Pre-processing the Photoplethysmography Signals for Enhancing the Cardiovascular Diseases Detection for Wrist Pulse Analysis in Nadi Ayurveda

Authors

DOI:

https://doi.org/10.4108/eetpht.10.5640

Keywords:

Photoplethysmography Signal, Primary Denoising, Two-stage Adaptive Noise Cancellation, Detrended Filter, Wavelet Transform, Premature ventricular contraction, Nadi Ayurveda

Abstract

INTRODUCTION: In recent years, Photoplethysmography (PPG) signal has played a vital role in detecting Cardiovascular Diseases (CVDs) in case of wrist pulse analysis emulating the Nadi Ayurveda. The PPG signals acquired from the sensor measurement are severely distorted by various artifacts, which significantly impact the accuracy of disease detection and hamper the disease diagnosis process.

OBJECTIVES: Removing the noises is essential before detecting CVDs from the signals and thus, developing a simple and effective noise reduction method for enhancing the PPG signal quality constitutes a challenging research problem, particularly with prominent artifacts.

METHODS: This paper designs an effective pre-processing technique that improves denoising methods to enhance the PPG signal quality. The design of pre-processing technique contains two major phases: Primary denoising-based artifact removal and secondary denoising-based Premature Ventricular Contraction (PVC) detection and Power-Line Interference (PLI) noise removal. The primary denoising method involves coarse and fine-grained filtering. The coarse-grained filtering removes the major artifacts, such as Baseline Wander (BLW) and Motion Artifacts (MA), by developing the Two-Stage Adaptive Noise Canceller (TANC) method. The fine-grained filtering process utilizes a detrended filter to filter the refined signal obtained from the TANC method. For the signals filtered from the primary denoising method, the secondary denoising method targets to detect the PVC-induced PPG signals from the decomposed high-frequency signals and removes high-frequency noise, such as PLI from noisy signals, by adopting the Wavelet Transform (WT) method.

RESULTS: During the signal reconstruction process in the WT method, the research work reconstructs the denoised PPG signals along with the PVC-induced PPG signals. The experimental results of the noise removal methodology illustrated significant improvements in PPG signal quality.

CONCLUSION: The designed pre-processing technique effectively denoises PPG signals, leading to enhanced signal quality which can further aid in accurate disease detection.

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References

Jagannathan, R., Patel, S.A., Ali, M.K. and Narayan, K.M., 2019. Global updates on cardiovascular disease mortality trends and attribution of traditional risk factors. Current diabetes reports, 19(7), pp.1-12. DOI: https://doi.org/10.1007/s11892-019-1161-2

Cardiovascular Disease 2021, Accessed on 16 September 2022, https://www.who.int/health-topics/cardiovascular-diseases#tab=tab_1

Roth, G.A., Mensah, G.A., Johnson, C.O., Addolorato, G., Ammirati, E., Baddour, L.M., Barengo, N.C., Beaton, A.Z., Benjamin, E.J., Benziger, C.P. and Bonny, A., 2020. Global burden of cardiovascular diseases and risk factors, 1990–2019: update from the GBD 2019 study. Journal of the American College of Cardiology, 76(25), pp.2982-3021.

Li, K., Zhang, S., Yang, L., Luo, Z. and Gu, G., 2014. The differences in waveform between photoplethysmography pulse wave and radial pulse wave in movement station. Bio-medical materials and engineering, 24(6), pp.2657-2664. DOI: https://doi.org/10.3233/BME-141082

Mishra, B. and Nirala, N.S., 2020, November. A Survey on Denoising Techniques of PPG Signal. In 2020 IEEE International Conference for Innovation in Technology (INOCON) (pp. 1-8). IEEE. DOI: https://doi.org/10.1109/INOCON50539.2020.9298358

Almarshad, M.A., Islam, M.S., Al-Ahmadi, S. and BaHammam, A.S., 2022, March. Diagnostic Features and Potential Applications of PPG Signal in Healthcare: A Systematic Review. In Healthcare (Vol. 10, No. 3, p. 547). MDPI. DOI: https://doi.org/10.3390/healthcare10030547

Chatterjee, A. and Roy, U.K., 2018, May. PPG based heart rate algorithm improvement with Butterworth IIR filter and Savitzky Golay FIR filter. In 2018 2nd International Conference on Electronics, Materials Engineering & Nanotechnology (IEMENTech) (pp. 1-6). IEEE. DOI: https://doi.org/10.1109/IEMENTECH.2018.8465225

Anagha, S., Suyampulingam, A. and Ramachandran, K.I., 2018, July. A Better Digital Filtering Technique for Estimation of SPO 2 and Heart Rate from PPG Signals. In 2018 International Conference on Inventive Research in Computing Applications (ICIRCA) (pp. 804-809). IEEE. DOI: https://doi.org/10.1109/ICIRCA.2018.8597329

