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Signal Processing Framework for the Detection of Ventricular Ectopic Beat Episodes
Avvaru Srinivasulu1,2,3, Natarajan Sriraam4
1Research Scholar, Center for Medical Electronics and Computing, M.S Ramaiah Institute of Technology, Belgaum, Karnataka, India.
Insights
This study introduces an automated signal processing framework to detect ventricular ectopic beat (VEB) episodes in long-term electrocardiogram (ECG) data, significantly reducing manual analysis time and improving diagnostic efficiency.
Area of Science:
- Biomedical Engineering
- Cardiology
- Signal Processing
Background:
- Long-term electrocardiogram (ECG) analysis for detecting cardiac abnormalities like ventricular ectopic beats (VEBs) is time-consuming due to manual cross-checking.
- Existing Holter monitor technology captures ECG data but relies on manual interpretation, creating a bottleneck in diagnosis.
Purpose of the Study:
- To develop and validate an effective automatic cardiac episode detection technique to reduce the manual burden in analyzing long-term ECG signals.
- To present a signal processing framework for detecting ventricular ectopic beat (VEB) episodes across different databases.
Main Methods:
- ECG signals were preprocessed, and features like RR intervals and QRS complex parameters were extracted.
- Four classification models, including Support Vector Machine, k-means nearest neighbor, nearest mean classifier, and Nearest RMS (NRMS) classifier, were employed.
- Models were trained and tested on both open-source and proprietary ECG databases for VEB detection.
Main Results:
- The Nearest RMS (NRMS) classifier demonstrated superior performance compared to other models.
- NRMS achieved high accuracy (98.68% on open-source, 99.97% on proprietary) and F1-scores (94.12% on open-source, 94.54% on proprietary) for VEB detection.
- Excellent recall rates (100% and 98.62%) and specificity (98.53% and 99.98%) were observed across both databases.
Conclusions:
- The proposed signal processing framework effectively detects ventricular ectopic beat (VEB) episodes in long-term ECG signals.
- The developed automated system shows significant potential for integration into clinical practice for efficient cardiac diagnosis.
- The NRMS classifier offers a robust and accurate solution for automated VEB detection, reducing physician workload.
Abstract:
The Holter monitor captures the electrocardiogram (ECG) and detects abnormal episodes, but physicians still use manual cross-checking. It takes a considerable time to annotate a long-term ECG record. As a result, research continues to be conducted to produce an effective automatic cardiac episode detection technique that will reduce the manual burden. The current study presents a signal processing framework to detect ventricular ectopic beat (VEB) episodes in long-term ECG signals of cross-database. The proposed study has experimented with the cross-database of open-source and proprietary databases. The ECG signals were preprocessed and extracted the features such as pre-RR interval, post-RR interval, QRS complex duration, QR slope, and RS slope from each beat. In the proposed work, four models such as support vector machine, k-means nearest neighbor, nearest mean classifier, and nearest RMS (NRMS) classifiers were used to classify the data into normal and VEB episodes. Further, the trained models were used to predict the VEB episodes from the proprietary database. NRMS has reported better performance among four classification models. NRMS has shown the classification accuracy of 98.68% and F1-score of 94.12%, recall rate of 100%, specificity of 98.53%, and precision of 88.89% with an open-source database. In addition, it showed an accuracy of 99.97%, F1-score of 94.54%, recall rate of 98.62%, specificity of 99.98%, and precision of 90.79% to detect the VEB cardiac episodes from the proprietary database. Therefore, it is concluded that the proposed framework can be used in the automatic diagnosis system to detect VEB cardiac episodes.
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