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CatBoost-based improved detection of P-wave changes in sinus rhythm and tachycardia conditions: a lead selection study

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Abstract

Examining P-wave morphological changes in Electrocardiogram (ECG) is essential for characterizing atrial arrhythmias. However, standard 12-lead ECGsuffer from diagnostic redundancy due to low signal-to-noise ratio of P-waves. To address this issue, various optimal leads have been proposed for improved atrial activity recording, but the right selection among these leads is crucial for enhancing diagnostic efficacy. This study proposes an automated lead selection technique using the CatBoost machine learning (ML) model to improve the detection of P-wave changes among optimal bipolar leads under different heart rates. ECGs were obtained from healthy participants with a mean age of 25 ± 3.81 years (34% women), including 114 in sinus rhythm (SR) and 38 in sinus tachycardia (ST). The recordings were made using a newly designed atrial lead system (ALS), standard limb lead (SLL), modified limb lead (MLL), modified Lewis lead (LLM) and P-lead. P-wave features and Atrioventricular (AV) ratio were extracted for statistical analysis and ML classification. The optimum ML model was chosen to identify the best-performing optimal lead, which was selected based on the SLL metrics among different ML classifiers. CatBoost was found to outperform the other ML models in SLL-II with the highest accuracy and sensitivity of 0.82 and 0.90, respectively. The CatBoost model, amid other optimal leads, gave the best results for AL-I and AL-II (0.86 and 0.83 in accuracy and 0.91 and 0.93 in sensitivity). The developed CatBoost model selected AL-I and AL-II as the top two best-performing optimal leads for the enhanced acquisition of P-wave changes, which may be useful for diagnosing atrial arrhythmias.

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Data availability

The datasets that support the findings of this work are self-made datasets from real-time recorded ECG that are not publicly accessible. However, the datasets are  provided to the scientific community for research purposes in supplementary information.

Abbreviations

AF:

Atrial fibrillation

ALS:

Atrial lead system

AUC:

Area under the curve

CART:

Classification and regression tree

CVD:

Cardiovascular diseases

DT:

Decision trees

ECG:

Electrocardiogram

GBDT:

Gradient boosting decision trees

LLM:

Modified Lewis lead

MAX_DI:

Maximum value of the discriminant index

ML:

Machine learning

MLL-II:

Modified limb lead-II

RMS:

Root mean square

ROC:

Receiver operating characteristic

SHAP:

SHapley additive exPlanations

SLL-II:

Standard limb lead-II

SR:

Sinus rhythm

ST:

Sinus tachycardia

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Acknowledgements

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

Funding

The 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).

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Authors and Affiliations

  1. Department of Biotechnology and Medical Engineering, National Institute of Technology Rourkela, Rourkela, Odisha, 769008, India

    N. Prasanna Venkatesh, Bala Chakravarthy Neelapu, Kunal Pal & J. Sivaraman

  2. Department of Cardiac Sciences, Jaiprakash Hospital and Research Centre, Rourkela, Odisha, 769004, India

    R. Pradeep Kumar

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  1. N. Prasanna Venkatesh
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by NPV. Conceptualization, Validation, Formal analysis, Resources, Data Curation, Writing- Review & Editing, Visualization, Supervision, Project administration, Funding acquisition are carried out by BCN, KP, RPK, JS. The first draft of the manuscript was written by NPV and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to J. Sivaraman.

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All procedures performed in studies involving human participants were in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of National Institute of Technology Rourkela (NITR/IEC/2022/M/04; dated 09/06/2022).

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Prasanna Venkatesh, N., Pradeep Kumar, R., Chakravarthy Neelapu, B. et al. CatBoost-based improved detection of P-wave changes in sinus rhythm and tachycardia conditions: a lead selection study. Phys Eng Sci Med 46, 925–944 (2023). https://doi.org/10.1007/s13246-023-01274-z

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