Tun, H.M., 2021. Photoplethysmography (PPG) Scheming System Based on Finite Impulse Response (FIR) Filter Design in Biomedical Applications. Int. J. Electr. Electron. Eng. Telecommun, 10, pp.272-282. DOI: https://doi.org/10.18178/ijeetc.10.4.272-282

Kasambe, P.V. and Rathod, S.S., 2015. VLSI wavelet based denoising of PPG signal. Procedia Computer Science, 49, pp.282-288. DOI: https://doi.org/10.1016/j.procs.2015.04.254

Awodeyi, A.E., Alty, S.R. and Ghavami, M., 2013, November. Median filter approach for removal of baseline wander in photoplethysmography signals. In 2013 European Modelling Symposium (pp. 261-264). IEEE. DOI: https://doi.org/10.1109/EMS.2013.45

Duan, K., Hu, Y., Qian, Z. and Wang, G., 2016, October. An FPGA-based morphological filter for baseline wandering correction in photoplethysmography. In 2016 IEEE Biomedical Circuits and Systems Conference (BioCAS) (pp. 216-219). IEEE. DOI: https://doi.org/10.1109/BioCAS.2016.7833770

Timimi, A.A., Ali, M.M. and Chellappan, K., 2017. A novel AMARS technique for baseline wander removal applied to photoplethysmogram. IEEE transactions on biomedical circuits and systems, 11(3), pp.627-639. DOI: https://doi.org/10.1109/TBCAS.2017.2649940

Aarthi, Y., Karthikeyan, B., Raj, N.P. and Ganesan, M., 2019, March. Fingertip based estimation of heart rate using photoplethysmography. In 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS), (pp. 817-821). IEEE. DOI: https://doi.org/10.1109/ICACCS.2019.8728432

Peng, F., Liu, H. and Wang, W., 2015. A comb filter-based signal processing method to effectively reduce motion artifacts from photoplethysmographic signals. Physiological measurement, 36(10), p.2159. DOI: https://doi.org/10.1088/0967-3334/36/10/2159

Han, H. and Kim, J., 2012. Artifacts in wearable photoplethysmographs during daily life motions and their reduction with least mean square based active noise cancellation method. Computers in biology and medicine, 42(4), pp.387-393. DOI: https://doi.org/10.1016/j.compbiomed.2011.12.005

Wijshoff, R.W., Mischi, M. and Aarts, R.M., 2016. Reduction of periodic motion artifacts in photoplethysmography. IEEE Transactions on Biomedical Engineering, 64(1), pp.196-207. DOI: https://doi.org/10.1109/TBME.2016.2553060

Hanyu, S. and Xiaohui, C., 2017, May. Motion artifact detection and reduction in PPG signals based on statistics analysis. In 2017 29th Chinese control and decision conference (CCDC) (pp. 3114-3119). IEEE. DOI: https://doi.org/10.1109/CCDC.2017.7979043

Chowdhury, S.S., Hasan, M.S. and Sharmin, R., 2019, October. Robust heart rate estimation from ppg signals with intense motion artifacts using cascade of adaptive filter and recurrent neural network. In TENCON 2019-2019 IEEE Region 10 Conference (TENCON) (pp. 1952-1957). IEEE. DOI: https://doi.org/10.1109/TENCON.2019.8929692

Park, J., Seok, H.S., Kim, S.S. and Shin, H., 2022. Photoplethysmogram Analysis and Applications: An Integrative Review. Frontiers in Physiology, p.2511. DOI: https://doi.org/10.3389/fphys.2021.808451

Faiz, M.M.U. and Kale, I., 2022. Removal of multiple artifacts from ECG signal using cascaded multistage adaptive noise cancellers. Array, 14, p.100133. DOI: https://doi.org/10.1016/j.array.2022.100133

Kavitha, K., Vasuki, S. And Karthikeyan, B., PPG Signal Denoising using a New Method for the Selection of Optimal Wavelet Transform Parameters.

BIDMC dataset: Available at: https://physionet.org/content/bidmc/1.0.0/, Accessed On October 2022.

Elgendi, M., 2016. Optimal signal quality index for photoplethysmogram signals. Bioengineering, 3(4), p.21. DOI: https://doi.org/10.3390/bioengineering3040021

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Published

04-04-2024

How to Cite

1.
Tandon A, Kumar V, Choudhury T. Pre-processing the Photoplethysmography Signals for Enhancing the Cardiovascular Diseases Detection for Wrist Pulse Analysis in Nadi Ayurveda. EAI Endorsed Trans Perv Health Tech [Internet]. 2024 Apr. 4 [cited 2024 May 20];10. Available from: https://publications.eai.eu/index.php/phat/article/view/5640