How to Enhance Reliability of PPG Data for Health Wearables, According To Maxim Integrated

Maxim Enhance Reliability PPG Data
Image: Maxim Integrated

Whether a sensor can provide reliable data depends on the target information, the algorithm deployed, and the specific details of a given use case. Optical biosensing has become a mainstream feature in smartwatches and fitness wearables, providing actionable insights to support preventative healthcare, chronic disease management, and remote patient monitoring. Ian Chen, a healthcare technology expert at Maxim Integrated, provides an overview of the interaction around photoplethysmography (PPG) data collected by optical biosensors in his blog post, “What’s in Photoplethysmography Data? A Look at the Interaction Between Sensor Performance and Algorithms.”

PPG measures the reflected (back-scattered) or transmitted light through tissue to investigate the variations in blood volume which occur with each heartbeat. Researchers and physicians have derived information including heart rate, respiratory rate, oxygen saturation, blood vessel viscosity, venous reflux, cold sensitivity, blood pressure, and cardiac output by processing PPG data with different algorithms. Whether the captured PPG data can provide all of these insights reliably depends on many factors.

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A pulse oximeter
Image: Lance Cpl. Christopher O’Quin (Wikimedia Commons)

When we take signal-to-noise ratio (SNR) as a measure of PPG data reliability, we see that the measurement environment in a specific end-use and the underlying PPG system design play fundamental roles in assuring reliability. Consider these parameters:

  • Optical configuration, which includes the wavelength of the light(s), the efficiency and field of view of the light-emitting diode (LED), the responsiveness of the photodetector (PD), and the design of the optical path. Each of these factors contributes to the current transfer rate (CTR) of the sensing system.
  • Subject’s skin tone, skin temperature, blood perfusion, and location of the sensor on the body all contribute to the perfusion index (PI). For most use cases, the subjects’ skin characteristics are beyond the control of the designers. Although medical practices typically take PPG data from fingers and ear lobes, those locations may not be convenient for ambulatory and long-term monitoring. Consequently, many designs expect and accept lower PI values.
  • Optical components and system design ultimately control the noises in a PPG system. In most wearable applications, NTX and NRX can be minimized by selecting an analog front-end with better intrinsic signal-to-noise performance. This is particularly important as CTR is typically a small number, especially when miniaturization or other industrial design considerations may constrain optical design. As such, the noise terms in the equation’s denominator begin to dominate.
  • Boosting SNR by increasing LED power consumption. When all else fails, designers can still get reliable PPG data by boosting the LED current, which, of course, substantially increases system power consumption. Furthermore, excessive power consumption may lead to other complexities in the system that compromise data reliability.

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Whereas poor SNR represents a lack of data reliability in general, other factors such as motion artifacts may affect the reliability of different PPG data-derived information to different extents. Read Chen’s blog to better understand the information that can be embedded in PPG data—and how this information can ultimately provide better health and well-being insights to wearable consumers.