Arrhythmia Classification with Single-Channel Features Extracted from “A Large-Scale 12-Lead ECG Database for Arrhythmia Study”

Fira, Monica; Goraș, Liviu; Fira, Lucian; Popa, Radu Florin; Costin, Hariton-Nicolae

doi:10.3390/s25185621

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Open AccessArticle

Arrhythmia Classification with Single-Channel Features Extracted from “A Large-Scale 12-Lead ECG Database for Arrhythmia Study”

by

Monica Fira

^1,*

,

Liviu Goraș

^1,2,

Lucian Fira

²,

Radu Florin Popa

³ and

Hariton-Nicolae Costin

¹

Institute of Computer Science, Romanian Academy, Iasi Branch, 700481 Iasi, Romania

²

Faculty of Electronics, Telecommunications & Information Technology, Gheorghe Asachi Technical University of Iasi, 700050 Iasi, Romania

³

Department of Vascular Surgery, Grigore T. Popa University of Medicine and Pharmacy of Iasi, 700115 Iasi, Romania

^*

Author to whom correspondence should be addressed.

Sensors 2025, 25(18), 5621; https://doi.org/10.3390/s25185621

Submission received: 16 July 2025 / Revised: 6 September 2025 / Accepted: 8 September 2025 / Published: 9 September 2025

(This article belongs to the Special Issue Advances in E-health, Biomedical Sensing, Biosensing Applications)

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Abstract

This study assesses how classical and modern features extracted from a single ECG lead (II) influence automated arrhythmia classification. Using the Large Scale 12-Lead Electrocardiogram Database for Arrhythmia Study and MATLAB^®, we compared traditional morphological measures (e.g., QRS duration, QT interval, atrial/ventricular rates) with advanced time-, frequency-, and nonlinear-domain descriptors. The method classifies ECGs into four or eight categories using 15–39 features, either automatically selected or combined. In the eight-class task, 29–39 features yielded 69% accuracy; in the four-class task, 15 MRMR-selected features achieved 94.2% accuracy. A key strength is efficiency: relying on a single lead reduces preprocessing, storage, and classification time by a factor of ~12 compared with 12-lead approaches. These findings show that advanced descriptors from a single lead can match multi-lead performance, enabling practical, scalable clinical applications.

Keywords: ECG; arrhythmia classification; feature extraction; machine learning

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MDPI and ACS Style

Fira, M.; Goraș, L.; Fira, L.; Popa, R.F.; Costin, H.-N. Arrhythmia Classification with Single-Channel Features Extracted from “A Large-Scale 12-Lead ECG Database for Arrhythmia Study”. Sensors 2025, 25, 5621. https://doi.org/10.3390/s25185621

AMA Style

Fira M, Goraș L, Fira L, Popa RF, Costin H-N. Arrhythmia Classification with Single-Channel Features Extracted from “A Large-Scale 12-Lead ECG Database for Arrhythmia Study”. Sensors. 2025; 25(18):5621. https://doi.org/10.3390/s25185621

Chicago/Turabian Style

Fira, Monica, Liviu Goraș, Lucian Fira, Radu Florin Popa, and Hariton-Nicolae Costin. 2025. "Arrhythmia Classification with Single-Channel Features Extracted from “A Large-Scale 12-Lead ECG Database for Arrhythmia Study”" Sensors 25, no. 18: 5621. https://doi.org/10.3390/s25185621

APA Style

Fira, M., Goraș, L., Fira, L., Popa, R. F., & Costin, H.-N. (2025). Arrhythmia Classification with Single-Channel Features Extracted from “A Large-Scale 12-Lead ECG Database for Arrhythmia Study”. Sensors, 25(18), 5621. https://doi.org/10.3390/s25185621

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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Arrhythmia Classification with Single-Channel Features Extracted from “A Large-Scale 12-Lead ECG Database for Arrhythmia Study”

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