Optimal Signal Quality Index for Photoplethysmogram Signals

Overview

Journal Bioengineering (Basel)

Date 2017 Sep 28

PMID 28952584

Citations 74

Authors

Mohamed Elgendi

Affiliations

Soon will be listed here.

Abstract

A photoplethysmogram (PPG) is a noninvasive circulatory signal related to the pulsatile volume of blood in tissue and is typically collected by pulse oximeters. PPG signals collected via mobile devices are prone to artifacts that negatively impact measurement accuracy, which can lead to a significant number of misleading diagnoses. Given the rapidly increased use of mobile devices to collect PPG signals, developing an optimal signal quality index (SQI) is essential to classify the signal quality from these devices. Eight SQIs were developed and tested based on: perfusion, kurtosis, skewness, relative power, non-stationarity, zero crossing, entropy, and the matching of systolic wave detectors. Two independent annotators annotated all PPG data (106 recordings, 60 s each) and a third expert conducted the adjudication of differences. The independent annotators labeled each PPG signal with one of the following labels: excellent, acceptable or unfit for diagnosis. All indices were compared using Mahalanobis distance, linear discriminant analysis, quadratic discriminant analysis, and support vector machine with leave-one-out cross-validation. The skewness index outperformed the other seven indices in differentiating between excellent PPG and acceptable, acceptable combined with unfit, and unfit recordings, with overall F 1 scores of 86.0%, 87.2%, and 79.1%, respectively.

Citing Articles

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References

Orphanidou C, Bonnici T, Charlton P, Clifton D, Vallance D, Tarassenko L . Signal-quality indices for the electrocardiogram and photoplethysmogram: derivation and applications to wireless monitoring. IEEE J Biomed Health Inform. 2014; 19(3):832-8. DOI: 10.1109/JBHI.2014.2338351. View

Clifford G, Behar J, Li Q, Rezek I . Signal quality indices and data fusion for determining clinical acceptability of electrocardiograms. Physiol Meas. 2012; 33(9):1419-33. DOI: 10.1088/0967-3334/33/9/1419. View

Elgendi M . Detection of c, d, and e waves in the acceleration photoplethysmogram. Comput Methods Programs Biomed. 2014; 117(2):125-36. DOI: 10.1016/j.cmpb.2014.08.001. View

Elgendi M . Standard terminologies for photoplethysmogram signals. Curr Cardiol Rev. 2012; 8(3):215-9. PMC: 3465827. DOI: 10.2174/157340312803217184. View

Krishnan R, Natarajan B, Warren S . Two-stage approach for detection and reduction of motion artifacts in photoplethysmographic data. IEEE Trans Biomed Eng. 2010; 57(8):1867-76. DOI: 10.1109/TBME.2009.2039568. View

Selvaraj N, Mendelson Y, Shelley K, Silverman D, Chon K . Statistical approach for the detection of motion/noise artifacts in Photoplethysmogram. Annu Int Conf IEEE Eng Med Biol Soc. 2012; 2011:4972-5. DOI: 10.1109/IEMBS.2011.6091232. View

Takazawa K, Tanaka N, Fujita M, Matsuoka O, Saiki T, Aikawa M . Assessment of vasoactive agents and vascular aging by the second derivative of photoplethysmogram waveform. Hypertension. 1998; 32(2):365-70. DOI: 10.1161/01.hyp.32.2.365. View

Elgendi M, Norton I, Brearley M, Abbott D, Schuurmans D . Systolic peak detection in acceleration photoplethysmograms measured from emergency responders in tropical conditions. PLoS One. 2013; 8(10):e76585. PMC: 3805543. DOI: 10.1371/journal.pone.0076585. View

Elgendi M, Norton I, Brearley M, Fletcher R, Abbott D, Lovell N . Towards Investigating Global Warming Impact on Human Health Using Derivatives of Photoplethysmogram Signals. Int J Environ Res Public Health. 2015; 12(10):12776-91. PMC: 4626999. DOI: 10.3390/ijerph121012776. View

10.

Colquhoun D, Forkin K, Durieux M, Thiele R . Ability of the Masimo pulse CO-Oximeter to detect changes in hemoglobin. J Clin Monit Comput. 2012; 26(2):69-73. DOI: 10.1007/s10877-012-9335-3. View

11.

Tong S, Li Z, Zhu Y, Thakor N . Describing the nonstationarity level of neurological signals based on quantifications of time-frequency representation. IEEE Trans Biomed Eng. 2007; 54(10):1780-5. DOI: 10.1109/TBME.2007.893497. View

12.

Elgendi M, Norton I, Brearley M, Abbott D, Schuurmans D . Detection of a and b waves in the acceleration photoplethysmogram. Biomed Eng Online. 2014; 13:139. PMC: 4190349. DOI: 10.1186/1475-925X-13-139. View

13.

Elgendi M . On the analysis of fingertip photoplethysmogram signals. Curr Cardiol Rev. 2012; 8(1):14-25. PMC: 3394104. DOI: 10.2174/157340312801215782. View

14.

Cannesson M, Delannoy B, Morand A, Rosamel P, Attof Y, Bastien O . Does the Pleth variability index indicate the respiratory-induced variation in the plethysmogram and arterial pressure waveforms?. Anesth Analg. 2008; 106(4):1189-94, table of contents. DOI: 10.1213/ane.0b013e318167ab1f. View

15.

Gehring H, Hornberger C, Matz H, Konecny E, Schmucker P . The effects of motion artifact and low perfusion on the performance of a new generation of pulse oximeters in volunteers undergoing hypoxemia. Respir Care. 2001; 47(1):48-60. View

16.

Brearley M, Heaney M, Norton I . Physiological responses of medical team members to a simulated emergency in tropical field conditions. Prehosp Disaster Med. 2013; 28(2):139-44. DOI: 10.1017/S1049023X12001847. View

17.

Li Q, Clifford G . Dynamic time warping and machine learning for signal quality assessment of pulsatile signals. Physiol Meas. 2012; 33(9):1491-501. DOI: 10.1088/0967-3334/33/9/1491. View

18.

Bortolotto L, Blacher J, Kondo T, Takazawa K, Safar M . Assessment of vascular aging and atherosclerosis in hypertensive subjects: second derivative of photoplethysmogram versus pulse wave velocity. Am J Hypertens. 2000; 13(2):165-71. DOI: 10.1016/s0895-7061(99)00192-2. View

19.

Lu S, Zhao H, Ju K, Shin K, Lee M, Shelley K . Can photoplethysmography variability serve as an alternative approach to obtain heart rate variability information?. J Clin Monit Comput. 2007; 22(1):23-9. DOI: 10.1007/s10877-007-9103-y. View

20.

Elgendi M, Fletcher R, Norton I, Brearley M, Abbott D, Lovell N . Frequency analysis of photoplethysmogram and its derivatives. Comput Methods Programs Biomed. 2015; 122(3):503-12. DOI: 10.1016/j.cmpb.2015.09.021. View