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Journals / Celal Bayar Üniversitesi Fen Bilimleri Dergisi / 2024 / Cilt: 20 Sayı: 4

Detection of Atrial Fibrillation with Custom Designed Wavelet-based Convolutional Autoencoder

Öykü Eravcı(YAŞAR ÜNİVERSİTESİ, LİSANSÜSTÜ EĞİTİM ENSTİTÜSÜ)0009-0009-8309-982X
Nalan Özkurt(YAŞAR ÜNİVERSİTESİ)0000-0002-7970-198X
Özlem Memiş(EGE ÜNİVERSİTESİ, EGE TIP FAKÜLTESİ)0009-0001-8341-0034
Evrim Şimşek(EGE ÜNİVERSİTESİ, EGE TIP FAKÜLTESİ)0000-0003-3183-8060
Pages
28–39
Publication Date
2024-12-29
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Moreover, it is observed that utilization of wavelet methods along with autoencoder model has a great potential for biomedical signal processing systems.

📌 3 Key Takeaways

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Objective & Hypothesis:Remote monitoring of patients is of great importance in terms of early diagnosis of diseases and improving people's quality of life.
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Key Findings & Data:The proposed approach received average scores of 95.45%, 99.99%, 90.90%, and 95.23% for accuracy, precision, recall, and F1, respectively, on a large selection of publicly available datasets.
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Conclusion & Impact:Moreover, it is observed that utilization of wavelet methods along with autoencoder model has a great potential for biomedical signal processing systems.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Atrial fibrillation detection · ECG · Autoencoder

Abstract

Remote monitoring of patients is of great importance in terms of early diagnosis of diseases and improving people's quality of life. With the rapid development of deep learning techniques, wearable health technologies have leaped forward. This has made the automatic diagnosis even more important. In this study, we provide a deep learning approach for classifying Atrial Fibrillation (AF) arrhythmia that uses a customized wavelet-based convolutional autoencoder (WCAE) model. WCAE is employed as an anomaly detector, which combines the time-frequency domain examination ability of wavelet and the data-driven feature learning capability of convolutional autoencoders. The proposed approach received average scores of 95.45%, 99.99%, 90.90%, and 95.23% for accuracy, precision, recall, and F1, respectively, on a large selection of publicly available datasets. The outcomes of the experiments demonstrate the significance of using deep learning-based models in diagnosing AF. Moreover, it is observed that utilization of wavelet methods along with autoencoder model has a great potential for biomedical signal processing systems.

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Cite This Article

Eravcı, Ö., Özkurt, N., Memiş, Ö., Şimşek, E. (2024). Detection of Atrial Fibrillation with Custom Designed Wavelet-based Convolutional Autoencoder. Celal Bayar Üniversitesi Fen Bilimleri Dergisi, Cilt: 20 Sayı: 4, 28–39. https://doi.org/10.18466/cbayarfbe.1508153

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