Lightweight Shufflenet Based CNN for Arrhythmia Classification

Tesfai, Huruy; Saleh, Hani; Al‐Qutayri, Mahmoud; Mohammad, Moath B.; Tekeste, Temesghen; Khandoker, Ahsan H.; Mohammad, Baker

doi:10.1109/access.2022.3215665

Cited by 21 publications

(8 citation statements)

References 36 publications

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“…Additionally, a meta-classifier combining these models was validated with the CNN-LSTM model, achieving 95.81% of accuracy. In [19], they introduced a novel CNN model, inspired by the Shuffle-Net architecture. This model was tailored for efficient deployment on resource-constrained wearable mobile devices.…”

Section: Literature Reviewmentioning

confidence: 99%

Approach Based Lightweight Custom Convolutional Neural Network and Fine-Tuned MobileNet-V2 for ECG Arrhythmia Signals Classification

Bechinia,

Benmerzoug,

Khlifa

2024

IEEE Access

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Arrhythmia detection in electrocardiogram (ECG) signals is a vital aspect of cardiovascular health monitoring. Current automated methods for arrhythmia classification often struggle to attain satisfactory performance in the detection of various heart conditions, particularly when dealing with imbalanced datasets. This study introduces a novel deep learning approach for the detection and classification of ECG arrhythmia plot images. Our methodology features a lightweight Custom Convolutional Neural Network model(LC-CNN), comprising just three convolutional layers and a transfer learning model with MobileNet-V2 architecture that leverages pre-trained features to enhance arrhythmia classification. Data preprocessing of the ECG signals involving noise reduction with a Butterworth filter and precise beat segmentation via R-peak detection, ensure high-quality input for our model. Furthermore, a notable contribution for ECG data augmentation, adopting the implementation of an Auxiliary Classifier Generative Adversarial Network (ACGAN), specifically addressing class imbalance in the benchmark MIT-BIH dataset to classify four types of ECG heartbeats. This approach enriches the dataset, enhancing the models' ability to detect underrepresented arrhythmia classes. The proposed system demonstrates an impressive average classification accuracy achieving 99.22% using the LC-CNN model. Closely followed by the fine-tuned MobileNet-V2 model with 98.69% accuracy, outperforming other methods and underscoring its effectiveness when faced with diverse irregular heartbeats and arrhythmia.

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Section: Literature Reviewmentioning

confidence: 99%

Approach Based Lightweight Custom Convolutional Neural Network and Fine-Tuned MobileNet-V2 for ECG Arrhythmia Signals Classification

Bechinia,

Benmerzoug,

Khlifa

2024

IEEE Access

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“…Over the past decade, the potential and feasibility of utilizing ECG signals to diagnose a wide spectrum of CVDs have been demonstrated by numerous previous studies [11], [18], [20], [21], [22], [23], [24], [25], [26], [27], [28]. Using a novel keen-guided neuroevolution algorithm, the SPN-V2 network achieved a stable balance between recognition accuracy and earliness [27].…”

Section: Ecg-based Cvds Prediction Using Deep Learningmentioning

confidence: 99%

Semi-Supervised Learning for Multi-Label Cardiovascular Diseases Prediction: A Multi-Dataset Study

Zhou,

Lu,

Liu

et al. 2024

IEEE Trans. Pattern Anal. Mach. Intell.

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Electrocardiography (ECG) is a non-invasive tool for predicting cardiovascular diseases (CVDs). Current ECG-based diagnosis systems show promising performance owing to the rapid development of deep learning techniques. However, the label scarcity problem, the co-occurrence of multiple CVDs and the poor performance on unseen datasets greatly hinder the widespread application of deep learning-based models. Addressing them in a unified framework remains a significant challenge. To this end, we propose a multi-label semi-supervised model (ECGMatch) to recognize multiple CVDs simultaneously with limited supervision. In the ECGMatch, an ECGAugment module is developed for weak and strong ECG data augmentation, which generates diverse samples for model training. Subsequently, a hyperparameter-efficient framework with neighbor agreement modeling and knowledge distillation is designed for pseudo-label generation and refinement, which mitigates the label scarcity problem. Finally, a label correlation alignment module is proposed to capture the co-occurrence information of different CVDs within labeled samples and propagate this information to unlabeled samples. Extensive experiments on four datasets and three protocols demonstrate the effectiveness and stability of the proposed model, especially on unseen datasets. As such, this model can pave the way for diagnostic systems that achieve robust performance on multi-label CVDs prediction with limited supervision. Code is available at https://github.com/KAZABANA/ECGMatch.

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“…For instance, in [50], an adaptive 1D CNN is proposed for ECG classification and anomaly detection at any sampling rate of ECG signals to avoid hand-crafted feature extraction. In [51], a lightweight 1D CNN considering channel shuffle over the group and depth-wise convolutions is designed, where 2-s ECG signal segments are considered as model input [37]. In [38], the 1D CNN is leveraged to classify 2, 5, and 20 types of heart diseases where few-shot learning is considered to deal with the small-size of the dataset.…”

Section: Modelmentioning

confidence: 99%

Deep Learning-Based ECG Arrhythmia Classification: A Systematic Review

et al. 2023

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Deep learning (DL) has been introduced in automatic heart-abnormality classification using ECG signals, while its application in practical medical procedures is limited. A systematic review is performed from perspectives of the ECG database, preprocessing, DL methodology, evaluation paradigm, performance metric, and code availability to identify research trends, challenges, and opportunities for DL-based ECG arrhythmia classification. Specifically, 368 studies meeting the eligibility criteria are included. A total of 223 (61%) studies use MIT-BIH Arrhythmia Database to design DL models. A total of 138 (38%) studies considered removing noise or artifacts in ECG signals, and 102 (28%) studies performed data augmentation to extend the minority arrhythmia categories. Convolutional neural networks are the dominant models (58.7%, 216) used in the reviewed studies while growing studies have integrated multiple DL structures in recent years. A total of 319 (86.7%) and 38 (10.3%) studies explicitly mention their evaluation paradigms, i.e., intra- and inter-patient paradigms, respectively, where notable performance degradation is observed in the inter-patient paradigm. Compared to the overall accuracy, the average F1 score, sensitivity, and precision are significantly lower in the selected studies. To implement the DL-based ECG classification in real clinical scenarios, leveraging diverse ECG databases, designing advanced denoising and data augmentation techniques, integrating novel DL models, and deeper investigation in the inter-patient paradigm could be future research opportunities.

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Lightweight Shufflenet Based CNN for Arrhythmia Classification

Cited by 21 publications

References 36 publications

Approach Based Lightweight Custom Convolutional Neural Network and Fine-Tuned MobileNet-V2 for ECG Arrhythmia Signals Classification

Approach Based Lightweight Custom Convolutional Neural Network and Fine-Tuned MobileNet-V2 for ECG Arrhythmia Signals Classification

Semi-Supervised Learning for Multi-Label Cardiovascular Diseases Prediction: A Multi-Dataset Study

Deep Learning-Based ECG Arrhythmia Classification: A Systematic Review

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