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.
References
- Acharya, U. R., Hagiwara, Y., Koh, J. E.W., Oh, S. L., Tan, J. H., Adam, M., and San Tan, R., (2018), Entropies for automated detection of coronary artery disease using ecg signals: A review, Biocybernetics and Biomedical Engineering, vol. 38, no. 2, pp. 373–384.
- Al Ghayab, H. R., Li, Y., Siuly, S., and Abdullah, S., (2019) A feature extraction technique based on tunable q-factor wavelet transform for brain signal classification, Journal of neuroscience methods, vol. 312, pp. 43–52,.
- Allen, J., & Murray, A.,(2003), Age-related changes in peripheral pulse shape characteristics at various body sites, Physiological Meaurement ,24(2), 297–307.
- Allen, J.,(2007), Plethysmography and its application in clinical physiological Measurement, Physiological Meaurement, 28(3), 1-39.
- American National Standards Institute (2023). Non-invasive sphygmomanometers - Part 2: clinical investigation of automated measurement type. ANSI/AAMI/ISO 81060–2:2013. http://webstore.ansi.org, Accessed September 26,.
- Bagha, S., & Shaw, L. ,(2011), A real time analysis of PPG signal for measurement of SpO2 and Pulse Rate, International Journal of Computer Applications, 36(11),45-50.
- Büyüköztürk, K., (1999), Turkish cardiology association national hypertension treatment and follow-up guide, https://tkd.org.tr/kilavuz/k03.htm, [Visited: 13/03/2021].
- Engel, T.A., Charao, A.S., Pinheiro, M.K., & Steffenel, L.A.,(2014), Performance Improvement Of Data Mining in Weka Through GPU Acceleration, Procedia Computer Science, 32, 93 – 100.
- Gao, S.C., P. Wittek, L. Zhao, W.J. Jiang, (2016) Data-driven estimation of blood pressure using photoplethysmographic signals, August 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, pp. 766-769.
- Han, J., Pei, J., and Kamber, M., (2011) Data Mining: Concepts and Techniques, Elsevier, Amsterdam, Netherlands.
- Hertzman A.B., (1938), The blood supply of various skin areas as estimated by the photoelectric plethysmograph, American Journal of Physiology,24(2), 328– 340.
- Johnston,W.,(2006), Development of a Signal Processing Library for Extraction of SpO2, HR, HRV, and RR from Photoplethysmographic Waveforms, Thesis (Msc) Worcester Polytechnic Institute.
- Kraitl, J., Hartmut E.,(2005), Optical non-invasive methods for characterization of the human health status, 1st International Conference on Sensing Technology, 21-23 November 2005 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.
- Liang, Y., Chen, Z., Liu, G., & Elgendi, M., (2018), A new short-recorded photoplethysmogram dataset for blood pressure monitoring in China, Scientific Data, 5.
- 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.
- Nafisi, V. B. & Shahabi, M, (2018), Intradialytic hypotension related episodes identification based on the most effective features of photoplethysmography signal. Comput. Methods Programs Biomed. 157 (4), 1–9.
- Rastegar, S., Gholamhosseini, H., Lowe, A., Mehdipour, F. and Lindén, M., (2019). Estimating Systolic Blood Pressure Using Convolutional Neural Networks, Studies in health technology and informatics, 261, 143-149, , PMID: 31156106.
- Schlesinger, O., Vigderhouse, N., Eytan, D. and Moshe, Y., (2020). Blood Pressure Estimation from PPG Signals Using Convolutional Neural Networks and Siamese Network, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 1135-1139, DOI: 10.1109/ICASSP40776.2020.9053446.
- Selesnick, I. W., (2011), Wavelet transform with tunable Q-factor, IEEE Transactions on Signal Processing, 59(8), 3560–3575.
- 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.
- Teng, X. F. and Zhang, Y. T., (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.
- Übeyli, E. D., Cvetkovic, D., & Cosic, I., (2010), Analysis of Human PPG, ECG and EEG Signals by Eigenvector Methods, Digit. Signal Process, 20 (3), 956–963.
- Xing, X., Sun, M., (2016). Optical blood pressure estimation with photoplethysmography and FFT-based neural networks, Biomed. Opt. Express, 7 (8), pp. 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.
Abstract
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.
References
- Acharya, U. R., Hagiwara, Y., Koh, J. E.W., Oh, S. L., Tan, J. H., Adam, M., and San Tan, R., (2018), Entropies for automated detection of coronary artery disease using ecg signals: A review, Biocybernetics and Biomedical Engineering, vol. 38, no. 2, pp. 373–384.
