PHOTOPLETHYSMOGRAPHY BASED BLOOD PRESSURE ESTIMATION USING SYNCHROSQUEEZING TRANSFORM AND DEEP LEARNING

Yeşim Hekim Tanç; Mahmut Öztürk

doi:10.17780/ksujes.1376860

EN TR

PHOTOPLETHYSMOGRAPHY BASED BLOOD PRESSURE ESTIMATION USING SYNCHROSQUEEZING TRANSFORM AND DEEP LEARNING

Abstract

Cardiovascular diseases are one of the deadliest health problems. Hypertension is the most common reason for cardiovascular diseases. Keeping the blood pressure (BP) level under control is the only way to protect against the deadly results of hypertension. Therefore, monitoring BP regularly makes it possible to detect dangerous conditions in patients with hypertension. With the rapid developments in computers and sensor technologies, it is becoming possible to monitor BP levels continuously by using photoplethysmogram (PPG) signals. This work presents a non-invasive BP prediction method using one channel PPG signal. We employed the Synchrosqueezing Transform to obtain Time-Frequency (TF) images of the PPG signals. The TF images were used to feed a pre-trained deep neural network. We estimated the BP levels inside the 5-second intervals. Our method estimates BP levels with a mean error (ME) of 0.2148 mmHg and -0.0370 mmHg in the systolic and diastolic blood pressure (SBP and DBP) respectively. The ME values of our method are in the applicable levels. The standard deviation (SD) of our method is 5.0642 mmHg for DBP and 10.9904 mmHg for SBP. The upper limit specified by the AAMI is 8 mmHg. Also, our method is coherent with grades A and B according to the BHS standard.

Keywords

SENKRON SIKIŞTIRMA DÖNÜŞÜMÜ VE DERİN ÖĞRENME KULLANILARAK FOTOPLETİSMOGRAFİ TABANLI KAN BASINCI KESTİRİMİ

Öz

Kalp ve damar hastalıkları en ölümcül sağlık sorunlarından biridir. Hipertansiyon, kardiyovasküler hastalıkların en yaygın nedenidir. Tansiyon düzeyini kontrol altında tutmak, hipertansiyonun ölümcül sonuçlarından kurtulmanın tek yoludur. Bu nedenle, hipertansiyonu olan hastalar için kan basıncının (KB) düzenli olarak izlenmesi, tehlikeli durumların tespitini mümkün kılar. Bilgisayar ve sensör teknolojilerindeki hızlı gelişmeler ile fotopletismogram (PPG) sinyalleri kullanılarak kan basıncının sürekli olarak izlenmesi mümkün hale gelmektedir. Bu çalışmada, tek kanallı PPG sinyali kullanan invazif olmayan bir KB tahmin yöntemi sunuyoruz. PPG sinyallerinin Zaman-Frekans (ZF) görüntülerini elde etmek için Eş Zamanlı Sıkıştırma Dönüşümünü kullandık. ZF görüntüleri, önceden eğitilmiş bir derin sinir ağını beslemek için kullanıldı. KB seviyelerini 5 saniyelik aralıklarla tahmin ettik. Yöntemimiz, sistolik ve diyastolik kan basıncı (SKB ve DKB) seviyelerini sırasıyla 0,2148 mmHg ve -0,0370 mmHg ortalama hata (ME) ile tahmin eder. Yöntemimizin ME değerleri uygulanabilir seviyelerdedir. Yöntemimizin standart sapması (SS) DKB için 5.0642 mmHg, SKB için 10.9904 mmHg değerindedir. AAMI tarafından belirlenmiş olan üst limit 8 mmHg değerindedir. Ayrıca yöntemimiz BHS standardına göre A ve B sınıfları ile uyumludur.

Anahtar Kelimeler

Supporting Institution

Scientific Research Projects Coordination Unit of Istanbul University - Cerrahpasa

Project Number

FBA-2022-36694

Thanks

This work was supported by Scientific Research Projects Coordination Unit of Istanbul University - Cerrahpasa with project number FBA-2022-36694.

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Details

Primary Language

English

Subjects

Image Processing , Pattern Recognition , Deep Learning

Journal Section

Research Article

Authors

Yeşim Hekim Tanç
0000-0001-8029-4253
Türkiye

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

Publication Date

March 3, 2024

Submission Date

October 16, 2023

Acceptance Date

November 7, 2023

Published in Issue

Year 2024 Volume: 27 Number: 1

DOI

https://doi.org/10.17780/ksujes.1376860

IZ

https://izlik.org/JA34CF56GJ

Cite

RIS / Bibtex

APA

Hekim Tanç, Y., & Öztürk, M. (2024). PHOTOPLETHYSMOGRAPHY BASED BLOOD PRESSURE ESTIMATION USING SYNCHROSQUEEZING TRANSFORM AND DEEP LEARNING. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 27(1), 243-255. https://doi.org/10.17780/ksujes.1376860