CardioNet: An Efficient ECG Arrhythmia Classification System Using Transfer Learning

Srivastva, Ranjeet; Singh, Yatindra Nath

doi:10.1016/j.bdr.2021.100271

Cited by 28 publications

(12 citation statements)

References 39 publications

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“…Similarly, a time-frequency 2D-based approach was studied by Zhang et al [11] to utilize the image-CNN technique by including a residual neural network (ResNet-101) TL for the PTB categorization. Other studies that implemented image-CNN using scalogram include [26,27] and [28], where the classifier's learning on the PTB data is fine-tuned using weights collected during the initial training process.…”

Section: Related Workmentioning

confidence: 99%

A Cost-Based Dual ConvNet-Attention Transfer Learning Model for ECG Heartbeat Classification

Victor,

Chew,

Khaw

et al. 2023

JIWE

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The heart is a very crucial organ of the body. Concerted efforts are constantly put forward to provide adequate monitoring of the heart. A heart disorder is reported to cause a lot of hidden ailments resulting in numerous deaths. Early heart monitoring using an electrocardiogram (ECG) through the advancement of computer-aided diagnostic (CAD) systems is widely used. Meanwhile, the use of human reading of ECG results are faced with many challenges of inaccurate and unreliable interpretations. Over two decades, studies provided artificial intelligence (AI) technique using machine learning (ML) algorithms as a fast and reliable technique for ECG heartbeat classification. Moreover, in recent times, deep learning (DL) techniques have been focused on providing automatic feature extraction and better classification performance. On the other hand, the challenge with the ECG data is its imbalance nature. Therefore, this paper proposes a cost-based dual convolutional attention transfer DL model for ECG classification. The proposed model uses PhysionNet-MIT-BIH and Physikalisch-Technische Bundesanstalt (PTB) Diagnostics datasets. The first part uses the MIT-BIH for ECG categorization, while representations learned from the first classifier are used for PTB analysis through transfer learning (TL). The proposed model is evaluated and compared with well-performing conventional ML models based on their F1-score and accuracy scores. Our experimental finding show that the proposed model outperformed the well-performing ML models as well as competitive with past studies for both the classification and TL part, having obtained 98.45% for both F1-score and accuracy. The proposed model is applicable to real-life trials and experiments for ECG heartbeat and other similar domains.

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Section: Related Workmentioning

confidence: 99%

A Cost-Based Dual ConvNet-Attention Transfer Learning Model for ECG Heartbeat Classification

Victor,

Chew,

Khaw

et al. 2023

JIWE

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show abstract

“…Previous studies applying deep learning to ECG classification focused on arrhythmia detection [21][22][23]. One of the reasons may be the availability of benchmark data sets that have reliable annotations that are mostly limited to arrhythmia, such as atrial fibrillation.…”

Section: Principal Findingsmentioning

confidence: 99%

Using Deep Transfer Learning to Detect Hyperkalemia From Ambulatory Electrocardiogram Monitors in Intensive Care Units: Personalized Medicine Approach

CHIU¹,

Cheng²,

Chen³

et al. 2022

J Med Internet Res

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Background Hyperkalemia is a critical condition, especially in intensive care units. So far, there have been no accurate and noninvasive methods for recognizing hyperkalemia events on ambulatory electrocardiogram monitors. Objective This study aimed to improve the accuracy of hyperkalemia predictions from ambulatory electrocardiogram (ECG) monitors using a personalized transfer learning method; this would be done by training a generic model and refining it with personal data. Methods This retrospective cohort study used open source data from the Waveform Database Matched Subset of the Medical Information Mart From Intensive Care III (MIMIC-III). We included patients with multiple serum potassium test results and matched ECG data from the MIMIC-III database. A 1D convolutional neural network–based deep learning model was first developed to predict hyperkalemia in a generic population. Once the model achieved a state-of-the-art performance, it was used in an active transfer learning process to perform patient-adaptive heartbeat classification tasks. Results The results show that by acquiring data from each new patient, the personalized model can improve the accuracy of hyperkalemia detection significantly, from an average of 0.604 (SD 0.211) to 0.980 (SD 0.078), when compared with the generic model. Moreover, the area under the receiver operating characteristic curve level improved from 0.729 (SD 0.240) to 0.945 (SD 0.094). Conclusions By using the deep transfer learning method, we were able to build a clinical standard model for hyperkalemia detection using ambulatory ECG monitors. These findings could potentially be extended to applications that continuously monitor one’s ECGs for early alerts of hyperkalemia and help avoid unnecessary blood tests.

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“…Metrics like accuracy, sensitivity, F1-score and specificity are essentially used for evaluating the performance of the classifier. In addition, DenseNet is widely used to classify the medical data and it has been introduced to fit ECG signals in [17]. This model proves to be efficient by achieving a percentage of 98.92%.…”

Section: Related Workmentioning

confidence: 99%

“…Several DNN models have been developed for COVID-19 detection. In this respect, the CNN is the most frequently used method for COVID-19 ECG and CT images classification in comparison with other DNN-based models [12][13][14] [15][16] [17]. Concerning the classification of images, the proposed model is proved to be useful for detecting normal, COVID-19 and pneumonia cases.…”

Section: Related Workmentioning

confidence: 99%

A novel internet of things based on deep neural network framework using soft-attention convolutional neural networks for COVID-19 detection

Fki

Ammar

Fourati

et al. 2022

Preprint

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Background: The impact of coronavirus (COVID-19) pandemic on health care is universal. The risks resulting from emerging contagious viruses and the efficacy of vaccines are persisting due to the presence of different variants. Learning of deeper and more interpretable models from COVID-19 data are conducive to understand this disease and to study the virus spread, individual diagnosis and may be other engrossing relating issues. However, some difficulties and intricacies are arising from the scarcity of precisely labelled data. Previous works have exploited existing Deep Neural Network (DNN) models that are pre-trained on large datasets like ImageNet. Method: In this paper, a new framework is proposed in order to monitor and predict COVID-19 cases and other diseases, pursuing medical data. The currently proposed framework essentially relies on (1) an Internet of Things (IoT) processing model to collect data and operate on them later, (2) a DNN model for data processing, known as REGATT. This proposed model is based on a pre-trained REGNet model finely tuned by spatial, channel ATTention and convolutional layers, boosting feature representation and discrimination. Results: Comparative experimental results on four different benchmark datasets show that the proposed model leads to a promising solution for diagnosing COVID-19. Conclusion: It is concluded that an IoT and DNN-based solution are a viable way for the diagnosis of not only COVID-19 but also other diseases. It is advisable that future works explore the development of interpretable models.

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CardioNet: An Efficient ECG Arrhythmia Classification System Using Transfer Learning

Cited by 28 publications

References 39 publications

A Cost-Based Dual ConvNet-Attention Transfer Learning Model for ECG Heartbeat Classification

A Cost-Based Dual ConvNet-Attention Transfer Learning Model for ECG Heartbeat Classification

Using Deep Transfer Learning to Detect Hyperkalemia From Ambulatory Electrocardiogram Monitors in Intensive Care Units: Personalized Medicine Approach

A novel internet of things based on deep neural network framework using soft-attention convolutional neural networks for COVID-19 detection

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