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

  • Dhananjay Budaraju , Bala Chakravarthy Neelapu , Kunal Pal und Sivaraman Jayaraman EMAIL logo
Veröffentlicht/Copyright: 27. März 2023
Veröffentlichen auch Sie bei De Gruyter Brill

Abstract

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.


Corresponding author: Dr. Sivaraman Jayaraman, Assistant Professor, Bio-signals and Medical Instrumentation Laboratory, Department of Biotechnology and Medical Engineering, National Institute of Technology Rourkela, Odisha, 769008, India, Phone: +91-9840968282, E-mail:

Award Identifier / Grant number: EEQ/2019/000148

Acknowledgments

The authors acknowledge the support from the Ministry of Education, Government of India to carry out this research work.

  1. Research funding: The present study was supported by financial grants from the Science and Engineering Research Board (SERB), Department of Science and Technology (DST), Government of India (EEQ/2019/000148).

  2. Author contributions: Budaraju Dhananjay: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Writing-Original Draft, Writing-Review & Editing, and Visualization. Bala Chakravarthy Neelapu, Kunal Pal, J. Sivaraman: Conceptualization, Methodology, Validation, Formal analysis, Resources, Data Curation, Writing- Review & Editing, Visualization, Supervision, Project administration, Funding acquisition.

  3. Conflicts of interests: The authors have no relevant financial or non-financial interests to disclose.

  4. Informed consent: Informed consent was obtained from all individual participants included in the study.

  5. Ethical approval: All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Received: 2022-11-07
Accepted: 2023-02-20
Published Online: 2023-03-27
Published in Print: 2023-08-28

© 2023 Walter de Gruyter GmbH, Berlin/Boston

Heruntergeladen am 15.9.2025 von https://www.degruyterbrill.com/document/doi/10.1515/bmt-2022-0430/html
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