Classification of ischemic and non-ischemic cardiac events in Holter recordings based on the continuous wavelet
Carolina Fernández Biscay1,2, Pedro David Arini3,4, Anderson Iván Rincón Soler3,4
1Instituto Argentino de Matemática, "Alberto P. Calderón", CONICET, Saavedra 15, piso 3, Ciudad Autónoma de Buenos Aires, C1083ACA, Argentina. cfernandezbiscay@conicet.gov.ar.
Insights
This study improves the classification of cardiac ischemia using novel spectral parameters derived from continuous wavelet transform (CWT) and temporal features. The new method enhances accuracy in distinguishing ischemic from non-ischemic events detected via Holter recordings.
Area of Science:
- Cardiology
- Biomedical Signal Processing
- Machine Learning in Healthcare
Background:
- Holter recordings are crucial for detecting transient cardiac events like ischemia.
- Accurate classification of ischemic versus non-ischemic events remains a challenge due to false positives from non-ischemic changes.
- Existing methods struggle to reliably differentiate true ischemia from other cardiac signal variations.
Purpose of the Study:
- To enhance the classification accuracy of ischemic and non-ischemic cardiac events.
- To introduce novel spectral parameters derived from Continuous Wavelet Transform (CWT) for improved event classification.
- To reduce false positives in ischemia detection using advanced signal processing techniques.
Main Methods:
- Extracted novel spectral parameters using Continuous Wavelet Transform (CWT) within the 0.5-4 Hz frequency band.
- Combined CWT-derived spectral features with traditional temporal parameters (ST level/slope, T wave, R wave, QRS width).
- Employed a nearest neighbor classifier with six neighbors for event classification on the Long Term ST Database.
Main Results:
- Achieved a sensitivity of 84.1% and specificity of 92.9% for classifying ischemic versus non-ischemic events.
- Demonstrated a 10% increase in sensitivity compared to existing literature findings.
- Showcased substantial improvement in classification by integrating CWT spectral features over temporal parameters alone.
Conclusions:
- Novel spectral parameters from CWT significantly improve the classification of ischemic and non-ischemic cardiac events.
- The proposed method offers a more accurate approach to ischemia detection, reducing misclassification.
- This advancement holds potential for earlier and more reliable diagnosis of myocardial infarction.
Abstract:
Holter recordings are widely used to detect cardiac events that occur transiently, such as ischemic events. Much effort has been made to detect early ischemia, thus preventing myocardial infarction. However, after detection, classification of ischemia has still not been fully solved. The main difficulty relies on the false positives produced because of non-ischemic events, such as changes in the heart rate, the intraventricular conduction or the cardiac electrical axis. In this work, the classification of ischemic and non-ischemic events from the long-term ST database has been improved, using novel spectral parameters based on the continuous wavelet transform (CWT) together with temporal parameters (such as ST level and slope, T wave width and peak, R wave peak, QRS complex width). This was achieved by using a nearest neighbour classifier of six neighbours. Results indicated a sensitivity and specificity of 84.1% and 92.9% between ischemic and non-ischemic events, respectively, resulting a 10% increase of the sensitivity found in the literature. Extracted features based on the CWT applied on the ECG in the frequency band 0.5-4 Hz provided a substantial improvement in classifying ischemic and non-ischemic events, when comparing with the same classifier using only temporal parameters. Graphical Abstract In this work it is improved the classification of ischemic and non-ischemic events. The main difficulty of ischemic detectors relies on the false positives produced because of non-ischemic events. After a preprocessing stage, temporal and spectral parameters are extracted from events of the Long Term ST Database. The novel parameters proposed in this work are extracted from the Continuous Wavelet Transform. A nearest Neighbor Classifier is used, obtaining a sensitivity and specificity of 84.1% and 92.9%, respectively.
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