu, Xin; Si, Yujuan; Yang, Wei

doi:10.3390/electronics10172052

Cited by 9 publications

(6 citation statements)

References 40 publications

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“…And ECG, as a very common way to check the health of the heart, plays a very important role in the process of confirming many diseases [7]. The research on ECG abnormalities is also advancing year by year, such as a research scholar specifically for the analysis of ECG abnormalities in the elderly population [8]; in the paper, the authors firstly analyzed the relevant basic data and clarified the criteria to be used and the determination of the index observation and then used the method of organizing and analyzing the data through statistical methods. The final results of the experiment showed that the number of people with abnormal ECG results was about 1800 out of 3,000 elderly medical examinations, accounting for more than 60%, which shows that ECG examination is extremely important for the safety of human health.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based ECG Abnormality Identification Prediction and Analysis

Liang

2022

Journal of Sensors

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In recent years, China’s economy has been developing rapidly, and people’s life and quality of life have been improving; more importantly, people’s habits and living habits have also developed from the previous “unhygienic” and “not very careful” to the current “Healthier, more hygienic, greener and more sophisticated” direction. In the process of this development, due to the rapid development of the economy and the industrialization of cities, the incidence of heart disease is also increasing year by year. According to relevant studies, China’s urbanization process has been unprecedented, the number of urbanized people in China has exploded in recent years, and the ratio of urban to rural population has increased from about 20 percent to about 75 percent today. The effects of the population and the urbanization of urban architecture are affecting people’s physical and mental health, both consciously and unconsciously, causing both positive and negative physical and mental effects on the psychological and physical levels. In this paper, the concept of deep learning is fully utilized to train the CNN neural network model and apply it to the ECG abnormality recognition and prediction examination. In order to fully validate the application and significance of deep learning in ECG abnormality identification and prediction, the whole project was completed through subjective and objective experiments. The experimental results show that, from the subjective aspect, the ECG examination has been accepted by most people for different age groups, and the analysis results of the ECG examination with the deep learning model in this paper are more satisfactory; from the objective aspect, the CNN-ECG abnormality recognition prediction network model trained in this paper has high. The accuracy of the model for ECG abnormality recognition prediction can reach 86% when the learning rate is set to 0.0001 and the batch size is set to 120, and the model can reduce the burden of medical personnel to a certain extent.

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Section: Introductionmentioning

confidence: 99%

Deep Learning-Based ECG Abnormality Identification Prediction and Analysis

Liang

2022

Journal of Sensors

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“…A comparison of the machine learning techniques is shown in The authors of [23] proposed a two-level fusion PCANet (Principal Component Analysis Network) deep recognition network. It achieved an over 95% recognition rate.…”

Section: Ecg-based Recognition Methodsmentioning

confidence: 99%

Biometric-Based Human Identification Using Ensemble-Based Technique and ECG Signals

Aleidan,

Abbas,

Daadaa

et al. 2023

Applied Sciences

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User authentication has become necessary in different life domains. Traditional authentication methods like personal information numbers (PINs), password ID cards, and tokens are vulnerable to attacks. For secure authentication, methods like biometrics have been developed in the past. Biometric information is hard to lose, forget, duplicate, or share because it is a part of the human body. Many authentication methods focused on electrocardiogram (ECG) signals have achieved great success. In this paper, we have developed cardiac biometrics for human identification using a deep learning (DL) approach. Cardiac biometric systems rely on cardiac signals that are captured using the electrocardiogram (ECG), photoplethysmogram (PPG), and phonocardiogram (PCG). This study utilizes the ECG as a biometric modality because ECG signals are a superior choice for accurate, secure, and reliable biometric-based human identification systems, setting them apart from PPG and PCG approaches. To get better performance in terms of accuracy and computational time, we have developed an ensemble approach based on VGG16 pre-trained transfer learning (TL) and Long Short-Term Memory (LSTM) architectures to optimize features. To develop this authentication system, we have fine-tuned this ensemble network. In the first phase, we preprocessed the ECG biosignal to remove noise. In the second phase, we converted the 1-D ECG signals into a 2-D spectrogram image using a transformation phase. Next, the feature extraction step is performed on spectrogram images using the proposed ensemble DL technique, and finally, those features are identified by the boosting machine learning classifier to recognize humans. Several experiments were performed on the selected dataset, and on average, the proposed system achieved 98.7% accuracy, 98.01% precision, 97.1% recall, and 0.98 AUC. In this paper, we have compared the developed approach with state-of-the-art biometric authentication systems. The experimental results demonstrate that our proposed system outperformed the human recognition competition.

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“…El_Rahman [20] AUC for sequential multi-mode systems MIT-BIH 98.5% Belo [21] TCNN MIT-BIH 96% Liu, Xin [22] Two-level PCANet MIT-BIH 95% Ding, Ling-Juan [26] Improved AlexNet MIT-BIH 99.27%…”

Section: Reference Methods Dataset Accmentioning

confidence: 99%

“…The two scores were calculated and fed to the relative score threshold classifier (RSTC) and the recognition accuracy was 96%. Liu, Xin et al [22] proposed a two-level fusion PCANet deep recognition network that achieved a recognition accuracy of over 95% on the MIT-BIH dataset. Dang, Hao et al [23] used network models of CNN, multi-scale CNN and multi-scale CNN plus residual block for identity recognition on the MIT-BIH dataset, and the overall recognition accuracy was 93.41%, 96.83% and 97.03%, respectively.…”

Section: Introductionmentioning

confidence: 99%

ECG Identity Recognition Based on Feature Reuse Residual Network

Yang

Liu

et al. 2022

Processes

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With the increasing demand for security and privacy, identity recognition based on the unique biometric features of ECG signals is gaining more and more attention. This paper proposes a feature reuse residual network (FRRNet) model to address the problem that the recognition accuracy of conventional ECG identification methods decreases with the increase in the number of testing samples at different moments or in different heartbeat cycles. The residual module of the proposed FRRNet model uses the adding layers of max pooling (MP) and average pooling (AP), and the proposed model splices the deep network with the shallow network to reduce noise extraction and enhance feature reuse. The FRRNet model is tested on 20 and 47 subjects under the MIT-BIH dataset, and its recognition accuracy is 99.32% and 100%, respectively. Additionally, the FRRNet model is tested on 50 and 87 subjects under the PhysioNet/Computing in Cardiology Challenge 2017 (CinC_2017) dataset, and its recognition accuracy is 94.52% and 93.51%, respectively. A total of 20 subjects are taken from the MIT-BIH and the CinC_2017 datasets for testing, and the recognition accuracy is 98.97%. The experimental results show that the FRRNet model proposed in this paper has high recognition accuracy, and the recognition accuracy is not greatly affected when the number of individuals increases.

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A Novel Two-Level Fusion Feature for Mixed ECG Identity Recognition

Cited by 9 publications

References 40 publications

Deep Learning-Based ECG Abnormality Identification Prediction and Analysis

Deep Learning-Based ECG Abnormality Identification Prediction and Analysis

Biometric-Based Human Identification Using Ensemble-Based Technique and ECG Signals

ECG Identity Recognition Based on Feature Reuse Residual Network

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