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Stacked machine learning models to classify atrial disorders based on clinical ECG features: a method to predict early atrial fibrillation by Dhananjay Budaraju; Bala Chakravarthy Neelapu; Kunal Pal; Sivaraman Jayaraman is a Medicine article available to read on EtoBox.

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Objectives: Atrial Tachycardia (AT) and Left Atrial Enlargement (LAE) are atrial diseases that are significant precursors to Atrial Fibrillation (AF). There are ML models for ECG classification; clinical features-based classification is required. The suggested work aims to create stacked ML models that categorize Sinus Rhythm (SR), Sinus Tachycardia (ST), AT, and LAE signals based on clinical parameters for AF prognosis. Methods: The classification was based on thirteen clinical parameters, such as amplitude, time domain ECG aspects, and P-Wave Indices (PWI), such as the ratio of P-wave length and amplitude ((P (ms)/P (μV)), P-wave area (μV\*ms), and P-wave terminal force (PTFV1(μV\*ms). Apart from classifying the ECG signals, the stacked ML models prioritized the clinical features using a pie formula-based technique. ## Results: The Stack 1 model achieves 99% accuracy, sensitivity, precision, and F1 score, while the Stack 2 model achieves 91%, 91%, 94%, and 92% for identifying SR, ST, LAE, and AT, respectively. Both stack models obtained a computational time of 0.06 seconds. PTFV1 (μV\*ms), P (ms)/P (μV)), and P-wave area (μV\*ms) were ranked as crucial clinical features. Conclusi

Who reads Stacked machine learning models to classify atrial disorders based on clinical ECG features: a method to predict early atrial fibrillation?

Author

Dhananjay Budaraju; Bala Chakravarthy Neelapu; Kunal Pal; Sivaraman Jayaraman

Publisher

Walter de Gruyter GmbH

Published

2023

Language

EN

Field

Medicine (Health Sciences)