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PPG ve ECG Sinyallerinden Tansiyon Tahmini

Year 2023, Volume: 14 Issue: 4, 527 - 538, 31.12.2023
https://doi.org/10.24012/dumf.1307817

Abstract

Yüksek kan basıncı; özellikle kritik hastalarda izlenmediği ve kontrol edilmediği takdirde daha fazla sağlık komplikasyonlarına sebep olmaktadır. Son zamanlarda dünyada sürekli tüketilen hazır gıda benzeri besinlerden dolayı kardiyovaskürel hastalıklar arttırmaktadır. Bu hastalıklar dünyanın en yaygın ölüm sebepleri arasında yer almaktadır. Kalp ile ilgili hastalıkları tespit ve tedavi etmek için birçok parametreyle birlikte kan basıncıda sürekli takip edilmelidir. Kan basıncı ölçümü için geliştirilen birçok girişimsel ve girişimsel olmayan yöntem geliştirilmiştir. Hastanelerde kullanılan çoğu yöntem girişimsel yöntemlerdir. Bu yöntemler, sürekli kan basıncı tahmini için kullanılmamaktadır. Ayrıca bir psikolojik rahatsızlık olan ‘Beyaz Palto Sendromu’ diye adlandırılan bir rahatsızlık vardır. Bu rahatsızlık özellikle halk arasında da ‘Doktordan Korkmak’ olarak bilinir. Ölçüm esnasında hastanın kan basıncının normal değerler dışında yüksek çıkmasında sebep olan bu hastalıktan kaçınmanın bir diğer yöntemi ise temassız tansiyon ölçümüdür. Bu çalışmada Photoplethysmogram (PPG) ve Electrocardiogram (ECG) gibi temassız bir şekilde toplanabilen sinyallerden kan basıncı tahmini yapılmaktadır. Çalışmada birden fazla derin öğrenme modeli ve bu modeller farklı hiperparametreler ile karşılaştırılmıştır. Elde edilen sonuçlara göre LSTM model için %98.35, LSTM ve dense katmanlarından oluşan model için %97.42, sadece dense katmanlı mimariden oluşan birinci model için %98.59, sadece dense katmanınlı mimariden oluşan ikinci model için %71.9 doğruluk oranına ulaşılmıştır.

Supporting Institution

Fırat Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Birimi (FÜBAP)

Project Number

TEKF.23.29

Thanks

Çalışmada, Fırat Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Birimi (FÜBAP) tarafından TEKF.23.29 numaralı proje ile desteklenmiştir. Desteklerinden dolayı FÜBAP birimine teşekkür ederiz.

