Development of AI-based method to detect the subtle ECG deviations from the population ECG norm

Abstract

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): National Academy of Science of Ukraine Background Electrocardiogram (ECG) is still the primary source for the diagnostic and prognostic information about cardiovascular diseases. The concept of "normal ECG" parameters is crucial for the reliable diagnosis, since it provides reference for the ECG under examination. With the development of new methods and tools for ECG feature extraction and classification based on artificial intelligence (AI), it becomes possible to identify subtle changes in the heart activity to detect possible abnormalities at the early stage. The challenge of this work is to identify the deviations in ECG of clinically healthy persons from the conditional "population" norm . Methods The normal ECG is described as a feature vector composed of the time-magnitude parameters of signal-averaged ECG (SAECG). To define the subjects that possibly have variations from the "population" norm, the outlier detection approach is proposed: first the cloud of the vectors , constructed from the set of normal ECG"s , obtained from young, clinically similar healthy persons was created in feature space. Then, a particular ECG is considered deviant and requires the attention of the clinician when it is considered an outlier of the cloud of normal ECGs. In the experiment, SAECGs from the group of 139 young subjects (male, age 18-28 years) with no reported cardiovascular problems are used to extract 34 features from SAECG leads (magnitudes and durations of ECG waves, duration of ECG segments, etc.). ECGs were routinely previewed by qualified physicians, and no obvious anomalies were noticed. The Isolation Forest anomaly detection method is used with variable numbers of trees and different contamination parameters. Results The ratio of outliers were changed from 5 to 10% (7-12 subjects) with various numbers of estimator trees. Seven outlier SAECGs were repeatedly appearing for various settings. Out of these, 4 subjects were the oldest persons in group examined , and 3 others had a rare ventricular premature beats during routine ECG examination. Conclusion The proposed method is promising for application in routine and express ECG tests since it is able to quantify the subtle deviations from the normal ECG group.