BLOOD PRESSURE AND HEART RATE ESTIMATION VIA TQWT BASED DECOMPOSITION OF PPG SIGNALS

Fatma Sevde Köklükaya; Mahmut Öztürk

doi:10.17780/ksujes.1356287

TR EN

PPG SİNYALLERİNİN TQWT TABANLI AYRIŞTIRILMASI YOLUYLA KAN BASINCI VE KALP ATIŞ HIZI TAHMİNİ

Öz

Fotopletismografi (PPG) sinyalleri, invaziv olmayan, hızlı ve basit kayıt teknikleri nedeniyle tıbbi uygulamalar için daha popüler ve umut verici hale geliyor. Kan basıncını (KB) ve kalp atış hızı (KAH) seviyelerini izlemek için geleneksel invaziv ve manşet tabanlı ölçüm teknikleri yerine PPG sinyallerinin kullanılması mümkündür ve KB ve KAH seviyelerinin sürekli takibi, yüksek ölçüm doğruluklarıyla gerçekleştirilebilir. Bu gelişmeler özellikle yüksek tansiyon ve kalp sorunu yaşayan kişiler için çok önemli ve faydalıdır. Bu çalışmada, PPG sinyallerini alt sinyallere ayrıştırmak ve her bir alt sinyalden ve ana sinyalden bazı istatistiksel özellikler çıkarmak için Ayarlanabilir Q-faktörü Dalgacık Dönüşümü'nü (TQWT) kullanmayı öneriyoruz. Diastolik kan basıncı (DKB), sistolik kan basıncı (SKB) ve kalp atış hızı (KAH) değerlerinin tahmin edilmesinde Yapay Sinir Ağları (YSA), Rastgele Ormanlar (RF) ve Destek Vektör Makineleri (SVM) algoritmaları kullanılmaktadır. Geleneksel yöntemlerle ölçülen PPG sinyalleri, DKB, SKB ve KAH değerleri Çin Guilin Halk Hastanesi'nin açık veri setinden elde edildi. Bu veri seti 219 kişinin bilgilerini içermektedir. Her bir makine öğrenmesi yöntemi, özelliklere ayrı ayrı uygulanmış ve regresyon analizi sonuçları, hata oranları ve gerçek ve tahmin edilen değerler arasındaki korelasyonlar kullanılarak yorumlanmıştır. Sonuçlar RF algoritmasının DKB, SKB ve KAH seviyelerinin tahmininde YSA ve SVM'den daha başarılı olduğunu göstermektedir.

Anahtar Kelimeler

BLOOD PRESSURE AND HEART RATE ESTIMATION VIA TQWT BASED DECOMPOSITION OF PPG SIGNALS

Abstract

Photoplethysmography (PPG) signals are getting more popular and promising for medical applications because of the non-invasive, fast, and simple recording techniques. Using PPG signals for monitoring the blood pressure (BP) and heart rate (HR) levels instead of traditional invasive and cuff-based measurement techniques is possible and continuous tracing of BP and HR levels can be accomplished with high measurement accuracies. These developments are very important and helpful, especially for people suffering from high tension and cardiac problems. In this study, we propose to use Tunable Q-factor Wavelet Transform (TQWT) for decomposing the PPG signals into sub-signals and extracting some statistical features from each of the sub-signals and main signal. Artificial Neural Networks (ANN), Random Forests (RF), and Support Vector Machines (SVM) algorithms are employed to estimate diastolic blood pressure (DBP), systolic blood pressure (SBP), and heart rate (HR) values. PPG signals, DBP, SBP, and HR values which were measured with traditional methods were obtained from the open dataset of Guilin People’s Hospital of China. This dataset includes information of 219 individuals. Each machine learning method was applied to the features separately, and the results of the regression analysis were interpreted by using the error rates and correlations between the actual and estimated values. Results show that the RF algorithm is more successful than ANN and SVM for the estimation of DBP, SBP, and HR levels.

Keywords

Kaynakça

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Görüntü İşleme , Makine Öğrenme (Diğer) , Elektrik Mühendisliği (Diğer)

Bölüm

Araştırma Makalesi

Yazarlar

Fatma Sevde Köklükaya
0000-0002-9348-0925
Türkiye

Mahmut Öztürk ^*
0000-0003-2600-7051
Türkiye

Yayımlanma Tarihi

3 Aralık 2023

Gönderilme Tarihi

6 Eylül 2023

Kabul Tarihi

13 Ekim 2023

Yayımlandığı Sayı

Yıl 2023 Cilt: 26 Sayı: 4

DOI

https://doi.org/10.17780/ksujes.1356287

IZ

https://izlik.org/JA46MB76GP

Kaynak Göster

RIS / Bibtex

APA

Köklükaya, F. S., & Öztürk, M. (2023). BLOOD PRESSURE AND HEART RATE ESTIMATION VIA TQWT BASED DECOMPOSITION OF PPG SIGNALS. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 26(4), 1050-1060. https://doi.org/10.17780/ksujes.1356287