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DNN-BP: a novel framework for cuffless blood pressure measurement from optimal PPG features using deep learning model

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Abstract

Continuous blood pressure (BP) provides essential information for monitoring one’s health condition. However, BP is currently monitored using uncomfortable cuff-based devices, which does not support continuous BP monitoring. This paper aims to introduce a blood pressure monitoring algorithm based on only photoplethysmography (PPG) signals using the deep neural network (DNN). The PPG signals are obtained from 125 unique subjects with 218 records and filtered using signal processing algorithms to reduce the effects of noise, such as baseline wandering, and motion artifacts. The proposed algorithm is based on pulse wave analysis of PPG signals, extracted various domain features from PPG signals, and mapped them to BP values. Four feature selection methods are applied and yielded four feature subsets. Therefore, an ensemble feature selection technique is proposed to obtain the optimal feature set based on major voting scores from four feature subsets. DNN models, along with the ensemble feature selection technique, outperformed in estimating the systolic blood pressure (SBP) and diastolic blood pressure (DBP) compared to previously reported approaches that rely only on the PPG signal. The coefficient of determination (\(R^2\)) and mean absolute error (MAE) of the proposed algorithm are 0.962 and 2.480 mmHg, respectively, for SBP and 0.955 and 1.499 mmHg, respectively, for DBP. The proposed approach meets the Advancement of Medical Instrumentation standard for SBP and DBP estimations. Additionally, according to the British Hypertension Society standard, the results attained Grade A for both SBP and DBP estimations. It concludes that BP can be estimated more accurately using the optimal feature set and DNN models. The proposed algorithm has the potential ability to facilitate mobile healthcare devices to monitor continuous BP.

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References

  1. Wu C-Y et al (2015) High blood pressure and all-cause and cardiovascular disease mortalities in community-dwelling older adults. Medicine 94(47)

  2. Ma HT (2014) A blood pressure monitoring method for stroke management. BioMed Research International 2014

  3. Ding X-R et al (2016) Continuous blood pressure measurement from invasive to unobtrusive: celebration of 200th birth anniversary of Carl Ludwig. IEEE J Biomed Health Inform 20(6):1455–1465

  4. Tao K-m, Sokha S, Yuan H-b (2019) Sphygmomanometer for invasive blood pressure monitoring in a medical mission. Anesthesiology 130(2):312–312

    Article  PubMed  Google Scholar 

  5. Chandrasekhar A et al (2018) Smartphone-based blood pressure monitoring via the oscillometric finger-pressing method. Sci Transl Med 10(431):eaap8674

  6. van Geene W (1993) Clinical evaluation of a blood pressure controller in anaesthesia

  7. Chakraborty A, Goswami D, Mukhopadhyay J, Chakrabarti S (2020) Measurement of arterial blood pressure through single-site acquisition of photoplethysmograph signal. IEEE Trans Instrum Meas 70:1–10

    Article  Google Scholar 

  8. Kachuee M, Kiani MM, Mohammadzade H, Shabany M (2016) Cuffless blood pressure estimation algorithms for continuous health-care monitoring. IEEE Trans Biomed Eng 64(4):859–869

  9. Haque MR, Raju SMTU, Golap MA-U, Hashem MMA (2021) A novel technique for non-invasive measurement of human blood component levels from fingertip video using DNN based models. IEEE Access 9:19025–19042

    Article  Google Scholar 

  10. Sun Y, Thakor N (2015) Photoplethysmography revisited: from contact to noncontact, from point to imaging. IEEE Trans Biomed Eng 63(3):463–477

    Article  PubMed  PubMed Central  Google Scholar 

  11. Castaneda D, Esparza A, Ghamari M, Soltanpur C, Nazeran H (2018) A review on wearable photoplethysmography sensors and their potential future applications in health care. Int J Biosens Bioelectron 4(4):195

    PubMed  PubMed Central  Google Scholar 

  12. Jarchi D, Casson AJ (2017) Towards photoplethysmography-based estimation of instantaneous heart rate during physical activity. IEEE Trans Biomed Eng 64(9):2042–2053

    Article  PubMed  Google Scholar 

  13. Sinchai S, Kainan P, Wardkein P, Koseeyaporn J (2018) A photoplethysmographic signal isolated from an additive motion artifact by frequency translation. IEEE Trans Biomed Circ Syst 12(4):904–917

    Article  Google Scholar 

  14. Hasanzadeh N, Ahmadi MM, Mohammadzade H (2019) Blood pressure estimation using photoplethysmogram signal and its morphological features. IEEE Sensors J 20(8):4300–4310

