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

Öykü Eravcı; Nalan Özkurt; Özlem Memiş; Evrim Şimşek

Makale detayları

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

Yazarlar:

Öykü Eravcı | Nalan Özkurt | Özlem Memiş | Evrim Şimşek

Yayıncı:

Manisa Celal Bayar University

Ana Başlıklar:

Yazılım Mühendisliği (Diğer) | Biyomedikal Tanı

Dergi:

Celal Bayar Üniversitesi Fen Bilimleri Dergisi Vol.20 Issue 4, pp.28-39

Belge Tipi:

Makale

Yayın Tarihi:

12/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001336635

pISSN:

1305-130X

Anahtar kelimeler:

Özet:

 Remote monitoring of patients is of great importance in terms of early diagnosis of diseases and improving peoples 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.

 Remote monitoring of patients is of great importance in terms of early diagnosis of diseases and improving peoples 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.

 Remote monitoring of patients is of great importance in terms of early diagnosis of diseases and improving peoples 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.

 Remote monitoring of patients is of great importance in terms of early diagnosis of diseases and improving peoples 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.

Makale detayları

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

Yazarlar:

Öykü Eravcı | Nalan Özkurt | Özlem Memiş | Evrim Şimşek

Yayıncı:

Manisa Celal Bayar University

Ana Başlıklar:

Yazılım Mühendisliği (Diğer) | Biyomedikal Tanı

Dergi:

Celal Bayar Üniversitesi Fen Bilimleri Dergisi Vol.20 Issue 4, pp.28-39

Belge Tipi:

Makale

Yayın Tarihi:

12/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001336635

pISSN:

1305-130X

Atrial fibrillation detection

ECG

Autoencoder

Deep learning

Discrete wavelet transform

Sayı

Makale detayları

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

Yazarlar: Öykü Eravcı | Nalan Özkurt | Özlem Memiş | Evrim Şimşek

Yayıncı: Manisa Celal Bayar University

Ana Başlıklar: Yazılım Mühendisliği (Diğer) | Biyomedikal Tanı

Dergi: Celal Bayar Üniversitesi Fen Bilimleri Dergisi Vol.20 Issue 4, pp.28-39

Belge Tipi: Makale

Yayın Tarihi: 12/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001336635

pISSN: 1305-130X

Atrial fibrillation detection

ECG

Autoencoder

Deep learning

Discrete wavelet transform

Sayı

Yazarlar:

Öykü Eravcı | Nalan Özkurt | Özlem Memiş | Evrim Şimşek

Yayıncı:

Manisa Celal Bayar University

Ana Başlıklar:

Yazılım Mühendisliği (Diğer) | Biyomedikal Tanı

Dergi:

Celal Bayar Üniversitesi Fen Bilimleri Dergisi Vol.20 Issue 4, pp.28-39

Belge Tipi:

Makale

Yayın Tarihi:

12/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001336635

pISSN:

1305-130X