Robust and Efficient Atrial Fibrillation Detection from Intracardiac Electrograms Using Minirocket

Celal Alagoz

doi:10.29137/umagd.1409437

EN TR

Robust and Efficient Atrial Fibrillation Detection from Intracardiac Electrograms Using Minirocket

Abstract

Atrial Fibrillation (AF) detection from intracardiac Electrogram (EGM) signals is a critical aspect of cardiovascular health monitoring. This study explores the application of Minirocket, a time series classification (TSC) algorithm, for robust and efficient AF detection. A comparative analysis is conducted against a deep learning approach using a subset of the dataset from Rodrigo et al. (2022). The study investigates the robustness of Minirocket in the face of shorter EGM sequences and varying training sizes, essential for real-world applications such as wearable and implanted devices. Empirical runtime analysis further assesses the efficiency of Minirocket in comparison to conventional machine learning (ML) algorithms. The results showcase Minirocket's notable performance, especially in scenarios with shorter signals and varying training sizes, making it a promising candidate for streamlined AF detection in emerging cardiovascular monitoring technologies. This research contributes to the optimization of AF detection algorithms for increased efficiency and adaptability to dynamic clinical scenarios.

Keywords

Atrial Fibrillation , Intracardiac Electrograms , Machine Learning , Time Series Classification

Minirocket Kullanarak Güçlendirilmiş ve Verimli Atriyal Fibrilasyon Tespiti

Öz

Atriyal Fibrilasyon (AF) tespiti, intrakardiyak Elektrogram (EGM) sinyallerinin kritik bir yönüdür ve kardiyovasküler sağlık izlemesinin önemli bir parçasını oluşturur. Bu çalışma, güçlendirilmiş ve verimli AF tespiti için bir zaman serisi sınıflandırma (TSC) algoritması olan Minirocket'ın uygulanmasını keşfeder. Rodrigo et al. (2022) tarafından elde edilen veri setinin bir alt kümesi üzerinde karşılaştırmalı bir analiz gerçekleştirilir. Çalışma, Minirocket'ın kısa EGM dizileri ve değişen eğitim büyüklükleri karşısındaki direncini araştırır; bu, giyilebilir ve implante edilebilir cihazlar gibi gerçek dünya uygulamaları için önemlidir. Ampirik çalışma süresi analizi, Minirocket'ın geleneksel makine öğrenimi algoritmalarına kıyasla verimliliğini değerlendirir. Sonuçlar, özellikle kısa sinyaller ve değişen eğitim büyüklükleri senaryolarında Minirocket'ın dikkate değer performansını sergiler, böylece geleceğin kardiyovasküler izleme teknolojilerinde AF tespiti için umut vadeden bir aday olarak öne çıkar. Bu araştırma, AF tespit algoritmalarının verimliliğinin artırılması ve dinamik klinik senaryolara adapte edilmesi konusunda katkıda bulunur.

Anahtar Kelimeler

Atrial Fibrilasyon , Elektrogramlar , Makine Öğrenme , Zaman Serisi Sınıflandırma

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Details

Primary Language

English

Subjects

Bioengineering (Other)

Journal Section

Research Article

Authors

Celal Alagoz ^*
0000-0001-9812-1473
Türkiye

Publication Date

January 31, 2024

Submission Date

December 25, 2023

Acceptance Date

January 16, 2024

Published in Issue

Year 2024 Volume: 16 Number: 1

DOI

https://doi.org/10.29137/umagd.1409437

IZ

https://izlik.org/JA59LT62YD

APA

Alagoz, C. (2024). Robust and Efficient Atrial Fibrillation Detection from Intracardiac Electrograms Using Minirocket. International Journal of Engineering Research and Development, 16(1), 432-447. https://doi.org/10.29137/umagd.1409437

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Kırıkkale University, Faculty of Engineering and Natural Science, 71450 Yahşihan / Kırıkkale, Türkiye.

ijerad@kku.edu.tr