RANDOM FOREST-BASED ECG PREMATURE VENTRICULAR CONTRACTION DETECTION

The detection of premature ventricular contractions (PVCs) is an effective method for assessing cardiac health and guiding timely interventions to prevent more severe heart conditions. This paper thoroughly investigates the general manifestations of PVCs in electrocardiograms (ECGs) and focuses on three prominent characteristics of PVCs: premature occurrence of the QRS complex, widened QRS complex, and abnormal morphology of the QRS complex. A total of 11 highly interpretable feature parameters were extracted based on these characteristics. Using the MIT-BIH arrhythmia database and leveraging Python libraries NeuroKit and Scikit-learn, a random forest classifier was employed to detect PVCs, achieving an accuracy of 99.55%, a precision of 0.98, a recall of 0.95, and an F1 score of 0.96. The experimental results demonstrate that this detection method not only provides high accuracy with low computational complexity but also offers significant clinical value for effective PVC detection.