Abstract
Photoplethysmography (PPG) is a biomedical signal which is obtained non-invasively by electro-optical sensors. Detection of peaks and onsets is required to estimate some important parameters such as heart rate (HR) and heart rate variability (HRV) using PPG signals. The robust detection of the peaks and onsets of a changing morphological wave with baseline drift and oscillations is a challenging one. This paper presents a novel real-time automated method to detect the peaks and onsets with low computational complexity. The method essentially consists of three stages. The first stage is a preprocessing stage where the PPG signal is transformed and smoothed followed by the detection stage where the peaks and onsets are detected. The last stage is the validation stage where the identified peaks and onsets are validated. The proposed method is tested with the PPG dataset, which is taken out of ten persons with duration of 10 min. The results reveal that the algorithm detects the peaks and onsets with highest average accuracy of 99.87%, average sensitivity of 99.91% and average positive predictive value (PPV) of 99.96%. The algorithm is implemented in the Cortex M4 platform using the Keil µvision 4 IDE and it requires only 12.52 kB of memory and speed of 0.5 MIPS. Since computational cost and speed is very critical for low-cost embedded platforms, the proposed method can be used to detect the peaks and onsets very effectively.
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References
Öberg P, Togawa T, Spelman F (2004) Optical sensors in medical care. Sens Appl 3 (Sensors in Medicine and Health Care. (Editors), Wiley-VCH)
Molitor H, Kniazuk M (1936) A new bloodless method for continuous recording of peripheral change. J Pharmacol Expr Ther 27:5–16
Bolanos M, Nazeran H, Haltiwanger E (2006) Comparison of heart rate variability signal features derived from electrocardiography and photoplethysmography in healthy individuals. In: Annual international conference of the IEEE engineering in medicine and biology society. vol 1, pp 4289–4294
Myers GA, Martin GJ, Magid NM, Barnett PS, Schaad JW, Weiss JS, Lesch M, Singer DH (1986) Power spectral analysis of heart rate variability in sudden cardiac death: comparison to other methods. IEEE Trans Bio-Med Eng 33(12):1149–1156
Choi J, Gutierrez-Osuna R (2011) Removal of respiratory influences from heart rate variability in stress monitoring. IEEE Sens J 11(11):2649–2656
Kamal AAR, Harness JB, Irving G, Mearns AJ (1989) Skin photoplethysmography—a review. Comput Meth Prog Biomed 28:257–269
Allen J (2007) Photoplethysmography and its application in clinical physiological measurement. Physiol Measur 28(3)
Feng L, Po LM, Xu X, Li Y, Ma R (2014) Motion resistant remote imaging photoplethysmography based on optical properties of skin. IEEE Trans Circ Syst Video Technol 25(5):879–891
Billauer E (2012) Peakdet: peak detection using MATLAB. http://billauer.co.il/peakdet.html
Zong W, Heldt T, Moody GB, Mark RG (2003) An open-source algorithm to detect onset of arterial blood pressure pulses. Comp Cardio 30:259–262
Bistra N, Ivo I (2010) An automated algorithm for fast pulse wave detection. Bioautomation 14:203–216
Kamal AA, Harness JB, Irving G, Mearns AJ (1989) Skin photoplethysmography—a review. Comp Method Prog Biomed 28:257–269
Webster JG (ed) (2009) Medical instrumentation: application and design. Wiley, New York
Awodeyi AE, Alty SR, Ghavami M (2014) On the filtering of photoplethysmography signals, bioinformatics and bioengineering (BIBE). In: 2014 IEEE international conference, pp 175–178
Farooq U, Jang D-G, Park J-H, Park S-H (2010) PPG delineator for real-time ubiquitous applications. In: Annual international conference of the IEEE engineering in medicine and biology society. vol 2010, pp 4582–4585
Aboy M, McNames J, Thong T, Tsunami D, Ellenby MS, Goldstein B (2005) An automatic beat detection algorithm for pressure signals. IEEE Trans Bio-Med Eng 52(10):1662–1670
Li BN, Dong MC, Vai MI (2010) On an automatic delineator for arterial blood pressure waveforms. Biomed Signal Process Control 5(1):76–81
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Madhan Mohan, P., Nagarajan, V., Vignesh, J.C. (2018). Real-Time Automatic Peaks and Onsets Detection of Photoplethysmographic Signals. In: Li, J., Sankar, A., Beulet, P. (eds) VLSI Design: Circuits, Systems and Applications . Lecture Notes in Electrical Engineering, vol 469. Springer, Singapore. https://doi.org/10.1007/978-981-10-7251-2_9
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DOI: https://doi.org/10.1007/978-981-10-7251-2_9
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