    Article  Google Scholar 

  15. Chowdhury MH et al (2020) Estimating blood pressure from the photoplethysmogram signal and demographic features using machine learning techniques. Sensors 20(11):3127

    Article  PubMed  PubMed Central  Google Scholar 

  16. Zhang Y, Feng Z (2017) A SVM method for continuous blood pressure estimation from a PPG signal. In: Proceedings of the 9th international conference on machine learning and computing, pp 128–132

  17. Panwar M, Gautam A, Biswas D, Acharyya A (2020) PP-Net: a deep learning framework for PPG-based blood pressure and heart rate estimation. IEEE Sensors J 20(17):10000–10011

    Article  Google Scholar 

  18. Samimi H, Dajani HR (2022) Cuffless blood pressure estimation using calibrated cardiovascular dynamics in the photoplethysmogram. Bioengineering 9(9):446

    Article  PubMed  PubMed Central  Google Scholar 

  19. Mukkamala R et al (2015) Toward ubiquitous blood pressure monitoring via pulse transit time: theory and practice. IEEE Trans Biomed Eng 62(8):1879–1901

    Article  PubMed  PubMed Central  Google Scholar 

  20. Teng X, Zhang Y (2003) Continuous and noninvasive estimation of arterial blood pressure using a photoplethysmographic approach. In: Proceedings of the 25th annual international conference of the ieee engineering in medicine and biology society (IEEE Cat. No. 03CH37439), vol 4, pp 3153–3156. IEEE

  21. Li P, Laleg-Kirati T-M (2021) Central blood pressure estimation from distal PPG measurement using semiclassical signal analysis features. IEEE Access 9:44963–44973

    Article  Google Scholar 

  22. Maqsood S, Xu S, Springer M, Mohawesh R (2021) A benchmark study of machine learning for analysis of signal feature extraction techniques for blood pressure estimation using photoplethysmography (PPG). IEEE Access 9:138817–138833

    Article  Google Scholar 

  23. El-Hajj C, Kyriacou PA (2021) Cuffless blood pressure estimation from PPG signals and its derivatives using deep learning models. Biomed Signal Process Control 70:102984

    Article  Google Scholar 

  24. Gaurav A, Maheedhar M, Tiwari VN, Narayanan R (2016) Cuff-less PPG based continuous blood pressure monitoring—a smartphone based approach. In: 2016 38th annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp 607–610. IEEE

  25. Mousavi SS, Firouzmand M, Charmi M, Hemmati M, Moghadam M, Ghorbani Y (2019) Blood pressure estimation from appropriate and inappropriate PPG signals using a whole-based method. Biomed Signal Process Control 47:196–206

    Article  Google Scholar 

  26. Peter L, Noury N, Cerny M (2014) A review of methods for non-invasive and continuous blood pressure monitoring: pulse transit time method is promising? Irbm 35(5):271–282

    Article  Google Scholar 

  27. Mukkamala R, Hahn J-O (2017) Toward ubiquitous blood pressure monitoring via pulse transit time: predictions on maximum calibration period and acceptable error limits. IEEE Trans Biomed Eng 65(6):1410–1420

    Article  PubMed  PubMed Central  Google Scholar 

  28. Möbius P, Remoissenet, M (1997) Waves called solitons. concepts and experiments. berlin etc., springer-verlag 1996. xx, 260 pp., dm 78, 00. isbn 3-540-60502-9. Zeitschrift Angewandte Mathematik und Mechanik 77(7):560–560

  29. Ding X-R, Zhang Y-T, Liu J, Dai W-X, Tsang HK (2015) Continuous cuffless blood pressure estimation using pulse transit time and photoplethysmogram intensity ratio. IEEE Trans Biomed Eng 63(5):964–972

    Article  PubMed  Google Scholar 

  30. Zheng Y-L, Yan BP, Zhang Y-T, Poon CC (2014) An armband wearable device for overnight and cuff-less blood pressure measurement. IEEE Trans Biomed Eng 61(7):2179–2186

    Article  PubMed  Google Scholar 

  31. Westerhof N, Lankhaar J-W, Westerhof BE (2009) The arterial windkessel. Med Biol Eng Comput 47(2):131–141

    Article  PubMed  Google Scholar 

  32. Miao F et al (2017) A novel continuous blood pressure estimation approach based on data mining techniques. IEEE J Biomed Health Inform 21(6):1730–1740

    Article  PubMed  Google Scholar 

  33. Liang Y, Chen Z, Elgendi M (2021) PPG-BP database. 2018. https://figshare.com/articles/dataset/PPG-BP_Database_zip/5459299. Accessed 06 April 2021

