A Multi-Site, Multi-Wavelength PPG Platform for Continuous Non-Invasive Health Monitoring in Hospital Settings

Abstract

This article presents a novel PPG acquisition platform capable of synchronous multi-wavelength signal acquisition from two measurement locations with up to 4 independent wavelengths from each in parallel. The platform is fully configurable and operates at 1ksps, accommodating a wide variety of transmitters and detectors to serve as both a research tool for experimentation and a clinical tool for disease monitoring. The sensing probes presented in this work acquire 4 PPG channels from the wrist and 4 PPG channels from the fingertip, with wavelengths such that surrogates for pulse wave velocity and haematocrit can be extracted. For conventional PPG sensing, we have achieved the mean error of 4.08 ± 3.72 bpm for heart-rate and a mean error of 1.54 ± 1.04% for SpO ₂ measurement, with the latter lying within the FDA limits for commercial pulse oximeters. We have further evaluated over 700 individual peak-to-peak time differences between wrist and fingertip signals, achieving a normalized weighted average PWV of 5.80 ± 1.58 m/s, matching with values of PWV found for this age group in literature. Lastly, we introduced and computed a haematocrit ratio ( R_hct) between the deep IR and deep red wavelength from the fingertip sensor, finding a significant difference between male and female values (median of 1.9 and 2.93 respectively) pointing to devices sensitivity to Hct.

Fig. 1. Illustration of the presented PPG acquisition platform.

Fig. 2

PPG waveform shape and most commonly extracted features. The figure contains 2 periods of the waveform with clearly defined dicrotic notch that is acquired from a fingertip sensor. The common features include: T_SD: Time period between systolic and diastolic peak during a single beat, also related to large artery stiffness index [6]; T_S: Time period between two consecutive systolic peaks, also corresponding to the heart rate and heart rate variability when measured over longer periods; P_S: Systolic peak; P_D: Diastolic peak; N_D: Dicrotic notch, the slight increase in the pressure in the beginning of diastole caused by the closure of the aortic valve; A1_S - A2_S: Area under the curve split by the systolic peak; A1_D - A2_D: Area under the curve split by the dicrotic notch.

Fig. 3

Light absorption properties for various blood components and water across the visible and near-IR light spectrum. The solid lines are based on spectroscopy data compiled by S. A. Prahl [8]. The dashed continuation of lines correspond to the approximate absorbances based on the work of Schmitt et al. [9] and Kuenstner et al. [10]. The green horizontal lines correspond to the wavelengths used within this work.

Fig. 4

The wrist probe design inspired by classic watch, houses the PCB inside a 3D printed structure secured on hand by using a flexible strap. The 10-lead medical cable is directly connected to the probe. The PCB pictured in 4(a) contains the PPG array sensor SFH7072 from OSRAM (1) and 10-lead connector (2).

Fig. 5

The semi-flex PCB design of the ring probe provides tight fit on variety of finger shapes and thicknesses. For best results, the sensor should be placed on the top part of the finger or directly on the fingertip. The pictured PCB design in 5(a) contains the emitter region (1), detector region (2), 10-lead connector (3) and flexible regions allowing 90^° bends (4).

Fig. 6

The block diagram of the presented PPG acquisition platform. The AFE4900EVM is an off-the-shelf development board from Texas Instruments, built around the AFE4900 analogue front-end chip for PPG. The chip (pictured in red) contains the whole pipeline for PPG signal with most of the block being configurable by internal registers. To configure this chip and retrieve data, an intermediary microcontroller (pictured in green) translates the standard serial commands from the PC. The probes utilise a 10-lead analogue medical cable with adequate shielding to prevent cross talk. The wrist probe contains 3 LEDs and 2 PDs in reflectance configuration, while the finger probe contains 4 LEDs and 2 PDs in transmissive configuration.

Fig. 7

Illustration of all parts of the system. The tan-coloured 3D printed box contains the TI AFE4900EVM boards providing the analogue front-end for PPG acquisition and analogue-to-digital conversion of the acquired data. A custom designed probes with standardized 10-pin connector are connected on one side, while the microUSB cable is connected to PC from the other. 2 USB cables and 2 analogue cables are needed to operate two sensor probes in parallel.

Fig. 8

Diagram showing the experimental setup. Two sensors are mounted on the subject’s hand on finger and wrist respectively. The Windows application GUI running on the PC is showing real-time data stream coming from the device from all 8 wavelength channels. The visualised data is raw and unfiltered to not introduce additional strain on computing resources.

Fig. 9

An illustration of the digital signal processing pipeline used on the raw data from obtained from the system. In the first stage, signals from the wrist probe are re-sampled to match the finger probe frequency and synchronised. The second stage employs a low pass filter with the cutoff frequency of 20 Hz that removes the high frequency noise including the 50 Hz mains interference. In the next stage, high-pass filter with a cutoff frequency of 0.5 Hz removes the DC offset. Lastly, the scaling step allows precise morphology-based feature extraction. This step is only applicable for features that do not depend on true amplitude. An optional step of smoothing is sometimes performed for low amplitude signals.

Fig. 10

Example filtered waveforms acquired from the finger sensor probe at all 4 wavelengths. For the deep IR wavelength at 1300 nm, water starts to dominate the absorption spectrum, significantly attenuating the transmitted signal and introducing noise. After smoothing the waveform using the Savitzky-Golay filter, the waveform becomes recognisable and both the AC trends are sufficient for extraction of high-level parameters.

Fig. 11

Example waveforms acquired from the wrist sensor probe. The green wavelength has larger amplitude variation thanks to the lower light penetration depth which results in shorter light path throughout tissue and smaller attenuation of the transmitted signal.

Fig. 12

Comparison between the oxygen saturation values obtained from equation (4) using the presented system and displayed values on the Masimo medical pulse oximeter.

Fig. 13. Experimental results for heart-rate monitoring across the subjects. Achieved mean error was 4.08 bpm with standard deviation of 3.72.

Fig. 14. Illustration of time lag between wrist and finger PPG signal using a trough detector.

Fig. 15. Haematocrit ratio (R_hct) plot to illustrate difference between values obtained from male and female participants.

Fig. 16

Boxplots of answers to each of the scored questions. The white square signifies mean score value for each question.+.