Toward ECG-based analysis of hypertrophic cardiomyopathy: a novel ECG segmentation method for handling abnormalities
Kasra Nezamabadi1, Jacob Mayfield2, Pengyuan Li1
1Computational Biomedicine Lab, Computer and Information Sciences, University of Delaware, Newark, Delaware, USA.
Insights
This study introduces a new electrocardiogram (ECG) segmentation method that accurately identifies waves in hypertrophic cardiomyopathy (HCM) and normal heartbeats, outperforming existing techniques for precise cardiac analysis.
Area of Science:
- Cardiology
- Biomedical Engineering
- Signal Processing
Background:
- Hypertrophic cardiomyopathy (HCM) frequently causes abnormal impulse propagation and cardiac repolarization, leading to electrocardiogram (ECG) abnormalities.
- Computational ECG analysis aids in identifying electrophysiological and structural remodeling, and predicting arrhythmias, but relies on accurate ECG segmentation.
- Current segmentation methods, often trained on normal heartbeats, may not perform optimally for HCM ECGs.
Purpose of the Study:
- To develop and validate a novel ECG segmentation method for accurately identifying waves in 12-lead hypertrophic cardiomyopathy (HCM) ECGs.
- To assess the performance of the developed method on both HCM and non-HCM (normal) ECG datasets.
- To compare the new method against existing state-of-the-art segmentation techniques.
Main Methods:
- A web-based tool was created for expert manual annotation of various ECG waves (P, QRS, T, etc.) and arrhythmias.
- An easy-to-implement segmentation algorithm was developed to identify ECG waves in both normal and abnormal heartbeats.
- The method was evaluated on 131 12-lead HCM ECGs and two public ECG datasets.
Main Results:
- The method achieved high sensitivity (99.2%) and positive predictive value (92%) for QRS complex detection in HCM ECGs, outperforming a previous state-of-the-art method.
- It demonstrated superior performance in detecting P-onset, P-peak, T-offset, and QRS-onset/peak on public ECG sets compared to three other methods.
- Performance on non-HCM ECG sets was comparable to existing methods for other segmentation tasks.
Conclusions:
- The developed segmentation method accurately identifies ECG waves in hypertrophic cardiomyopathy (HCM) datasets.
- It significantly outperforms existing methods for HCM ECG analysis.
- The method shows robust performance on normal and non-HCM ECG datasets, indicating broad applicability.
Objective:
Abnormalities in impulse propagation and cardiac repolarization are frequent in hypertrophic cardiomyopathy (HCM), leading to abnormalities in 12-lead electrocardiograms (ECGs). Computational ECG analysis can identify electrophysiological and structural remodeling and predict arrhythmias. This requires accurate ECG segmentation. It is unknown whether current segmentation methods developed using datasets containing annotations for mostly normal heartbeats perform well in HCM. Here, we present a segmentation method to effectively identify ECG waves across 12-lead HCM ECGs.
Methods:
We develop (1) a web-based tool that permits manual annotations of P, P', QRS, R', S', T, T', U, J, epsilon waves, QRS complex slurring, and atrial fibrillation by 3 experts and (2) an easy-to-implement segmentation method that effectively identifies ECG waves in normal and abnormal heartbeats. Our method was tested on 131 12-lead HCM ECGs and 2 public ECG sets to evaluate its performance in non-HCM ECGs.
Results:
Over the HCM dataset, our method obtained a sensitivity of 99.2% and 98.1% and a positive predictive value of 92% and 95.3% when detecting QRS complex and T-offset, respectively, significantly outperforming a state-of-the-art segmentation method previously employed for HCM analysis. Over public ECG sets, it significantly outperformed 3 state-of-the-art methods when detecting P-onset and peak, T-offset, and QRS-onset and peak regarding the positive predictive value and segmentation error. It performed at a level similar to other methods in other tasks.
Conclusion:
Our method accurately identified ECG waves in the HCM dataset, outperforming a state-of-the-art method, and demonstrated similar good performance as other methods in normal/non-HCM ECG sets.
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