Interpretable Assessment of ST-Segment Deviation in ECG Time Series
Israel Campero Jurado1, Andrejs Fedjajevs2,3, Joaquin Vanschoren1
1Department of Mathematics and Computer Science, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands.
Insights
This study introduces an automated method to detect and quantify ST-segment deviation in electrocardiography (ECG) signals, crucial for identifying myocardial infarction (MI). The machine learning approach achieved high accuracy, improving cardiovascular disease diagnosis.
Area of Science:
- Biomedical Engineering
- Cardiology
- Machine Learning in Healthcare
Background:
- Cardiovascular diseases (CVD) remain a leading cause of mortality despite medical advancements.
- ST-segment deviation on electrocardiography (ECG) is a key indicator of myocardial infarction (MI), often diagnosed manually.
- Accurate and automated detection of ST-segment deviation is needed for timely diagnosis.
Purpose of the Study:
- To develop and validate an automated methodology for detecting and quantifying ST-segment deviation in single-lead ECG.
- To apply automated machine learning for optimizing classification and regression models for ST-segment deviation analysis.
- To assess the performance of the proposed method using a standard cardiovascular database.
Main Methods:
- Extraction of statistical features, point-to-point beat characteristics, and signal quality indexes (SQIs) from single-lead ECG.
- Application of automated machine learning (AutoML) to identify optimal hyperparameters for predictive models.
- Utilizing the Physionet ST-T database for method validation.
Main Results:
- The proposed automated method achieved 98.30% accuracy for multiclass classification of ST-segment deviation.
- The method demonstrated 99.87% accuracy for binary classification (presence or absence of deviation).
- Quantification of ST-segment deviation scale up to 1 mV was achieved.
Conclusions:
- Automated detection and quantification of ST-segment deviation using machine learning is highly accurate.
- This methodology offers a promising tool for objective and efficient myocardial infarction diagnosis.
- The approach has the potential to enhance cardiovascular disease screening and management.
Abstract:
Nowadays, even with all the tremendous advances in medicine and health protocols, cardiovascular diseases (CVD) continue to be one of the major causes of death. In the present work, we focus on a specific abnormality: ST-segment deviation, which occurs regularly in high-performance athletes and elderly people, serving as a myocardial infarction (MI) indicator. It is usually diagnosed manually by experts, through visual interpretation of the printed electrocardiography (ECG) signal. We propose a methodology to detect ST-segment deviation and quantify its scale up to 1 mV by extracting statistical, point-to-point beat characteristics and signal quality indexes (SQIs) from single-lead ECG. We do so by applying automated machine learning methods to find the best hyperparameter configuration for classification and regression models. For validation of our method, we use the ST-T database from Physionet; the results show that our method obtains 98.30% accuracy in the case of a multiclass problem and 99.87% accuracy in the case of binarization.
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