Prediction of cardiac rhythm based on heart rate variability features in sinus tachycardia conditions

Dhananjay, B.; Jayaraman, Srinivasan

doi:10.1109/icbsii51839.2021.9445167

2021 Seventh International Conference on Bio Signals, Images, and Instrumentation (ICBSII) 2021

DOI: 10.1109/icbsii51839.2021.9445167

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Prediction of cardiac rhythm based on heart rate variability features in sinus tachycardia conditions

B. Dhananjay

Srinivasan Jayaraman

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2023

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

Budaraju

Neelapu

Pal

et al. 2023

Biomedical Engineering / Biomedizinische Technik

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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. Conclusion Clinical feature-based stacking ML models may help doctors obtain insight into important clinical ECG aspects for early AF prediction.

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

Budaraju

Neelapu

Pal

et al. 2023

Biomedical Engineering / Biomedizinische Technik

View full text Add to dashboard Cite

show abstract

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Prediction of cardiac rhythm based on heart rate variability features in sinus tachycardia conditions

Cited by 1 publication

References 27 publications

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

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

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