Wearable speckle plethysmography (SPG) for characterizing microvascular flow and resistance

Abstract

In this work we introduce a modified form of laser speckle imaging (LSI) referred to as affixed transmission speckle analysis (ATSA) that uses a single coherent light source to probe two physiological signals: one related to pulsatile vascular expansion (classically known as the photoplethysmographic (PPG) waveform) and one related to pulsatile vascular blood flow (named here the speckle plethysmographic (SPG) waveform). The PPG signal is determined by recording intensity fluctuations, and the SPG signal is determined via the LSI dynamic light scattering technique. These two co-registered signals are obtained by transilluminating a single digit (e.g. finger) which produces quasi-periodic waveforms derived from the cardiac cycle. Because PPG and SPG waveforms probe vascular expansion and flow, respectively, in cm-thick tissue, these complementary phenomena are offset in time and have rich dynamic features. We characterize the timing offset and harmonic content of the waveforms in 16 human subjects and demonstrate physiologic relevance for assessing microvascular flow and resistance.

Keywords: (170.0170) Medical optics and biotechnology; (170.3890) Medical optics instrumentation; (230.0230) Optical devices.

Fig. 1

ATSA signal processing diagram. The schematic shows the core algorithmic steps from acquisition of raw data to the final SPG and PPG waveforms.

Fig. 2

ATSA Instrument Description. (a) Orientation of the core hardware components, casing and tissue sample. (b) Physiological origin of the SPG and PPG signals. (c) SPG waveform example. (d) PPG waveform example

Fig. 3

TD Algorithm. (a) The SPG and PPG signals in blue and red, respectively. The black dotted rectangle demarcates the single pulse in Fig. 3(b). (b) Close-up of a single cardiac cycle. The two parallel lines flanked by the inward pointing black arrows visually depict the time delay. In both panels, the black asterisks mark the systolic peaks of the SPG waveform and the green asterisks mark the systolic peaks of the PPG waveform.

Fig. 4

Time Frequency Analysis algorithm. The top left shows a raw SPG tracing from which (1) a single cardiac waveform is extracted. (2) The FFT of the waveform is computed to yield the frequency spectrum, consisting of distinct harmonic peaks. (3) The height of each harmonic is used to calculate harmonic ratios, discrete values stored for each cardiac cycle. (4) The algorithm repeats for the next cardiac cycle, accumulating harmonic ratios into a distribution that can be visualized with a histogram or used to compute a mean.

Fig. 5

In-vitro validation test setup. (a) A syringe pump pushes a hemoglobin solution through a Digit Analog with a clipped on ATSA instrument. (b) Measured flow index in arbitrary units plotted against volumetric flow in ml per minute. The blue circles show ATSA recordings averaged over 10 seconds while the dotted black line shows a linear fit with an R² coefficient of 0.98.

Fig. 6

Comparison of distortion of SPG and PPG signals. (a) Dual-axis plot of the SPG and PPG waveforms during exercise. (b) Dual-axis plot the SPG and PPG waveforms during a cold pressor challenge. In both graphs, SPG signal is blue and PPG signal is red.

Fig. 7

Two Subject Comparison of TD. (a) Dual-axis plot of the SPG and PPG waveforms marked for Subject A. (b) Dual axis plots of the SPG and PPG waveforms for Subject B. In both Fig. 6(a) and (b), SPG and PPG signals are blue and red, respectively. The SPG and PPG signal peaks are highlighted with black and green asterisks, respectively. (c) Histogram distribution of TD calculated for both subjects. The histogram values were extracted from approximately 300 pulses each.

Fig. 8

Time Frequency Analysis Comparison. (a) Single cardiac cycle extracted from the raw flow data of Subject A. (b) Frequency spectrum computed for Subject A’s waveform wherein H1 and H3 demarcate the first (fundamental) and third harmonic, respectively. (c) Single cardiac cycle taken from Subject B. (d) Frequency spectrum computed for Subject B’s waveform. As in Fig. 8(b), H1 and H3 point to the first and third harmonic. (e) Histogram distribution of THR for Subject A (orange) and Subject B (turquoise). The distributions for each subject were extracted from approximately 300 heartbeats.

Fig. 9

SPG-PPG parameters vs. age. (a) Linear regression of TD onto age. (b) Linear regression of THR onto age. The solid red line is the linear best fit of the data. The curved dotted red lines are the 95% confidence intervals. The blue tick marks indicate the individual data points.

Fig. 10

SPG and PPG signals from each of the physiological challenges. (a) Baseline data. (b) Cold pressor data. (c) Post-exercise data. In each panel, the SPG waveform is blue and the PPG waveform is red, both in arbitrary units.

Fig. 11

Changes from baseline in the cold-pressor and exercise challenges. (a) Repeatability testing on a single subject performing seven tandem measurements. Each box plot depicts the range of changes during the cold pressor, exercise and baseline conditions. (b) Changes from baseline obtained from individual measurements performed on four subjects.