Photoplethysmogram Processing Using an Adaptive Single Frequency Phase Vocoder Algorithm
We have previously designed a pulse oximeter connected to a mobile phone, called the Phone Oximeter, for clinical decision making based on photoplethysmography. The limited battery and computational resources demand efficient and low-power algorithms for the Phone Oximeter to be effective in resource-poor and remote areas. We present two new algorithms for the fast and economical estimation of heart rate (HR) from the photoplethysmogram (PPG). One method estimates the HR frequency by adaptively modeling the PPG wave with a sine function using a modified phase vocoder. The other method uses the obtained wave as an envelope for the detection of peaks in the PPG signal. HR is computed using the vocoder center frequency or the peak intervals in a histogram, respectively. PPG data obtained from 42 subjects were processed with the vocoder algorithms and, for comparison, with two traditional methods that use filtering algorithms (Pan-Tompkins) and frequency domain transformations (Fast-Fourier Transform). We compared HR estimates obtained from these four methods to the reference HR obtained from a electrocardiogram. The two vocoder methods performed at least as well as the two traditional methods in terms of normalized root mean sqare error and robustness towards artifacts. Experiments on a mobile device prototype showed comparable speed performance of the vocoder algorithms with the Pan-Tompkins algorithm while the frequency domain approach was nearly two orders of magnitude slower. These results point to further developments using a combination of both vocoder HR estimation methods that will enable the robust implementation of adaptive phase vocoders into mobile device health applications.
KeywordsPulse detection Heart rate estimation Mobile phones Embedded systems Photoplethysmography Vocoder
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- 3.Goldberger, A.L., Amaral, L.A.N., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.K., Stanley, H.E.: PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23), e215–e220 (2000), http://www.physionet.org/physiotools/wfdb/psd/fft.c CrossRefGoogle Scholar
- 4.Karlen, W., Dumont, G., Petersen, C., Gow, J., Lim, J., Sleiman, J., Ansermino, J.M.: Human-centered Phone Oximeter Interface Design for the Operating Room. In: Traver, V., Fred, A., Filipe, J., Gamboa, H. (eds.) HEALTHINF 2011 - Proceedings of the International Conference on Health Informatics, pp. 4333–4337. SciTePress, Rome (2011)Google Scholar
- 5.Karlen, W., Turner, M., Cooke, E., Dumont, G., Ansermino, J.M.: CapnoBase: Signal database and tools to collect, share and annotate respiratory signals. In: Annual Meeting of the Society for Technology in Anesthesia (STA), p. 25. West Palm Beach (2010), http://www.capnobase.org
- 8.Press, W., Teukolsky, S., Vetterling, W., Flannery, B.: Numerical recipes in C: the art of scientific computing, 2nd edn. Cambridge University Press, New York (1992)Google Scholar