References

  • [1] J. Booth, “A short history of blood pressure measurement,” Proceedings of the Royal Society of Medicine, vol. 70, no. 11, pp. 793–799, 1977. doi:10.1177/003591577707001112.
  • [2] G. Beevers, “ABC of hypertension: Blood pressure measurement,” BMJ, vol. 322, no. 7292, pp. 981–985, 2001. doi:10.1136/bmj.322.7292.981.
  • [3] “High blood pressure ,” www.heart.org, https://www.heart.org/en/health-topics/high-blood-pressure (accessed Oct. 16, 2023).
  • [4] “High blood pressure in adults - hypertension: Medlineplus medical encyclopedia,” MedlinePlus, https://medlineplus.gov/ency/article/000468.htm (accessed Oct. 16, 2023).
  • [5] “Hypertension,” World Health Organization, https://www.who.int/health-topics/hypertension#tab=tab_1 (accessed Oct. 16, 2023).
  • [6] “Low blood pressure (hypotension),” Mayo Clinic, https://www.mayoclinic.org/diseases-conditions/low-blood-pressure/symptoms-causes/syc-20355465 (accessed Oct. 16, 2023).
  • [7] “Know your risk for high blood pressure,” Centers for Disease Control and Prevention, https://www.cdc.gov/bloodpressure/risk_factors.htm (accessed Oct. 16, 2023).
  • [8] “High blood pressure,” Centers for Disease Control and Prevention, https://www.cdc.gov/bloodpressure/index.htm (accessed Oct. 16, 2023).
  • [9] M. Holanger, S. E. Kjeldsen, K. Jamerson, and S. Julius, “Smoking and overweight associated with masked uncontrolled hypertension: A hypertension optimal treatment (HOT) sub-study,” Blood Pressure, vol. 30, no. 1, pp. 51–59, 2020. doi:10.1080/08037051.2020.1787815.
  • [10] E. OBrien, “From measurement to profiles, phenomena and indices: A workshop of the European Society of Hypertension,” Blood Pressure Monitoring, vol. 10, no. 6, pp. 291–295, 2005. doi:10.1097/00126097-200512000-00001.
  • [11] G. Parati et al., “European Society of Hypertension Practice Guidelines for Ambulatory Blood Pressure Monitoring,” Journal of Hypertension, vol. 32, no. 7, pp. 1359–1366, 2014. doi:10.1097/hjh.0000000000000221.
  • [12] S. M. Fati, A. Muneer, N. A. Akbar, and S. M. Taib, “A continuous cuffless blood pressure estimation using tree-based pipeline optimization tool,” Symmetry, vol. 13, no. 4, p. 686, 2021. doi:10.3390/sym13040686.
  • [13] S. Mahmud et al., “A shallow U-net architecture for reliably predicting blood pressure (BP) from Photoplethysmogram (PPG) and Electrocardiogram (ECG) signals,” Sensors, vol. 22, no. 3, p. 919, 2022. doi:10.3390/s22030919.
  • [14] A. Farki, R. Baradaran Kazemzadeh, and E. Akhondzadeh Noughabi, “A novel clustering-based algorithm for continuous and noninvasive cuff-less blood pressure estimation,” Journal of Healthcare Engineering, vol. 2022, pp. 1–13, 2022. doi:10.1155/2022/3549238.
  • [15] L. N. Harfiya, C.-C. Chang, and Y.-H. Li, “Continuous blood pressure estimation using exclusively photopletysmography by LSTM-based signal-to-signal translation,” Sensors, vol. 21, no. 9, p. 2952, 2021. doi:10.3390/s21092952.
  • [16] A. L. Goldberger et al., “Physiobank, PhysioToolkit, and PhysioNet,” Circulation, vol. 101, no. 23, 2000. doi:10.1161/01.cir.101.23.e215.
  • [17] A. Paviglianiti, V. Randazzo, E. Pasero, and A. Vallan, “Noninvasive arterial blood pressure estimation using abpnet and vital-ECG,” 2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 2020. doi:10.1109/i2mtc43012.2020.9129361.
  • [18] J. Kraitl, U. Timm, H. Ewald, and E. Lewis, “Non-invasive measurement of blood components,” 2011 Fifth International Conference on Sensing Technology, 2011. doi:10.1109/icsenst.2011.6136976.
  • [19] “A wireless heart rate monitoring system based on photoplethysmography (PPG) technique,” Strad Research, vol. 7, no. 9, 2020. doi:10.37896/sr7.9/011.
  • [20] Joon Lee et al., “Open-access mimic-II database for Intensive Care Research,” 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011. doi:10.1109/iembs.2011.6092050.
  • [21] Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.
  • [22] I. Goodfellow et al., “Generative Adversarial Networks,” Communications of the ACM, vol. 63, no. 11, pp. 139–144, 2020. doi:10.1145/3422622.
Year 2023, Volume: 14 Issue: 4, 527 - 538, 31.12.2023
https://doi.org/10.24012/dumf.1307817

