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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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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