Deep learning based hypertension detection with ballistocardiography signals

Salih Taha Alperen Özçelik; Hakan Uyanık; Abdülkadir Şengür

Makale detayları

Deep learning based hypertension detection with ballistocardiography signals

Yazarlar:

Salih Taha Alperen Özçelik | Hakan Uyanık | Abdülkadir Şengür

Yayıncı:

Nigde Omer Halisdemir University

Ana Başlıklar:

Elektrik Mühendisliği

Dergi:

Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi Vol.12 Issue 3, pp.704-715

Belge Tipi:

Makale

Yayın Tarihi:

7/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001295895

pISSN:

2564-6605

Anahtar kelimeler:

Özet:

 Blood pressure is the pressure exerted by the blood in the vessels against the vessel walls. If this pressure value is above normal levels, it is known as high blood pressure HBP or hypertension HPT. The diagnosis of this health problem, which negatively affects the quality of life, often causes various damage to organs and can lead to death, is very important. In this study, a method for automatic diagnosis of HPT using ballistocardiography BCG signals is proposed. For this, BCG signals are transferred to the time-frequency domain using the continuous wavelet transform filter bank CWTFB method. The quality of the conversion has been increased by making some parameter settings in the conversion method used while performing these operations. Then, the obtained images were classified with ResNet18, ResNet50, VGG16 and AlexNet convolutional neural networks and the obtained results were compared. With the proposed method, classification accuracy of 98.92, 99.34, 99.22 and 98.07 was obtained for ResNet18, ResNet50, VGG16 and AlexNet architectures, respectively. These high classification results obtained prove that the proposed method can be used for the diagnosis of hypertension.

 Blood pressure is the pressure exerted by the blood in the vessels against the vessel walls. If this pressure value is above normal levels, it is known as high blood pressure HBP or hypertension HPT. The diagnosis of this health problem, which negatively affects the quality of life, often causes various damage to organs and can lead to death, is very important. In this study, a method for automatic diagnosis of HPT using ballistocardiography BCG signals is proposed. For this, BCG signals are transferred to the time-frequency domain using the continuous wavelet transform filter bank CWTFB method. The quality of the conversion has been increased by making some parameter settings in the conversion method used while performing these operations. Then, the obtained images were classified with ResNet18, ResNet50, VGG16 and AlexNet convolutional neural networks and the obtained results were compared. With the proposed method, classification accuracy of 98.92, 99.34, 99.22 and 98.07 was obtained for ResNet18, ResNet50, VGG16 and AlexNet architectures, respectively. These high classification results obtained prove that the proposed method can be used for the diagnosis of hypertension.

 Blood pressure is the pressure exerted by the blood in the vessels against the vessel walls. If this pressure value is above normal levels, it is known as high blood pressure HBP or hypertension HPT. The diagnosis of this health problem, which negatively affects the quality of life, often causes various damage to organs and can lead to death, is very important. In this study, a method for automatic diagnosis of HPT using ballistocardiography BCG signals is proposed. For this, BCG signals are transferred to the time-frequency domain using the continuous wavelet transform filter bank CWTFB method. The quality of the conversion has been increased by making some parameter settings in the conversion method used while performing these operations. Then, the obtained images were classified with ResNet18, ResNet50, VGG16 and AlexNet convolutional neural networks and the obtained results were compared. With the proposed method, classification accuracy of 98.92, 99.34, 99.22 and 98.07 was obtained for ResNet18, ResNet50, VGG16 and AlexNet architectures, respectively. These high classification results obtained prove that the proposed method can be used for the diagnosis of hypertension.

 Kan basıncı, damarlardaki kanın damar duvarlarına uyguladığı basınçtır. Bu basınç değerinin normal kabul edilen seviyelerin üzerinde seyir etmesi yüksek tansiyon YT veya hipertansiyon HPT olarak bilinir. Hayat kalitesini negatif yönde etkileyen, çoğu zaman organlarda çeşitli tahribatlara sebep olan ve ölümlere yol açabilen bu sağlık probleminin teşhisi oldukça önemlidir. Bu çalışmada, balistokardiyografi BKG sinyalleri kullanılarak HPTnin otomatik teşhisine yönelik bir yöntem önerilmiştir. Bunun için BKG sinyalleri, sürekli dalgacık dönüşümü filtre bankası SDDFB yöntemi kullanılarak zaman-frekans domenine taşınmıştır. Bu işlemler yapılırken kullanılan dönüşüm yönteminde bazı parametre ayarları gerçekleştirilerek dönüşümün kalitesi arttırılmıştır. Daha sonra elde edilen görüntüler ResNet18, ResNet50, VGG16 ve AlexNet evrişimsel sinir ağlarıyla sınıflandırılmış ve elde edilen sonuçlar karşılaştırılmıştır. Önerilen yöntem ile ResNet18, ResNet50, VGG16 ve AlexNet mimarileri için sırasıyla 98,92, 99,34 ve 99,22 ve 98,07 sınıflandırma doğruluğu elde etmiştir. Elde edilen bu yüksek sınıflandırma sonuçları önerilen yöntemin hipertansiyon teşhisi için kullanılabileceğini ispatlar niteliktedir.

Makale detayları

Deep learning based hypertension detection with ballistocardiography signals

Yazarlar:

Salih Taha Alperen Özçelik | Hakan Uyanık | Abdülkadir Şengür

Yayıncı:

Nigde Omer Halisdemir University

Ana Başlıklar:

Elektrik Mühendisliği

Dergi:

Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi Vol.12 Issue 3, pp.704-715

Belge Tipi:

Makale

Yayın Tarihi:

7/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001295895

pISSN:

2564-6605

Hypertension

BCG signals

Deep learning

Continuous wavelet transform filter bankHipertansiyon

BKG sinyali

Derin Öğrenme

Sürekli Dalgacık Dönüşümü Filtre Bankası

Sayı

Makale detayları

Yazarlar: Salih Taha Alperen Özçelik | Hakan Uyanık | Abdülkadir Şengür

Yayıncı: Nigde Omer Halisdemir University

Ana Başlıklar: Elektrik Mühendisliği

Dergi: Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi Vol.12 Issue 3, pp.704-715

Belge Tipi: Makale

Yayın Tarihi: 7/2023

Belge Dili: Türkçe

Document ID (Org No.): 2001295895

pISSN: 2564-6605

Sayı

Yazarlar:

Salih Taha Alperen Özçelik | Hakan Uyanık | Abdülkadir Şengür

Yayıncı:

Nigde Omer Halisdemir University

Ana Başlıklar:

Elektrik Mühendisliği

Dergi:

Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi Vol.12 Issue 3, pp.704-715

Belge Tipi:

Makale

Yayın Tarihi:

7/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001295895

pISSN:

2564-6605