Abstract

Project Number

TEKF.23.29

References

  • [1] J. Booth, “A short history of blood pressure measurement,” Proceedings of the Royal Society of Medicine, vol. 70, no. 11, pp. 793–799, 1977. doi:10.1177/003591577707001112.
  • [2] G. Beevers, “ABC of hypertension: Blood pressure measurement,” BMJ, vol. 322, no. 7292, pp. 981–985, 2001. doi:10.1136/bmj.322.7292.981.
  • [3] “High blood pressure ,” www.heart.org, https://www.heart.org/en/health-topics/high-blood-pressure (accessed Oct. 16, 2023).
  • [4] “High blood pressure in adults - hypertension: Medlineplus medical encyclopedia,” MedlinePlus, https://medlineplus.gov/ency/article/000468.htm (accessed Oct. 16, 2023).
  • [5] “Hypertension,” World Health Organization, https://www.who.int/health-topics/hypertension#tab=tab_1 (accessed Oct. 16, 2023).
  • [6] “Low blood pressure (hypotension),” Mayo Clinic, https://www.mayoclinic.org/diseases-conditions/low-blood-pressure/symptoms-causes/syc-20355465 (accessed Oct. 16, 2023).
  • [7] “Know your risk for high blood pressure,” Centers for Disease Control and Prevention, https://www.cdc.gov/bloodpressure/risk_factors.htm (accessed Oct. 16, 2023).
  • [8] “High blood pressure,” Centers for Disease Control and Prevention, https://www.cdc.gov/bloodpressure/index.htm (accessed Oct. 16, 2023).
  • [9] M. Holanger, S. E. Kjeldsen, K. Jamerson, and S. Julius, “Smoking and overweight associated with masked uncontrolled hypertension: A hypertension optimal treatment (HOT) sub-study,” Blood Pressure, vol. 30, no. 1, pp. 51–59, 2020. doi:10.1080/08037051.2020.1787815.
  • [10] E. OBrien, “From measurement to profiles, phenomena and indices: A workshop of the European Society of Hypertension,” Blood Pressure Monitoring, vol. 10, no. 6, pp. 291–295, 2005. doi:10.1097/00126097-200512000-00001.
  • [11] G. Parati et al., “European Society of Hypertension Practice Guidelines for Ambulatory Blood Pressure Monitoring,” Journal of Hypertension, vol. 32, no. 7, pp. 1359–1366, 2014. doi:10.1097/hjh.0000000000000221.
  • [12] S. M. Fati, A. Muneer, N. A. Akbar, and S. M. Taib, “A continuous cuffless blood pressure estimation using tree-based pipeline optimization tool,” Symmetry, vol. 13, no. 4, p. 686, 2021. doi:10.3390/sym13040686.
  • [13] S. Mahmud et al., “A shallow U-net architecture for reliably predicting blood pressure (BP) from Photoplethysmogram (PPG) and Electrocardiogram (ECG) signals,” Sensors, vol. 22, no. 3, p. 919, 2022. doi:10.3390/s22030919.
  • [14] A. Farki, R. Baradaran Kazemzadeh, and E. Akhondzadeh Noughabi, “A novel clustering-based algorithm for continuous and noninvasive cuff-less blood pressure estimation,” Journal of Healthcare Engineering, vol. 2022, pp. 1–13, 2022. doi:10.1155/2022/3549238.
  • [15] L. N. Harfiya, C.-C. Chang, and Y.-H. Li, “Continuous blood pressure estimation using exclusively photopletysmography by LSTM-based signal-to-signal translation,” Sensors, vol. 21, no. 9, p. 2952, 2021. doi:10.3390/s21092952.
  • [16] A. L. Goldberger et al., “Physiobank, PhysioToolkit, and PhysioNet,” Circulation, vol. 101, no. 23, 2000. doi:10.1161/01.cir.101.23.e215.
  • [17] A. Paviglianiti, V. Randazzo, E. Pasero, and A. Vallan, “Noninvasive arterial blood pressure estimation using abpnet and vital-ECG,” 2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 2020. doi:10.1109/i2mtc43012.2020.9129361.
  • [18] J. Kraitl, U. Timm, H. Ewald, and E. Lewis, “Non-invasive measurement of blood components,” 2011 Fifth International Conference on Sensing Technology, 2011. doi:10.1109/icsenst.2011.6136976.
  • [19] “A wireless heart rate monitoring system based on photoplethysmography (PPG) technique,” Strad Research, vol. 7, no. 9, 2020. doi:10.37896/sr7.9/011.
  • [20] Joon Lee et al., “Open-access mimic-II database for Intensive Care Research,” 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011. doi:10.1109/iembs.2011.6092050.
  • [21] Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.
  • [22] I. Goodfellow et al., “Generative Adversarial Networks,” Communications of the ACM, vol. 63, no. 11, pp. 139–144, 2020. doi:10.1145/3422622.
There are 22 citations in total.

Details

Primary Language Turkish
Journal Section Articles
Authors

Hüseyin Alperen Dağdögen 0000-0003-2862-8257

İbrahim Türkoğlu 0000-0003-4938-4167

Project Number TEKF.23.29
Early Pub Date December 31, 2023
Publication Date December 31, 2023
Submission Date May 31, 2023
Published in Issue Year 2023 Volume: 14 Issue: 4

Cite

IEEE H. A. Dağdögen and İ. Türkoğlu, “PPG ve ECG Sinyallerinden Tansiyon Tahmini”, DUJE, vol. 14, no. 4, pp. 527–538, 2023, doi: 10.24012/dumf.1307817.
DUJE tarafından yayınlanan tüm makaleler, Creative Commons Atıf 4.0 Uluslararası Lisansı ile lisanslanmıştır. Bu, orijinal eser ve kaynağın uygun şekilde belirtilmesi koşuluyla, herkesin eseri kopyalamasına, yeniden dağıtmasına, yeniden düzenlemesine, iletmesine ve uyarlamasına izin verir. 24456