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Lightweight Multireceptive Field CNN for 12-Lead ECG Signal Classification
Degaga Wolde Feyisa1,2, Taye Girma Debelee1,2, Yehualashet Megersa Ayano1
1Ethiopian Artificial Intelligence Institute, P.O. Box 40782, Addis Ababa, Ethiopia.
Insights
A novel multireceptive field CNN (MRF-CNN) improves electrocardiogram (ECG) classification accuracy. This computer-aided approach addresses cardiologist shortages and enhances diagnostic capabilities for heart conditions.
Area of Science:
- Cardiology
- Artificial Intelligence
- Signal Processing
Background:
- Electrocardiogram (ECG) interpretation is crucial for diagnosing heart conditions but faces challenges due to cardiologist shortages and interpretation complexity.
- Existing computer-aided ECG analysis methods, particularly deep learning models like 1D-CNN, can be data-intensive and computationally demanding.
- The need for efficient and accurate automated ECG interpretation systems is critical for widespread clinical application.
Purpose of the Study:
- To develop and evaluate a novel deep learning architecture for improved ECG signal classification.
- To address the limitations of traditional deep learning models in ECG analysis by incorporating multi-receptive field concepts.
- To enhance the accuracy and efficiency of computer-aided diagnosis for cardiovascular diseases (CVDs) using ECG data.
Main Methods:
- Designed a custom multireceptive field Convolutional Neural Network (MRF-CNN) architecture tailored for 1D ECG time-series data.
- Utilized the PTB-XL dataset, a comprehensive ECG database, for training and evaluating the proposed MRF-CNN model.
- Compared the performance of the MRF-CNN against established methods for classifying ECG signals across different diagnostic granularities (superclasses, subclasses, and all diagnostic classes).
Main Results:
- The MRF-CNN architecture demonstrated improved performance in ECG classification tasks.
- Achieved an F1 score of 0.72 and AUC of 0.93 for classifying 5 ECG superclasses.
- Obtained an F1 score of 0.46 and AUC of 0.92 for 20 subclasses, and an F1 score of 0.31 and AUC of 0.92 for all diagnostic classes on the PTB-XL dataset.
Conclusions:
- The proposed MRF-CNN model effectively captures semantic context within ECG signals, leading to enhanced classification performance.
- This approach offers a promising solution for automated ECG interpretation, potentially alleviating the burden on healthcare professionals.
- The MRF-CNN architecture represents a significant advancement in applying deep learning for accurate and reliable cardiovascular disease detection from ECGs.
Abstract:
The electrical activity produced during the heartbeat is measured and recorded by an ECG. Cardiologists can interpret the ECG machine's signals and determine the heart's health condition and related causes of ECG signal abnormalities. However, cardiologist shortage is a challenge in both developing and developed countries. Moreover, the experience of a cardiologist matters in the accurate interpretation of the ECG signal, as the interpretation of ECG is quite tricky even for experienced doctors. Therefore, developing computer-aided ECG interpretation is required for its wide-reaching effect. 12-lead ECG generates a 1D signal with 12 channels among the well-known time-series data. Classical machine learning can develop automatic detection, but deep learning is more effective in the classification task. 1D-CNN is being widely used for CVDS detection from ECG datasets. However, adopting a deep learning model designed for computer vision can be problematic because of its massive parameters and the need for many samples to train. In many detection tasks ranging from semantic segmentation of medical images to time-series data classification, multireceptive field CNN has improved performance. Notably, the nature of the ECG dataset made performance improvement possible by using a multireceptive field CNN (MRF-CNN). Using MRF-CNN, it is possible to design a model that considers semantic context information within ECG signals with different sizes. As a result, this study has designed a multireceptive field CNN architecture for ECG classification. The proposed multireceptive field CNN architecture can improve the performance of ECG signal classification. We have achieved a 0.72 F 1 score and 0.93 AUC for 5 superclasses, a 0.46 F 1 score and 0.92 AUC for 20 subclasses, and a 0.31 F 1 score and 0.92 AUC for all the diagnostic classes of the PTB-XL dataset.
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