Design of a Highly Accurate PPG Sensing Interface via Multimodal Ensemble Classification Architecture

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Neha Singh 1,* Arun Kumar 1

1. ETC/ Bhilai Institute of Technology, Durg, India

* Corresponding author.


Received: 5 Jul. 2021 / Revised: 9 Aug. 2021 / Accepted: 21 Sep. 2021 / Published: 8 Feb. 2022

Index Terms

Blood pressure, PPG, sensing, ensemble, classifier


Photoplethysmogram (PPG) sensing is a field of signal measurement that involves accurate sensor design and efficient signal processing. Sensing interfaces have matured due to use of sophisticated nano-meter technologies, that allow for high speed, and low error sampling. Thus, in order to improve the efficiency of PPG sensing, the signal processing unit must be tweaked. A wide variety of algorithms have been proposed by researchers that use different classification models for signal conditioning and error reduction. When applied to blood pressure (BP) monitoring, the efficiency of these models is limited by their ability to differentiate between BP levels. In order to improve this efficiency, the underlying text proposes a novel multimodal ensemble classifier. The proposed classifier accumulates correct classification instances from a series of highly efficient classifiers in order to enhance the efficiency of PPG sensing. This efficiency is compared with standard classification models like k-nearest neighbors (kNN), random forest (RF), linear support vector machine (LSVM), multilayer perceptron (MLP), and logistic regression (LR). It is observed that the proposed model is 10% efficient than these models in terms of classification accuracy; and thus, can be used for real time BP monitoring PPG signal acquisition scenarios. This accuracy is estimated by comparing actual BP values with measured BP values, and then evaluating error difference w.r.t. other algorithms.

Cite This Paper

Neha Singh, Arun Kumar, "Design of a Highly Accurate PPG Sensing Interface via Multimodal Ensemble Classification Architecture", International Journal of Computer Network and Information Security(IJCNIS), Vol.14, No.1, pp.13-24, 2022. DOI: 10.5815/ijcnis.2022.01.02


[1] Elgendi, M., Fletcher, R., Liang, Y., Howard, N., Lovell, N. H., Abbott, D. & Ward, R. (2019). The use of photoplethysmography for assessing hypertension. NPJ digital medicine, 2(1), 1-11.

[2] Pereira, T., Tran, N., Gadhoumi, K., Pelter, M. M., Do, D. H., Lee, R. J., ... & Hu, X. (2020). Photoplethysmography based atrial fibrillation detection: A review. NPJ digital medicine, 3(1), 1-12.

[3] Tseng, C. H., Tseng, T. J., & Wu, C. Z. (2020). Cuffless Blood Pressure Measurement Using a Microwave Near-Field Self-Injection- Locked Wrist Pulse Sensor. IEEE Transactions on Microwave Theory and Techniques.

[4] Chandrasekhar, A., Yavarimanesh, M., Natarajan, K., Hahn, J. O., & Mukkamala, R. (2020). PPG sensor contact pressure should be taken into account for cuff-less blood pressure measurement. IEEE Transactions on Biomedical Engineering.

[5] Zhong, D., Yian, Z., Lanqing, W., Junhua, D., & Jiaxuan, H. (2020). Continuous blood pressure measurement platform: A wearable system based on multidimensional perception data. IEEE Access, 8, 10147- 10158

[6] Tabei, F., Gresham, J. M., Askarian, B., Jung, K., & Chong, J. W. (2020). Cuff-Less Blood Pressure Monitoring System Using Smartphones. IEEE Access, 8, 11534-11545.

[7] Shao, J., Shi, P., Hu, S., Liu, Y., & Yu, H. (2020). An optimization study of estimating blood pressure models based on pulse arrival time for continuous monitoring. Journal of Healthcare Engineering, 2020.

[8] Singla, M., Azeemuddin, S., & Sistla, P. (2020). Accurate Fiducial Point Detection Using Haar Wavelet for Beat-by-Beat Blood Pressure Estimation. IEEE Journal of Translational Engineering in Health and Medicine, 8, 1-11.

[9] Mohebbian, M. R., Dinh, A., Wahid, K., & Alam, M. S. (2020). Blind, Cuff-less, Calibration-Free and Continuous Blood Pressure Estimation using Optimized Inductive Group Method of Data Handling. Biomedical Signal Processing and Control, 57, 101682.

[10] Ibrahim, B., & Jafari, R. (2019). Cuffless blood pressure monitoring from an array of wrist bio-impedance sensors using subject-specific regression models: Proof of concept. IEEE transactions on biomedical circuits and systems, 13(6), 1723-1735.

[11] Honcharuk, A., & Adamenko, Y. (2019, October). Portable Device for Monitoring Blood Pressure. In 2019 IEEE International Scientific- Practical Conference Problems of Infocommunications, Science and Technology (PIC S&T) (pp. 1-4). IEEE.

[12] Huynh, T. H., Jafari, R., & Chung, W. Y. (2018). Noninvasive cuffless blood pressure estimation using pulse transit time and impedance plethysmography. IEEE Transactions on Biomedical Engineering, 66(4), 967-976.

