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A Deep Learning Approach Toward Differentiating Left versus Right for Idiopathic Ventricular Arrhythmia Originated
Reza Talebzadeh1, Hossein Khosravi1, Majid Haghjoo2
1Faculty of Electrical Engineering, Shahrood University of Technology, Shahrood, Iran.
Insights
Deep learning accurately identifies the origin of idiopathic ventricular arrhythmias (VA) from the heart's outflow tract using ECG data. This 1D-CNN model offers a promising, non-invasive diagnostic tool for guiding catheter ablation treatment.
Area of Science:
- Cardiology
- Medical Imaging
- Artificial Intelligence
Background:
- Idiopathic ventricular arrhythmias (VA) are common cardiac conditions, often originating from the complex outflow tract (OT).
- Accurate diagnosis of VA origin is challenging due to anatomical complexity and similar ECG features between right and left OT origins.
- Misdiagnosis can complicate treatment decisions, particularly for catheter ablation procedures.
Purpose of the Study:
- To develop a method for detecting the site of origin for VAs originating from the outflow tract.
- To improve diagnostic accuracy for guiding catheter ablation treatment.
- To leverage deep learning for non-invasive VA diagnosis.
Main Methods:
- Utilized a public dataset of 334 patients with idiopathic VA.
- Processed standard 12-lead ECG data into one-dimensional (1D) streams of premature beats.
- Compared the performance of 1D deep learning models: LSTM, GRU, and 1D-CNN.
Main Results:
- The 1D-CNN model achieved the highest performance.
- Achieved an accuracy of 93.4% and an F1-score of 0.9313.
- Demonstrated superior performance compared to other 1D DL models.
Conclusions:
- Deep learning, specifically 1D-CNN, effectively detects VA origin from the outflow tract.
- This DL approach surpasses conventional computerized ECG analysis methods.
- Offers a promising, non-invasive diagnostic tool for future clinical applications in guiding treatment.
Background:
Idiopathic ventricular arrhythmia (VA) is among the common cardiac diseases, ranging from benign conditions to those requiring immediate medical intervention. Many VAs originate from the heart's outflow tract (OT). However, this area's complexity and small size, along with other influencing external factors, pose significant challenges to accurate diagnosis. The similarity of the features of VAs on the electrocardiogram (ECG) originating from the right or left side of the OT may lead to misdiagnosis. This study aims to detect the site of origin for VAs originating from the OT, which is important as a key precognition for treatment during catheter ablation.
Methods:
We perform this diagnosis using the standard 12-lead ECG and deep learning (DL) techniques without additional equipment. First, inspired by next-generation sequencing in genetics, we created one-dimensional (1D) streams of premature beats from a public dataset of 334 patients. Then, to compare the performance of common 1D DL models, the data were presented to various models, including long short-term memory, gated recurrent unit, and 1D convolutional neural network (1D-CNN).
Results:
Experimental results show that the 1D-CNN network achieves the best performance, with an accuracy of 93.4% and an F1-score of 0.9313.
Conclusions:
The findings demonstrate the effectiveness of DL in a higher level of applications, specifically in the treatment process, compared to conventional ECG analysis applications based on computerized methods. This represents a promising prospect for use in treatment processes without relying on complex and multifaceted diagnostic methods in the future.
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