Maxim Integrated Highlights Ways to Enhance Reliability of PPG Data for Health-Monitoring Wearables

Rhea-AI Summary

Maxim Integrated Products (NASDAQ: MXIM) emphasizes the importance of reliable photoplethysmography (PPG) data in health monitoring wearables. In a recent blog post, Ian Chen discusses how factors such as optical configuration, subject skin characteristics, and system design affect the data's reliability. He highlights the role of the signal-to-noise ratio (SNR) and warns that increasing LED power consumption might compromise reliability. The insights derived from PPG data can enhance preventive healthcare and chronic disease management.

Positive

• Insightful blog post on enhancing reliability of PPG data.
• Potential to improve health monitoring in wearables.

Negative

• Challenges with SNR affecting data reliability.
• High LED power consumption may lead to compromised reliability.

SAN JOSE, Calif., June 26, 2020 /PRNewswire/ -- 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 smart watches 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 Products, Inc. (NASDAQ: MXIM), 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.

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(s)), 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, N_TX and N_RX 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.

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.

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SOURCE Maxim Integrated Products, Inc.

FAQ

What is the significance of PPG data in health monitoring for MXIM?

PPG data is crucial for deriving health insights, such as heart rate and oxygen saturation, in wearable devices.

How does Maxim Integrated highlight data reliability for wearables?

Maxim discusses factors like optical configuration and subject characteristics that influence PPG data reliability.

What are the risks associated with increasing LED power consumption in PPG devices?

Higher LED power may compromise data reliability and lead to other system complexities.

What does the blog by Ian Chen focus on regarding PPG data?

The blog explores the interaction between sensor performance and algorithms affecting PPG data reliability.

How can PPG data aid in chronic disease management according to MXIM?

Reliable PPG data supports actionable insights for better health management and remote patient monitoring.