Robust and Efficient Atrial Fibrillation Detection from Intracardiac Electrograms Using Minirocket

Celal Alagoz

Makale detayları

Robust and Efficient Atrial Fibrillation Detection from Intracardiac Electrograms Using Minirocket

Yazarlar:

Celal Alagoz

Yayıncı:

Kirikkale University

Ana Başlıklar:

Bioengineering (Other)

Dergi:

International Journal of Engineering Research and Development Vol.16 Issue 1, pp.432-447

Belge Tipi:

Makale

Yayın Tarihi:

1/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001310755

pISSN:

1308-5506

Anahtar kelimeler:

Özet:

 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 Minirockets 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.

 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 Minirockets 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.

 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 Minirockets 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.

 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 Minirocketın uygulanmasını keşfeder. Rodrigo et al. 2022 tarafından elde edilen veri setinin bir alt kümesi üzerinde karşılaştırmalı bir analiz gerçekleştirilir. Çalışma, Minirocketın 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, Minirocketın geleneksel makine öğrenimi algoritmalarına kıyasla verimliliğini değerlendirir. Sonuçlar, özellikle kısa sinyaller ve değişen eğitim büyüklükleri senaryolarında Minirocketın 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.

Makale detayları

Robust and Efficient Atrial Fibrillation Detection from Intracardiac Electrograms Using Minirocket

Yazarlar:

Celal Alagoz

Yayıncı:

Kirikkale University

Ana Başlıklar:

Bioengineering (Other)

Dergi:

International Journal of Engineering Research and Development Vol.16 Issue 1, pp.432-447

Belge Tipi:

Makale

Yayın Tarihi:

1/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001310755

pISSN:

1308-5506

Atrial Fibrillation

Intracardiac Electrograms

Machine Learning

Time Series ClassificationAtrial Fibrilasyon

Elektrogramlar

Makine Öğrenme

Zaman Serisi Sınıflandırma

Sayı

Makale detayları

Yazarlar: Celal Alagoz

Yayıncı: Kirikkale University

Ana Başlıklar: Bioengineering (Other)

Dergi: International Journal of Engineering Research and Development Vol.16 Issue 1, pp.432-447

Belge Tipi: Makale

Yayın Tarihi: 1/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001310755

pISSN: 1308-5506

Sayı

Yazarlar:

Celal Alagoz

Yayıncı:

Kirikkale University

Ana Başlıklar:

Bioengineering (Other)

Dergi:

International Journal of Engineering Research and Development Vol.16 Issue 1, pp.432-447

Belge Tipi:

Makale

Yayın Tarihi:

1/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001310755

pISSN:

1308-5506