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

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

American National Standards Institute. ANSI/AAMI/ISO 81060–2:2013.Non-invasive sphygmomanometers - Part 2: clinical investigation of automated measurement type. http://webstore.ansi.org, Accessed July 15, 2017.
Arnold, M. A., Liu, L.,& Olesberg, J. T., (2007), Selectivity assessment of noninvasive glucose measurements based on analysis of multivariate calibration vectors, Journal of Diabetes Science and Technology, 1(4), 454–462.
Auger F., Flandrin P., Lin Y., et al, (2013). Time-Frequency reassignment and synchrosqueezing, IEEE Signal Processing Magazine, 30:32-41.
Chao, P. C. , Wu, P. C. -C. D., Nguyen, H. B. -S. Nguyen, P. -C. Huang and V. -H. Le, (2021). The Machine Learnings Leading the Cuffless PPG Blood Pressure Sensors Into the Next Stage, IEEE Sensors Journal, 21(11), 12498-12510, doi: 10.1109/JSEN.2021.3073850.
Daubechies, I., Lu, J., Wu, H.T., (2011), Synchrosqueezed wavelet transform: an empirical mode decomposition like tool, Appl. Comput. Harmon. Anal., 30(2), 243-261.
Elgendi M, Norton I, Brearley M, Abbott D, Schuurmans D. (2013), Systolic peak detection in acceleration photoplethysmograms measured from emergency responders in tropical conditions. PLoS One, 8:e76585.
El-Hajj, C., and Kyriacou, P.A, (2021), Cuffless blood pressure estimation from PPG signals and its derivatives using deep learning models, Biomedical Signal Processing and Control, 70, 102984.
El-Hajj, C., and Kyriacou, P.A., (2020), A review of machine learning techniques in photoplethysmography for the non-invasive cuff-less measurement of blood pressure, Biomedical Signal Processing and Control, 58,.

