ECG SIGNAL CLASSIFICATION WITH DEEP LEARNING AND MACHINE LEARNING: REPRESENTATION OF QRS COMPLEXES IN TWO-DIMENSIONAL FRAMES AND DATA BALANCING WITH SMOTE

Mehmet Reşat Öner; İsmail Babaoğlu; Gülay Tezel

doi:10.17780/ksujes.1852101

TR EN

DERİN ÖĞRENME VE MAKİNE ÖĞRENİMİ İLE EKG SİNYALİ SINIFLANDIRMASI: İKİ BOYUTLU ÇERÇEVELERDE QRS KOMPLEKSLERİNİN TEMSİLİ VE SMOTE İLE VERİ DENGELEME

Abstract

Bu çalışma, bilgisayar destekli analiz yöntemleri aracılığıyla insan kaynaklı değişkenliği azaltarak güvenilir ECG sinyali yorumlamasını desteklemeyi amaçlamaktadır. Makine öğrenmesi ve derin öğrenme yöntemleri, ECG sınıflandırmasında iki boyutlu ECG temsillerini ve Synthetic Minority Over-sampling Technique (SMOTE) tabanlı veri dengelemenin etkisini incelemek amacıyla kullanılmıştır. Mevcut ECG sınıflandırma çalışmalarında genellikle sinyal temsili ve sınıf dengesizliği ayrı ayrı ele alınırken, bu çalışmada iki boyutlu QRS temsili ile SMOTE tabanlı veri dengelemenin etkileşimi tek bir deneysel çerçeve içerisinde birlikte incelenmiş ve bu yaklaşımların sınıflandırma performansı üzerindeki birleşik etkisi sistematik olarak analiz edilmiştir. Yapay Sinir Ağları (ANN), Evrişimsel Sinir Ağları (CNN) ve K-En Yakın Komşu (KNN) algoritmaları uygulanmış ve karşılaştırmalı olarak analiz edilmiştir. MIT-BIH Aritmi veri setinin 108 numaralı kaydından elde edilen ECG atımları, sınıflandırma amacıyla görsel tabanlı bir biçimde temsil edilmiştir. Ciddi sınıf dengesizliğini gidermek amacıyla SMOTE yalnızca eğitim verisine uygulanmış ve iki boyutlu ECG temsilleri üzerindeki etkisi açıkça incelenmiştir. Normal ve Anormal kalp atımları, tabakalı 5 katlı çapraz doğrulama stratejisi kullanılarak sınıflandırılmıştır. Deneysel sonuçlar, SMOTE uygulandıktan sonra CNN modelinin %99,82 ± 0,002 ağırlıklı ortalama F1-skoru ile en başarılı performansı elde ettiğini göstermiş; bu durum, iki boyutlu QRS temsili ile veri dengelemenin birlikte kullanımının otomatik ECG sınıflandırma performansını artırmadaki etkinliğini ortaya koymuştur.

Keywords

ECG SIGNAL CLASSIFICATION WITH DEEP LEARNING AND MACHINE LEARNING: REPRESENTATION OF QRS COMPLEXES IN TWO-DIMENSIONAL FRAMES AND DATA BALANCING WITH SMOTE

Abstract

This study aims to support reliable ECG signal interpretation by reducing human-dependent variability through computer-aided analysis methods. Machine learning and deep learning methods were employed to examine 2D ECG representations and Synthetic Minority Over-Sampling Technique (SMOTE)-based balancing in ECG classification. Unlike existing ECG classification studies that typically address signal representation and class imbalance separately, this study jointly investigates the interaction between two-dimensional QRS representation and SMOTE-based data balancing within a unified experimental framework, thereby providing a systematic analysis of their combined impact on classification performance. Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and K-Nearest Neighbors (KNN) algorithms were implemented and comparatively analyzed. ECG beats from record 108 of the MIT-BIH Arrhythmia dataset were represented in a vision-based form for classification. To address severe class imbalance, SMOTE was applied only to the training data, and its effect on two-dimensional ECG representations was explicitly examined. Normal and Abnormal heartbeats were classified using a stratified 5-fold cross-validation strategy. Experimental results demonstrated that the CNN model achieved the most successful performance after applying SMOTE, reaching a weighted average F1-score of 99.82% ± 0.002, highlighting the combined effectiveness of two-dimensional QRS representation and data balancing in improving automated ECG classification.

