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

Öz

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.

Anahtar Kelimeler

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

Destekleyen Kurum

Scientific Research Projects Coordination Unit of Istanbul University - Cerrahpasa

Proje Numarası

FBA-2022-36694

Teşekkür

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

Kaynakça

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Görüntü İşleme, Örüntü Tanıma, Derin Öğrenme

Bölüm

Araştırma Makalesi

Yazarlar

Yeşim Hekim Tanç
0000-0001-8029-4253
Türkiye

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

Yayımlanma Tarihi

3 Mart 2024

Gönderilme Tarihi

16 Ekim 2023

Kabul Tarihi

7 Kasım 2023

Yayımlandığı Sayı

Yıl 2024 Cilt: 27 Sayı: 1

DOI

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

IZ

https://izlik.org/JA34CF56GJ

Kaynak Göster

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

AMA

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

Chicago

Hekim Tanç, Yeşim, ve Mahmut Öztürk. 2024. “PHOTOPLETHYSMOGRAPHY BASED BLOOD PRESSURE ESTIMATION USING SYNCHROSQUEEZING TRANSFORM AND DEEP LEARNING”. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi 27 (1): 243-55. https://doi.org/10.17780/ksujes.1376860.

EndNote

Hekim Tanç Y, Öztürk M (01 Mart 2024) PHOTOPLETHYSMOGRAPHY BASED BLOOD PRESSURE ESTIMATION USING SYNCHROSQUEEZING TRANSFORM AND DEEP LEARNING. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi 27 1 243–255.

IEEE

[1]Y. Hekim Tanç ve M. Öztürk, “PHOTOPLETHYSMOGRAPHY BASED BLOOD PRESSURE ESTIMATION USING SYNCHROSQUEEZING TRANSFORM AND DEEP LEARNING”, Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, c. 27, sy 1, ss. 243–255, Mar. 2024, doi: 10.17780/ksujes.1376860.

ISNAD

Hekim Tanç, Yeşim - Öztürk, Mahmut. “PHOTOPLETHYSMOGRAPHY BASED BLOOD PRESSURE ESTIMATION USING SYNCHROSQUEEZING TRANSFORM AND DEEP LEARNING”. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi 27/1 (01 Mart 2024): 243-255. https://doi.org/10.17780/ksujes.1376860.

JAMA

1.Hekim Tanç Y, Öztürk M. PHOTOPLETHYSMOGRAPHY BASED BLOOD PRESSURE ESTIMATION USING SYNCHROSQUEEZING TRANSFORM AND DEEP LEARNING. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi. 2024;27:243–255.

MLA

Hekim Tanç, Yeşim, ve Mahmut Öztürk. “PHOTOPLETHYSMOGRAPHY BASED BLOOD PRESSURE ESTIMATION USING SYNCHROSQUEEZING TRANSFORM AND DEEP LEARNING”. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, c. 27, sy 1, Mart 2024, ss. 243-55, doi:10.17780/ksujes.1376860.

Vancouver

1.Yeşim Hekim Tanç, Mahmut Öztürk. PHOTOPLETHYSMOGRAPHY BASED BLOOD PRESSURE ESTIMATION USING SYNCHROSQUEEZING TRANSFORM AND DEEP LEARNING. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi. 01 Mart 2024;27(1):243-55. doi:10.17780/ksujes.1376860