  34. Liang Y, Elgendi M, Chen Z, Ward R (2018) An optimal filter for short photoplethysmogram signals. Sci data 5(1):1–12

    Article  Google Scholar 

  35. Elgendi M (2012) On the analysis of fingertip photoplethysmogram signals. Curr Cardiol Rev 8(1):14–25

    Article  PubMed  PubMed Central  Google Scholar 

  36. Chatterjee A, Roy UK (2018) PPG based heart rate algorithm improvement with Butterworth IIR Filter and Savitzky-Golay FIR Filter. In: 2018 2nd International conference on electronics, materials engineering & nano-technology (IEMENTech), pp 1–6. IEEE

  37. Holschneider M, Kronland-Martinet R, Morlet J, Tchamitchian P (1990) A real-time algorithm for signal analysis with the help of the wavelet transform. In: Wavelets, pp 286–297. Springer

  38. Xing X, Sun M (2016) Optical blood pressure estimation with photoplethysmography and FFT-based neural networks. Biomed Opt Express 7(8):3007–3020

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Zahoor U (2011) Baseline wandering removal from human electrocardiogram signal using projection pursuit gradient ascent algorithm. Int J Electr Comput Sci IlECS/lJENS 9(9):11–13

    Google Scholar 

  40. Kavsaoğlu AR, Polat K, Hariharan M (2015) Non-invasive prediction of hemoglobin level using machine learning techniques with the PPG signal’s characteristics features. Appl Soft Comput 37:983–991

    Article  Google Scholar 

  41. Kira K et al (1992) The feature selection problem: traditional methods and a new algorithm. Aaai 2:129–134

    Google Scholar 

  42. Urbanowicz RJ, Meeker M, La Cava W, Olson RS, Moore JH (2018) Relief-based feature selection: introduction and review. J Biomed Inform 85:189–203

    Article  PubMed  PubMed Central  Google Scholar 

  43. Acid S, De Campos LM, Fernández M (2011) Minimum redundancy maximum relevancy versus score-based methods for learning Markov boundaries. In: 2011 11th International conference on intelligent systems design and applications, pp 619–623. IEEE

  44. Singh BK, Verma K, Thoke A, Suri JS (2017) Risk stratification of 2d ultrasound-based breast lesions using hybrid feature selection in machine learning paradigm. Measurement 105:146–157

    Article  Google Scholar 

  45. Aggarwal CC (2018) Training deep neural networks. In: Neural networks and deep learning, pp 105–167. Springer

  46. Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958

    Google Scholar 

  47. for the Advancement of Medical Instrumentation A et al (1987) American national standards for electronic or automated sphygmomanometers. ANSI/AAMI SP 10-1987

  48. O’brien E, Waeber B, Parati G, Staessen J, Myers MG (2001) Blood pressure measuring devices: recommendations of the European Society of Hypertension. Bmj 322(7285):531–536

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This work is supported in part by Khulna University of Engineering & Technology.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna, 9203, Bangladesh

    S. M. Taslim Uddin Raju, Safin Ahmed Dipto, Md Imran Hossain, Fabliha Haque, Md Mahamudul Hasan Khan & M. M. A. Hashem

  2. Department of Biomedical Engineering, Khulna University of Engineering & Technology, Khulna, 9203, Bangladesh

    Md. Abu Shahid Chowdhury & Ayesha Tun Nashrah

  3. Department of Business Administration, International American University, Los Angeles, CA, 90010, USA

    Araf Nishan

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  1. S. M. Taslim Uddin Raju
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Contributions

S. M. Taslim Uddin Raju: conceptualization, methodology, writing—original draft. Safin Ahmed Dipto: conceptualization, methodology, writing—review and editing. Md Imran Hossain: visualization, investigation. Md. Abu Shahid Chowdhury: formal analysis, visualization, writing—original draft. Fabliha Haque: writing—review and editing, project administration. Ayesha Tun Nashrah: investigation, validation. Araf Nishan: writing—review and editing, visualization. Mahamudul Hasan Khan: writing—review and editing, revision. M. M. A. Hashem: supervision.

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Correspondence to S. M. Taslim Uddin Raju.

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Raju, S.M.T.U., Dipto, S.A., Hossain, M.I. et al. DNN-BP: a novel framework for cuffless blood pressure measurement from optimal PPG features using deep learning model. Med Biol Eng Comput 62, 3687–3708 (2024). https://doi.org/10.1007/s11517-024-03157-1

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