Galya Georgieva-Tsaneva
ABSTRACT
The report presents an algorithm for noise reduction in photoplethysmographic signals, applying a hybrid method based on wavelet analysis and adaptive processing threshold. The algorithm is recursive, works in the time-frequency domain, processes the detailed coefficients at each level of decomposition. Apply a discrete wavelet transform with the possibility to determine the optimal basis (Daubechies bases with a different number of coefficients were studied). The proposed algorithm is efficient, achieves good performance, and is evaluated using the parameters Signal to Noise Ratio, Root Mean Square Error, and Percentage Root Mean Square Difference. The presented algorithm is applied and tested on real PPG signals. The algorithm was tested in a software program for preprocessing of PPG signals, determination of the time series of heart rate variability, respiratory rate, and mathematical analysis of the characteristics and quality of the signal.
- C. Fischer, B. Domer, T. Wibmer, and T. Penzel, 2017. “An algorithm for real-time pulse waveform segmentation and artifact detection in photoplethysmograms,” IEEE Journal of Biomedical and Health Informatics, vol. 21, no. 2, pp. 372–381. https://ieeexplore.ieee.org/document/7384413Google Scholar
Cross Ref
- Galya Georgieva-Tsaneva, Evgeniya Gospodinova and Krasimir Cheshmedzhiev. 2022. Cardiodiagnostics Based on Photoplethysmographic Signals. Diagnostics 2022, 12, 412. https://doi.org/10.3390/diagnostics12020412.Google Scholar
- A. Koneshloo and D. Du, 2019. "A Novel Motion Artifact Removal Method via Joint Basis Pursuit Linear Program to Accurately Monitor Heart Rate," in IEEE Sensors Journal, vol. 19, no. 21, pp. 9945-9952, 1 Nov.1, , doi: 10.1109/JSEN.2019.2927994.Google Scholar
- S. Chowdhury, R. Hyder, M. Hafiz, and M. Haque, 2018. “Real-time robust heart rate estimation from wrist-type PPG signals using multiple reference adaptive noise cancellation,” IEEE J. Biomed. Health Inform., vol. 22, no. 2, pp. 450–459.Google ScholarCross Ref
- M. Ram, K. Madhav, E. Krishna, N. Komalla, and K. Reddy, 2012. “A novel approach for motion artifact reduction in PPG signals based on AS-LMS adaptive filter,” IEEE Trans. Instrum. Meas., vol. 61, no. 5, pp. 1445–1457.Google ScholarCross Ref
- Z. Zhang, Z. Pi, and B. Liu, 2015. “TROIKA: A general framework for heart rate monitoring using wrist-type photoplethysmographic signals during intensive physical exercise,” IEEE Trans. Biomed. Eng., vol. 62, no. 2, pp. 522–531.Google ScholarCross Ref
- W. Karlen, J. Ansermino and G. Dumont, 2012. "Adaptive pulse segmentation and artifact detection in photoplethysmography for mobile applications," 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3131-3134, doi: 10.1109/EMBC.2012.6346628.Google Scholar
- Azzedine Dliou, Samir Elouaham, Rachid Latif and Mostafa Laaboubi. 2019. Combination of the CEEM Decomposition with Adaptive Noise and Periodogram Technique for ECG Signals Analysis. Chapter in Practical Applications of Electrocardiogram (Edited by Umashankar Lakshmanadoss). DOI: 10.5772/intechopen.86007 https://www.intechopen.com/chapters/69455Google Scholar
- Dong Han, Syed Bashar, Jesús Lázaro, Fahimeh Mohagheghian, Andrew Peitzsch, Nishat Nishita, Eric Ding, Emily Dickson, Danielle DiMezza, Jessica Scott, Cody Whitcomb, Timothy Fitzgibbons, David McManus and Ki Chon. 2022. A Real-Time PPG Peak Detection Method for Accurate Determination of Heart Rate during Sinus Rhythm and Cardiac Arrhythmia. Biosensors, 12, 82. https://doi.org/10.3390/bios12020082.Google ScholarCross Ref
- A. Karim, Muhammad Rahman, Md. Mahmud, Shikder Bashar, Md. Miah, Maliha Marium. 2019. Analysis of Empirical Mode Decomposition Method of Heart Rate Extraction from Motion Artifact Corrupted PPG Signal. American Journal of Signal Processing, 9(1): 6-10 DOI: 10.5923/j.ajsp.20190901.02Google Scholar
- H. Zhang, Y. Sun, Y. Lu, J. Lan, and Y. Ji, 2015. "A novel motion and Noise Artifacts Reduction Mechanism (MNARM) for wearable PPG-based heart rate extraction," IET International Conference on Biomedical Image and Signal Processing (ICBISP 2015), 2015, pp. 1-4, doi: 10.1049/cp.2015.0785.Google Scholar
- M. Chowdhury, M. Shuzan, M. Chowdhury, Z. Mahbub, M. Uddin, A. Khandakar, M. Reaz. 2020. Estimating Blood Pressure from Photoplethysmogram Signal and Demographic Features using Machine Learning Techniques. Sensors, 20(11), 3127; https://doi.org/10.3390/s20113127Google Scholar
- D. Donoho.1995. Denoising by soft thresholding. IEEE Transactions on Information Theory, 41(3), 613-627.Google ScholarDigital Library
- G. Georgieva-Tsaneva, K.Tcheshmedjiev. 2013. Denoising of Electrocardiogram Data with Methods of WA, In: Local Proceed. of 14th Intern. Conf. on Computer Systems and Technologies-CompSysTech’13, University of Ruse, Bulgaria, 9-16.Google Scholar
- Jihyoung Lee,Kenta Matsumura,Ken-ichi Yamakoshi,Peter Rolfe,Shinobu Tanaka, Takehiro Yamakoshi. 2013. Comparison between red, green and blue light reflection photoplethysmography for heart rate monitoring during motion. Annu Int Conf IEEE Eng Med Biol Soc. 2013;2013:1724-7. doi: 10.1109/EMBC.2013.6609852.Google Scholar
- E. Gospodinova, P. Lebamovski, M.Gospodinov.2021. Automatic analysis of ECG signals based on their fractal and multifractal properties. International Conference on Computer Systems and Technologies'21, Ruse, Bulgaria, June 18 - 19, Association for Computing Machinery, New York, NY, United States, ISBN:978-1-4503-8982-2, DOI:10.1145/3472410.3472421, 136-140.Google Scholar
- K.Cheshmedzhiev,E.Gospodinova,M.Gospodinov,P.Lebamovski. 2021. Electronic sensor system for registering ECG and PPG signals. International Conference on Computer Systems and Technologies'21, Ruse, Bulgaria, June 18 - 19, Association for Computing Machinery, New York, NY, United States, ISBN:978-1-4503-8982-2, DOI:10.1145/3472410.3472422, 141-144.Google ScholarCross Ref
- T.Todorov, G.Bogdanova, N. Noev, N. Sabev.2019. Data management in a Holter Monitoring System, TEM Journal, 8(3), pp. 801-805.Google Scholar
Index Terms
- A Novel Photoplethysmographic Noise Removal Method via Wavelet Transform to Effective preprocessing
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