Esmaelpoor, J., Moradi, M. H., and Kadkhodamohammadi, A., (2020), A multistage deep neural network model for blood pressure estimation using photoplethysmogram signals, Computer in Biology and Medicine, 120, 103719.
He, K., Zhang, X., Ren, S., and Sun, J., (2016), Deep Residual Learning for Image Recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770-778, doi: 10.1109/CVPR.2016.90.
Johnston, William S., (2006), Development of a Signal Processing Library for Extraction of SpO2, HR, HRV, and RR from PhotoplethysmographicWaveforms, Masters Theses (All Theses, All Years), 919, https://digitalcommons.wpi.edu/etd-theses/919.
Kachuee, M., Kiani, M. M., Mohammadzade, H., and Shabany, M., (2017), Cuffless Blood Pressure Estimation Algorithms for Continuous Health-Care Monitoring, IEEE Transactions on Biomedical Engineering, 64,(4), 859-869.
Kachuee, M., Kiani, M. M., Mohammadzade, H., and Shabany, M., (2015), Cuff-less high-accuracy calibration-free blood pressure estimation using pulse transit time, 2015 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1006-1009.
Kayadelen, C., Altay, G., Önal, S., & Önal, Y., (2022) Sequential minimal optimization for local scour around bridge piers, Marine Georesources & Geotechnology, 40:4, 462-472, DOI: 10.1080/1064119X.2021.1907635
Kim, B.S., & Yoo S. K.,(2006), Motion artifact reduction in photoplethysmography using independent component analysis, IEEE Transactions on Bio-Medical Engineering, 53 (3), 566–568.
Kraitl J, Ewald H (2005), Optical non-invasive methods for characterization of the human health status. In: Presented at the 21st international conference on sensing technology, Palmerston North, New Zealand.
Kurylyak, Y., Lamonaca, F., and Grimaldi, D., (2013), A Neural Network-based method for continuous blood pressure estimation from a PPG signal, 2013 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), pp. 280-283.
Lang, N., Kalischek, N., Armston, J., Schindler, K., Dubayah, R., & Wegner, J. D. (2022). Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles. Remote Sensing of Environment, 268, 112760.
Lázaro, J., Gil, E., Vergara, J. M., & Laguna, P., (2014), Pulse rate variability analysis for discrimination of sleep-apnea-related decreases in the amplitude fluctuations of pulse photoplethysmographic signal in children. IEEE Journal of Biomedical and Health Informatics, 18(1), 240-246.
Liang, Y., Chen, Z., Ward, R., & Elgendi, M. (2018), Photoplethysmography and deep learning: enhancing hypertension risk stratification, Biosensors, 8(4), 101.
Liu Z, Zhou B, Li Y, Tang M, Miao F. (2020), Continuous Blood Pressure Estimation From Electrocardiogram and Photoplethysmogram During Arrhythmias. Front Physiol. Sep 9;11:575407. doi: 10.3389/fphys.2020.575407. PMID: 33013491; PMCID: PMC7509183.
McDuff D., Gontarek S. & Picard R. W., (2014), Remote detection of photoplethysmographic systolic and diastolic peaks using a digital camera, IEEE Transactions on Biomedical Engineering, 61(12), 2948-2954.
O’brien E., Waeber B., Parati G., Staessen J., Myers M.G. (2001), Blood pressure measuring devices: Recommendations of the European Society of Hypertension. BMJ.; 322:531–536.
Oğuz, F.E., Alkan, A. & Schöler, T. (2023), Emotion detection from ECG signals with different learning algorithms and automated feature engineering. SIViP, 17, 3783–3791. https://doi.org/10.1007/s11760-023-02606-y
Pour Ebrahim, M., Heydari, F., Wu, T. et al. (2019), Blood Pressure Estimation Using On-body Continuous Wave Radar and Photoplethysmogram in Various Posture and Exercise Conditions. Sci Rep 9, 16346.
Rodríguez, Juan C.R., et al. (2013), Innovative continuous non-invasive cuffless blood pressure monitoring based on photoplethysmography technology. Intensive Care Medicine 39.9: 1618-1625.
Saeed, M., Villarroel, M., Reisner, A.T., Clifford, G., Lehman, L., Moody, G.B., Heldt, T., Kyaw, T.H., Moody, B.E., Mark, R.G.. (2011 May), Multiparameter intelligent monitoring in intensive care II (MIMIC-II): A public-access ICU database. Critical Care Medicine, 39(5):952-960; DO: 10.1097/CCM.0b013e31820a92c6.
Salehizadeh, S. M., Dao, D. K., Chong, J. W., McManus, D., Darling, C., Mendelson, Y., & Chon, K. H.,(2014), Photoplethysmograph signal reconstruction based on a novel motion artifact detection-reduction approach. Part II: Motion and noise artifact removal, Annals of Biomedical Engineering, 42(11), 2251–2263.
Shin H. S., Lee C., & Lee M., (2009), Adaptive threshold method for the peak detection of photoplethysmographic waveform, Computers in Biology and Medicine, 39(12), 1145-1152.
Silva, I, Moody, G. (2014), An Open-source Toolbox for Analysing and Processing PhysioNet Databases in MATLAB and Octave. Journal of Open Research Software 2(1):e27 [http://dx.doi.org/10.5334/jors.bi].
Slapničar G, Mlakar N, Luštrek M. (2019), Blood Pressure Estimation from Photoplethysmogram Using a Spectro-Temporal Deep Neural Network. Sensors (Basel). Aug 4;19(15):3420. doi: 10.3390/s19153420.
Sun, X., Zhou, L., Chang, S., & Liu, Z. (2021), Using CNN and HHT to predict blood pressure level based on photoplethysmography and its derivatives, Biosensors, 11(4), 120.
Sunnetci, KM, Kaba, E, Beyazal Çeliker, F, Alkan, A. (2023), Comparative parotid gland segmentation by using ResNet-18 and MobileNetV2 based DeepLab v3+ architectures from magnetic resonance images. Concurrency Computat Pract Exper. ; 35(1):e7405. doi:10.1002/cpe.7405
Suzuki, T., Kameyama, K.,& Tamura, T., (2009), Development of the irregular pulse detection method in daily life using wearable photoplethysmographic sensor, Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Annual International Conference, 6080-6083.
Tanveer, M.S., Hasan, M.K. (2019), Cuffless blood pressure estimation from electrocardiogram and photoplethysmogram using waveform based ANN-LSTM network, Biomedical Signal Processing and Control, 51, 382-392.
Tazarv, A. and Levorato, M., (2021), A deep learning approach to predict blood pressure from ppg signals, in 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 5658–5662, Mexico.
Teng, X. F., and Y. T. Zhang. (2003), Continuous and noninvasive estimation of arterial blood pressure using a photoplethysmographic approach, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No. 03CH37439). Vol. 4. IEEE.
Tjahjadi, H., Ramli, K., & Murfi, H. (2020). Noninvasive classification of blood pressure based on photoplethysmography signals using bidirectional long short-term memory and time-frequency analysis. IEEE Access, 8, 20735-20748.
World Health Organization [2022-05-22]. Hypertension. https://www.who.int/news-room/fact-sheets/detail/hypertension
Xing, X., & Sun, M. (2016). Optical blood pressure estimation with photoplethysmography and FFT-based neural networks. Biomedical Optics Express, 7(8), 3007-3020.
Yousef Q., Reaz M. B. I., Ali, M. A. M., (2012), The analysis of PPG morphology: investigating the effects of aging on arterial compliance, Measurement Science Review, 12(6), 266-271.

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 1970 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

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

PHOTOPLETHYSMOGRAPHY BASED BLOOD PRESSURE ESTIMATION USING SYNCHROSQUEEZING TRANSFORM AND DEEP LEARNING

Abstract

Keywords

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

Öz

Anahtar Kelimeler

Destekleyen Kurum

Proje Numarası

Teşekkür

Kaynakça

Ayrıntılar

Birincil Dil

Konular

Bölüm

Yazarlar

Yayımlanma Tarihi

Gönderilme Tarihi

Kabul Tarihi

Yayımlandığı Sayı

DOI

IZ

Kaynak Göster