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Heartbeat Classification by Random Forest With a Novel Context Feature: A Segment Label
Congyu Zou1, Alexander Muller1, Utschick Wolfgang2
1Klinikum Rechts der Isar derTechnische Universität München 81675 München Germany.
Insights
A new segment label feature improves heartbeat classification accuracy for physicians. This algorithm aids in diagnosing cardiac abnormalities, particularly Supra-Ventricular Ectopic Beats (SVEB), enhancing patient care.
Area of Science:
- Cardiology
- Artificial Intelligence
- Biomedical Engineering
Background:
- Electrocardiograms (ECG) are vital for diagnosing cardiac abnormalities.
- Precise diagnosis of abnormal heartbeats often requires deeper analysis.
- Current methods may need enhancement for complex arrhythmia classification.
Purpose of the Study:
- To design a more accurate heartbeat classification algorithm.
- To assist physicians in identifying specific types of heartbeats.
- To improve the diagnosis of cardiac conditions.
Main Methods:
- Proposed a novel 'segment label' feature extracted using a Convolutional Neural Network.
- Trained a random forest classifier using the new feature alongside traditional ones.
- Validated the method on the MIT-BIH Arrhythmia dataset using inter-patient evaluation.
Main Results:
- Achieved an overall accuracy of 0.96.
- Obtained F1-scores of 0.98 for normal beats, 0.93 for ventricular ectopic beats, and 0.74 for Supra-Ventricular Ectopic Beats (SVEB).
- Demonstrated superior precision (0.76) and sensitivity (0.78) for SVEB compared to state-of-the-art methods.
Conclusions:
- The segment label feature enhances heartbeat classification accuracy, especially for arrhythmias needing contextual rhythm information like SVEB.
- Integrating this algorithm into medical devices can streamline cardiovascular disease diagnosis for physicians.
- The findings offer significant potential for improving clinical implementation in diagnosing conditions such as SVEB.
Objective:
Physicians use electrocardiograms (ECG) to diagnose cardiac abnormalities. Sometimes they need to take a deeper look at abnormal heartbeats to diagnose the patients more precisely. The objective of this research is to design a more accurate heartbeat classification algorithm to assist physicians in identifying specific types of the heartbeat.
Methods And Procedures:
In this paper, we propose a novel feature called a segment label, to improve the performance of a heartbeat classifier. This feature, provided by a Convolutional Neural Network, encodes the information surrounding the particular heartbeat. The random forest classifier is trained based on this new feature and other traditional features to classify the heartbeats.
Results:
We validate our method on the MIT-BIH Arrhythmia dataset following the inter-patient evaluation paradigm. The proposed method is competitive with other similar works. It achieves an accuracy of 0.96, and F1-scores for normal beats, ventricular ectopic beats, and Supra-Ventricular Ectopic Beats (SVEB) of 0.98, 0.93, and 0.74, respectively. The precision and sensitivity for SVEB are 0.76 and 0.78, which outperforms the state-of-the-art methods.
Conclusion:
This study demonstrates that the segment label can contribute to precisely classifying heartbeats, especially those that require rhythm information as context information (e.g. SVEB). Clinical impact: Using a medical devices embedding our algorithm could ease the physicians' processes of diagnosing cardiovascular diseases, especially for SVEB, in clinical implementation.
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