Photoplethysmography behind the Ear Outperforms Electrocardiogram for Cardiovascular Monitoring in Dynamic Environments

Abstract

An increasing proportion of occupational mishaps in dynamic, high-risk operational environments have been attributed to human error, yet there are currently no devices to routinely provide accurate physiological data for insights into underlying contributing factors. This is most commonly due to limitations of commercial and clinical devices for collecting physiological data in environments of high motion. Herein, a novel Photoplethysmography (PPG) sensor device was tested, called SPYDR (Standalone Performance Yielding Deliberate Risk), reading from a behind-the-ear location, specifically designed for high-fidelity data collection in highly dynamic high-motion, high-pressure, low-oxygen, and high-G-force environments. For this study, SPYDR was installed as a functional ear-cup replacement in flight helmets worn by rated US Navy aircrew. Subjects were exposed to reduced atmospheric pressure using a hypobaric chamber to simulated altitudes of 25,000 feet and high G-forces in a human-rated centrifuge up to 9 G acceleration. Data were compared to control devices, finger and forehead PPG sensors, and a chest-mounted 12-lead ECG. SPYDR produced high-fidelity data compared to controls with little motion-artifact controls in the no-motion environment of the hypobaric chamber. However, in the high-motion, high-force environment of the centrifuge, SPYDR recorded consistent, accurate data, whereas PPG controls and ECG data were unusable due to a high-degree-motion artifacts. The data demonstrate that SPYDR provides an accurate and reliable system for continuous physiological monitoring in high-motion, high-risk environments, yielding a novel method for collecting low-artifact cardiovascular assessment data important for investigating currently inaccessible parameters of human physiology.

Photoplethysmogram Recording Length: Defining Minimal Length Requirement from Dynamical Characteristics

“…Thus, PPG can be used for mental stress identification [ 6 , 7 , 8 ] and early detection of depression [ 10 ], which is one of the mental disorders with various social and health consequences recognized by the WHO as a leading cause of disability worldwide [ 12 ]; it is also applicable for estimation of psychiatric patients’ recovery [ 9 ]. Additionally, PPG was previously applied for occupational physiological health monitoring [ 13 , 14 , 15 ]. In addition to its wide applicability for health monitoring purposes, PPG technology is also simple and inexpensive, and—as such—it has considerable potential for use in accessible, affordable mobile health monitoring for prevention and early detection of disease, including CVD and mental disorders.…”

Preprocessing of PPG and EDA signals for detection of emotional and cognitive states via physiological signals

“…Every act of monitoring, as suggested above, could be successfully achieved through the use of sensors and systems for acquisition of physiological signals such as Electrodermal Activity (EDA), Photoplethysmography (PPG), Electrocardiography (ECG), etc. The easiest and most accurate physiological signals for out-of-the-lab monitoring prove to be the EDA (Yang, 2021) and PPG, which outperforms ECG in dynamic scenarios (Bradke, 2021). There are many databases with physiological signals adapted for emotional and cognitive states detection, for example, DEAP (Koelstra, 2012), CASE (Sharma, 2019) and CLAS (Markova, 2019).…”