[13] Chong, H., Lou, J., Bogie, K. M., Zorman, C. A., & Majerus, S. J. (2019). Vascular Pressure–Flow Measurement Using CB-PDMS Flexible Strain Sensor. IEEE transactions on biomedical circuits and systems, 13(6), 1451-1461.

[14] Rachim, V. P., & Chung, W. Y. (2019, July). Compressive Sensing of Cuff-less Biosensor for Energy-Efficient Blood Pressure Monitoring. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 7072- 7075). IEEE.

[15] Rastegar, S., GholamHosseini, H., & Lowe, A. (2020). Non-invasive continuous blood pressure monitoring systems: current and proposed technology issues and challenges. Physical and Engineering Sciences in Medicine, 43(1), 11-28.

[16] Barszczyk, A., & Lee, K. (2019). Measuring Blood Pressure: from Cuff to Smartphone. Current hypertension reports, 21(11), 84.

[17] Rao, K. S., Samyuktha, W., Vardhan, D. V., Naidu, B. G., Kumar, P. A., Sravani, K. G., & Guha, K. (2020). Design and sensitivity analysis of capacitive MEMS pressure sensor for blood pressure measurement. Microsystem Technologies, 1-9.

[18] Kao, Y. H., Chao, P. C. P., & Wey, C. L. (2018). Towards maximizing the sensing accuracy of an cuffless, optical blood pressure sensor using a high-order front-end filter. Microsystem Technologies, 24(11), 4621-4630.

[19] Oiwa, K., Bando, S., & Nozawa, A. (2018). Contactless blood pressure sensing using facial visible and thermal images. Artificial Life and Robotics, 23(3), 387-394

[20] Stojanova, A., Koceski, S., & Koceska, N. (2019). Continuous blood pressure monitoring as a basis for ambient assisted living (AAL)– review of methodologies and devices. Journal of medical systems, 43(2), 24.

[21] Eun, S. J., & Kim, J. (2020). Development of intelligent Eun, S. J., & Kim, J. (2020). Development of intelligent healthcare system based on ambulatory blood pressure measuring device. Neural Computing and Applications, 1-12.

[22] Brady, T. M., Padwal, R., Blakeman, D. E., Farrell, M., Frieden, T. R., Kaur, P., ... & Jaffe, M. G. (2020). Blood pressure measurement device selection in low‐resource settings: Challenges, compromises, and routes to progress. The Journal of Clinical Hypertension, 22(5), 792-801

[23] Heydari, F., Ebrahim, M. P., Redoute, J. M., Joe, K., Walker, K., Avolio, A., & Yuce, M. R. (2020). Clinical study of a chest‐based cuffless blood pressure monitoring system. Medical Devices & Sensors, e10091

[24] Lin, Q., Huang, J., Yang, J., Huang, Y., Zhang, Y., Wang, Y., ... & Hou, X. (2020). Highly Sensitive Flexible Iontronic Pressure Sensor for Fingertip Pulse Monitoring. Advanced Healthcare Materials, 9(17), 2001023.

[25] Meng, K., Chen, J., Li, X., Wu, Y., Fan, W., Zhou, Z., ... & Yang, J. (2019). Flexible weaving constructed self‐powered pressure sensor enabling continuous diagnosis of cardiovascular disease and measurement of cuffless blood pressure. Advanced Functional Materials, 29(5), 1806388.

[26] Fan, X., Huang, Y., Ding, X., Luo, N., Li, C., Zhao, N., & Chen, S. C. (2018). Alignment‐Free Liquid‐Capsule Pressure Sensor for Cardiovascular Monitoring. Advanced Functional Materials, 28(44), 1805045

[27] Mulè, G., Sorce, A., Carollo, C., Geraci, G., & Cottone, S. (2019). Self-blood pressure monitoring as a tool to increase hypertension awareness, adherence to antihypertensive therapy, and blood pressure control. Journal of clinical hypertension (Greenwich, Conn.), 21(9), 1305.

[28] Satyanarayana Nimmala, Ramadevi Y., Ramalingaswamy Cheruku, " A Novel Approach to Predict High Blood Pressure Using ABF Function", International Journal of Modern Education and Computer Science(IJMECS), Vol.10, No.7, pp. 67-73, 2018.DOI: 10.5815/ijmecs.2018.07.07

[29] Muthana H. Hamd, Marwa Y. Mohammed, "Multimodal Biometric System based Face-Iris Feature Level Fusion", International Journal of Modern Education and Computer Science(IJMECS), Vol.11, No.5, pp. 1-9, 2019.DOI: 10.5815/ijmecs.2019.05.01

[30] Salmanpour, M. R., Shamsaei, M., Saberi, A., Setayeshi, S., Klyuzhin, I. S., Sossi, V., & Rahmim, A. (2019). Optimized machine learning methods for prediction of cognitive outcome in Parkinson's disease. Computers in biology and medicine, 111, 103347.