Multi-wavelength transmittance photoplethysmography with near infrared laser diodes during exercise

Silva, S. M. Lopez; Giannetti, R.; Dotor, María Luisa; Golmayo, D.; Martin, P.; Miguel‐Tobal, Francisco; Bilbao, A.; Silveira, Juan Pedro

doi:10.1117/12.633042

Cited by 1 publication

(1 citation statement)

References 11 publications

(11 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The PTG-based approach is thus drawing attention as an alternative noninvasive measurement of arterial stiffness. However, although photoplethysmographic parameters derived from PTG signals such as the stiffness index and the reflection index can easily be estimated from a pulse wave with prominent systolic and diastolic peaks such as in healthy subjects without significant arterial stiffness, the systolic and diastolic peaks are ill-defined in the aged and those with premature arterial stiffening [10,11]. Therefore, the second derivative of photoplethysmogram (SDPTG), which is also referred to as the acceleration photoplethysmogram, has been widely used for pulse wave analysis since it can stabilize the baseline drift and enable the individual pulses to be visualized [12,13].…”

Section: Introductionmentioning

confidence: 99%

Untitled

Jang¹,

Park²,

Hahn³

2013

J. Med. Biol. Eng.

View full text Add to dashboard Cite

Knowledge-based rules for delineating the second derivative of photoplethysmogram (SDPTG), which is widely used as an indicator of arterial stiffness, are proposed in this study. The SDPTG facilitates the distinction of five sequential waves, namely the initial positive wave (IPW), the early negative wave (ENW), the late upsloping wave (LUW), the late downsloping wave (LDW), and the diastolic positive wave (DPW). An analysis of these waves indicates that the SDPTG is a slowly time-varying signal and that the difference between two adjacent pulses cannot go beyond a certain range. It also indicates that the diastolic positive wave can be accurately estimated from the envelope of the SDPTG signal even with a noisy signal. To delineate the SDPTG, pulse waveforms are first divided into pulse segments, each of which contains one period of the SDPTG signal, using the slope sum function with an adaptive thresholding scheme, which simplifies detecting pulse onsets by enhancing the upslope of the pulse signal and suppressing the remainder of the signal. After pulse segmentation, IPW is first identified by picking the maximum positive peak in the segment. DPW is then extracted using a knowledge-based rule that uses the envelope of the SDPTG signal. In the range from IPW to DPW, ENW, LUW, and LDW are sequentially determined using knowledgebased rules. The proposed method is evaluated using the HIMS database, which includes 1,386 pulses. A positive predictive value of 99.71% and a false negative rate of 1.02% are obtained, and thus the proposed rules are expected to facilitate pulse diagnosis using SDPTG signals.

show abstract