|Title||Spectral photoplethysmographic imaging sensor fusion for enhanced heart rate detection|
|Publication Type||Conference Paper|
|Year of Publication||2015|
|Authors||Amelard, R., D. A. Clausi, and A. Wong|
|Conference Name||SPIE photonics West|
|Keywords||heart rate, non-contact, photoplethysmographic imgaging, Photoplethysmography|
Continuous heart rate monitoring can provide important context for quantitative clinical assessment in scenarios such as long-term health monitoring and disability prevention. Photoplethysmographic imaging (PPGI) systems are particularly useful for such monitoring scenarios as contact-based devices pose problems related to comfort and mobility. Each pixel can be regarded as a virtual PPG sensor, thus enabling simultaneous measurements of multiple skin sites. Existing PPGI systems analyze temporal PPGI sensor fluctuations related to hemodynamic pulsations across a region of interest to extract the blood pulse signal. However, due to spatially varying optical properties of the skin, the blood pulse signal may not be consistent across all PPGI sensors, leading to inaccurate heart rate monitoring. To increase the hemodynamic signal-to-noise ratio (SNR), we propose a novel spectral PPGI sensor fusion method for enhanced estimation of the true blood pulse signal. Motivated by the observation that PPGI sensors with high hemodynamic SNR exhibit a spectral energy peak at the heart rate frequency, an entropy-based fusion model was formulated to combine PPGI sensors based on the sensors’ spectral energy distribution. The optical PPGI device comprised a near infrared (NIR) sensitive camera and an 850 nm LED. Spatially uniform irradiance was achieved by placing optical elements along the LED beam, providing consistent illumination across the skin area. Dual-mode temporally coded illumination was used to negate the temporal effect of ambient illumination. Experimental results show that the spectrally weighted PPGI method can accurately and consistently extract heart rate information where traditional region-based averaging fails.