- Al Ghayab, H. R., Li, Y., Siuly, S., and Abdullah, S., (2019) A feature extraction technique based on tunable q-factor wavelet transform for brain signal classification, Journal of neuroscience methods, vol. 312, pp. 43–52,.
- Allen, J., & Murray, A.,(2003), Age-related changes in peripheral pulse shape characteristics at various body sites, Physiological Meaurement ,24(2), 297–307.
- Allen, J.,(2007), Plethysmography and its application in clinical physiological Measurement, Physiological Meaurement, 28(3), 1-39.
- American National Standards Institute (2023). Non-invasive sphygmomanometers - Part 2: clinical investigation of automated measurement type. ANSI/AAMI/ISO 81060–2:2013. http://webstore.ansi.org, Accessed September 26,.
- Bagha, S., & Shaw, L. ,(2011), A real time analysis of PPG signal for measurement of SpO2 and Pulse Rate, International Journal of Computer Applications, 36(11),45-50.
- Büyüköztürk, K., (1999), Turkish cardiology association national hypertension treatment and follow-up guide, https://tkd.org.tr/kilavuz/k03.htm, [Visited: 13/03/2021].
- Engel, T.A., Charao, A.S., Pinheiro, M.K., & Steffenel, L.A.,(2014), Performance Improvement Of Data Mining in Weka Through GPU Acceleration, Procedia Computer Science, 32, 93 – 100.
- Gao, S.C., P. Wittek, L. Zhao, W.J. Jiang, (2016) Data-driven estimation of blood pressure using photoplethysmographic signals, August 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, pp. 766-769.
- Han, J., Pei, J., and Kamber, M., (2011) Data Mining: Concepts and Techniques, Elsevier, Amsterdam, Netherlands.
- Hertzman A.B., (1938), The blood supply of various skin areas as estimated by the photoelectric plethysmograph, American Journal of Physiology,24(2), 328– 340.
- Johnston,W.,(2006), Development of a Signal Processing Library for Extraction of SpO2, HR, HRV, and RR from Photoplethysmographic Waveforms, Thesis (Msc) Worcester Polytechnic Institute.
- Kraitl, J., Hartmut E.,(2005), Optical non-invasive methods for characterization of the human health status, 1st International Conference on Sensing Technology, 21-23 November 2005 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.
- Liang, Y., Chen, Z., Liu, G., & Elgendi, M., (2018), A new short-recorded photoplethysmogram dataset for blood pressure monitoring in China, Scientific Data, 5.
- 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.
- Nafisi, V. B. & Shahabi, M, (2018), Intradialytic hypotension related episodes identification based on the most effective features of photoplethysmography signal. Comput. Methods Programs Biomed. 157 (4), 1–9.
- Rastegar, S., Gholamhosseini, H., Lowe, A., Mehdipour, F. and Lindén, M., (2019). Estimating Systolic Blood Pressure Using Convolutional Neural Networks, Studies in health technology and informatics, 261, 143-149, , PMID: 31156106.
- Schlesinger, O., Vigderhouse, N., Eytan, D. and Moshe, Y., (2020). Blood Pressure Estimation from PPG Signals Using Convolutional Neural Networks and Siamese Network, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 1135-1139, DOI: 10.1109/ICASSP40776.2020.9053446.
- Selesnick, I. W., (2011), Wavelet transform with tunable Q-factor, IEEE Transactions on Signal Processing, 59(8), 3560–3575.
- 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.
- Teng, X. F. and Zhang, Y. T., (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.
- Übeyli, E. D., Cvetkovic, D., & Cosic, I., (2010), Analysis of Human PPG, ECG and EEG Signals by Eigenvector Methods, Digit. Signal Process, 20 (3), 956–963.
- Xing, X., Sun, M., (2016). Optical blood pressure estimation with photoplethysmography and FFT-based neural networks, Biomed. Opt. Express, 7 (8), pp. 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.
Details
| Primary Language | English |
|---|---|
| Subjects | Image Processing, Machine Learning (Other), Electrical Engineering (Other) |
| Journal Section | Electrical and Electronics Engineering |
| Authors | |
| Publication Date | December 3, 2023 |
| Submission Date | September 6, 2023 |
| Published in Issue | Year 2023Volume: 26 Issue: 4 |