Keywords

References

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Details

Primary Language

English

Subjects

Deep Learning, Artificial Intelligence (Other)

Journal Section

Research Article

Authors

Mehmet Reşat Öner ^*
0000-0003-2084-0181
Türkiye

İsmail Babaoğlu
0000-0002-2503-1482
Türkiye

Gülay Tezel
0000-0003-1698-0106
Türkiye

Publication Date

March 3, 2026

Submission Date

December 30, 2025

Acceptance Date

February 10, 2026

Published in Issue

Year 2026 Volume: 29 Number: 1

DOI

https://doi.org/10.17780/ksujes.1852101

IZ

https://izlik.org/JA95BD94UY

Cite

RIS / Bibtex

APA

Öner, M. R., Babaoğlu, İ., & Tezel, G. (2026). ECG SIGNAL CLASSIFICATION WITH DEEP LEARNING AND MACHINE LEARNING: REPRESENTATION OF QRS COMPLEXES IN TWO-DIMENSIONAL FRAMES AND DATA BALANCING WITH SMOTE. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 29(1), 379-389. https://doi.org/10.17780/ksujes.1852101

AMA

1.Öner MR, Babaoğlu İ, Tezel G. ECG SIGNAL CLASSIFICATION WITH DEEP LEARNING AND MACHINE LEARNING: REPRESENTATION OF QRS COMPLEXES IN TWO-DIMENSIONAL FRAMES AND DATA BALANCING WITH SMOTE. KSU J. Eng. Sci. 2026;29(1):379-389. doi:10.17780/ksujes.1852101

Chicago

Öner, Mehmet Reşat, İsmail Babaoğlu, and Gülay Tezel. 2026. “ECG SIGNAL CLASSIFICATION WITH DEEP LEARNING AND MACHINE LEARNING: REPRESENTATION OF QRS COMPLEXES IN TWO-DIMENSIONAL FRAMES AND DATA BALANCING WITH SMOTE”. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi 29 (1): 379-89. https://doi.org/10.17780/ksujes.1852101.

EndNote

Öner MR, Babaoğlu İ, Tezel G (March 1, 2026) ECG SIGNAL CLASSIFICATION WITH DEEP LEARNING AND MACHINE LEARNING: REPRESENTATION OF QRS COMPLEXES IN TWO-DIMENSIONAL FRAMES AND DATA BALANCING WITH SMOTE. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi 29 1 379–389.

IEEE

[1]M. R. Öner, İ. Babaoğlu, and G. Tezel, “ECG SIGNAL CLASSIFICATION WITH DEEP LEARNING AND MACHINE LEARNING: REPRESENTATION OF QRS COMPLEXES IN TWO-DIMENSIONAL FRAMES AND DATA BALANCING WITH SMOTE”, KSU J. Eng. Sci., vol. 29, no. 1, pp. 379–389, Mar. 2026, doi: 10.17780/ksujes.1852101.

ISNAD

Öner, Mehmet Reşat - Babaoğlu, İsmail - Tezel, Gülay. “ECG SIGNAL CLASSIFICATION WITH DEEP LEARNING AND MACHINE LEARNING: REPRESENTATION OF QRS COMPLEXES IN TWO-DIMENSIONAL FRAMES AND DATA BALANCING WITH SMOTE”. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi 29/1 (March 1, 2026): 379-389. https://doi.org/10.17780/ksujes.1852101.

JAMA

1.Öner MR, Babaoğlu İ, Tezel G. ECG SIGNAL CLASSIFICATION WITH DEEP LEARNING AND MACHINE LEARNING: REPRESENTATION OF QRS COMPLEXES IN TWO-DIMENSIONAL FRAMES AND DATA BALANCING WITH SMOTE. KSU J. Eng. Sci. 2026;29:379–389.

MLA

Öner, Mehmet Reşat, et al. “ECG SIGNAL CLASSIFICATION WITH DEEP LEARNING AND MACHINE LEARNING: REPRESENTATION OF QRS COMPLEXES IN TWO-DIMENSIONAL FRAMES AND DATA BALANCING WITH SMOTE”. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, vol. 29, no. 1, Mar. 2026, pp. 379-8, doi:10.17780/ksujes.1852101.

Vancouver

1.Mehmet Reşat Öner, İsmail Babaoğlu, Gülay Tezel. ECG SIGNAL CLASSIFICATION WITH DEEP LEARNING AND MACHINE LEARNING: REPRESENTATION OF QRS COMPLEXES IN TWO-DIMENSIONAL FRAMES AND DATA BALANCING WITH SMOTE. KSU J. Eng. Sci. 2026 Mar. 1;29(1):379-8. doi:10.17780/ksujes.1852101