Quantifiying the Influence of Moving Artifact on the Determination of Pulse Rate Variability (PRV) from the Pulse Oximetry (SpO2) Signal Measurements

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Determination of Heart Rate Variability (HRV) derived from the Pulse Rate Variability (PRV) of the SpO2 signals measurement can be used to monitor cardiac activity. One disadvantage of the use of SpO2 probe is due to existence unavoidable movement artifacts. These artifacts tend to reduce the accuracy of PRV determination. In order to quantify the influence of moving artifacts on the measured SpO2 signals, the Short-time Fourier Transform (STFT) method is used and this has not been done in previous studies. This method is regarded to be suitable since the artifacts only occurs momentarily, i.e. as the finger moves. Three modes of finger movements were simulated, in addition to the still finger as a control, i.e. in direction of up-down, left-right, and rotating one. Contributing spectra from each of these movements will be recognized, and suitable filtering schemes are then being applied to suppress the influence of these moving artifacts. Parallelly measurements using three-leads ECG were also done to determine the HRV for each of the finger movements condition. Results show that by implementing filtering scheme to each mode of finger movements may reduce the error rate in HRV determination from SpO2 measurements, i.e. from 6 - 25 % (without filtering) to be only 0 - 1.56 %. Meanwhile measurements both HRV and PRV under still finger show only 0-3.33 % difference for each of data groups.

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Edited by:

Khairurrijal, Kuwat Triyana and Shidiq Nur Hidayat

Pages:

204-208

Citation:

Sumber and A. Nasution, "Quantifiying the Influence of Moving Artifact on the Determination of Pulse Rate Variability (PRV) from the Pulse Oximetry (SpO2) Signal Measurements", Applied Mechanics and Materials, Vol. 771, pp. 204-208, 2015

Online since:

July 2015

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$38.00

[1] S. de Lacy, Signal artifact, intrinsic, extrinsic or pathological?, ESST Meeting, Paris. (2012).

[2] C.K. Lao, U.K. Che, W. Chen, Portable Heart Rate Detector Based on Photoplethysmography with Android Programmable Devices for Ubiquitous Health Monitoring System, Int. J. Advances in Telecommunications, Electrotechnics, Signals and Systems 2(1) (2013).

DOI: https://doi.org/10.11601/ijates.v2i1.22

[3] C.S. Bulte, S.W. Keet, C. Boer, R.A. Bouwman, Level of agreement between heart rate variability and pulse rate variability in healthy individuals, European Journal of Anaesthesiology, 28(1) 2011 34-38.

DOI: https://doi.org/10.1097/eja.0b013e32834088c4

[4] Y-T. Li, Pulse Oximetry, Departement of Electronic Engineering, University of Surrey, Guildford. (2011).

[5] K.V.P. Naraharisetti, M. Bawa, Comparison of different signal processing method for reducing artifacts from photoplethysmograph signal, 2011 IEEE Int. Conf. Electro/Information Technology (EIT), Mankato, 15-17 May 2011, pp.1-8.

DOI: https://doi.org/10.1109/eit.2011.5978571

[6] H. Han, J. Kim, Artifacts in wearable photoplethysmographs during daily life motions and their reduction with least mean square based active noise cancellation method, Comput Biol Med. 42(4) (2012) 387-393.

DOI: https://doi.org/10.1016/j.compbiomed.2011.12.005

[7] K.V. Madhavi, M.R. Ram, E.H. Krishna, K.N. Reddy, K.A. Reddy, Estimation of respiratory rate from principal components photoplethysmographic signal, 2010 IEEE EMBS Conf. Biomedical Engineering and Sciences (IECBES), Kuala Lumpur, 30 Nov-2 Dec 2010, pp.311-314.

DOI: https://doi.org/10.1109/iecbes.2010.5742251

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