Photoplethysmography Signal Analysis for Optimal Region-of-Interest Determination in Video Imaging on a Built-In Smartphone under Different Conditions

Abstract

Smartphones and tablets are widely used in medical fields, which can improve healthcare and reduce healthcare costs. Many medical applications for smartphones and tablets have already been developed and widely used by both health professionals and patients. Specifically, video recordings of fingertips made using a smartphone camera contain a pulsatile component caused by the cardiac pulse equivalent to that present in a photoplethysmographic signal. By performing peak detection on the pulsatile signal, it is possible to estimate a continuous heart rate and a respiratory rate. To estimate the heart rate and respiratory rate accurately, which pixel regions of the color bands give the most optimal signal quality should be investigated. In this paper, we investigate signal quality to determine the best signal quality by the largest amplitude values for three different smartphones under different conditions. We conducted several experiments to obtain reliable PPG signals and compared the PPG signal strength in the three color bands when the flashlight was both on and off. We also evaluated the intensity changes of PPG signals obtained from the smartphones with motion artifacts and fingertip pressure force. Furthermore, we have compared the PSNR of PPG signals of the full-size images with that of the region of interests (ROIs).

A Novel Personalized Motion and Noise Artifact (MNA) Detection Method for Smartphone Photoplethysmograph (PPG) Signals

“…The hand movement MNAs can be introduced into smartphone signals by involuntary hand movement coming from physical body movement, tremors or after-exercise. On the other hand, fingertip misplacement MNA can occur by placing the fingertip partially on the camera lens, which can measure only some parts of a fingertip with background [10, 11]. Finally, lens-pressing MNAs can occur by pressing lens harder than required.…”

Enhancing the Robustness of Smartphone Photoplethysmography: A Signal Quality Index Approach

“…Third, there are many methods for improving smartphone PPG accuracy [27,[29][30][31][32][33]. For example, adding a suitable bandpass filter for signal processing [28] or excluding data with RR intervals that differ more than a certain threshold [70] are simple and effective approaches to reduce noise.…”

“…Given that smartphones with in-built cameras have become a part of modern life, using them to access health information is an ideal alternative when ECGs or similar medical devices are not available [26]. In addition, there have been several reported techniques for increasing the accuracy of smartphone PPG, such as point-of-interest selection [27], bandpass filtering [28], adaptive signal thresholding [29], motion detection techniques [30][31][32], interpolation techniques [33], and signal decomposition methods [34][35][36]. Bioengineering studies indicate that the average HR [37] and HRV measured using smartphone PPG are comparable with those measured using gold standard ECGs [21,28,[38][39][40].Although it is a promising solution for practical data collection and has an accuracy that has been well proved in several experiments, using smartphone PPG to measure HRV has received limited research attention in applied disciplines such as medicine or psychology [41]; a possible explanation is the lack of robustness in practical scenarios.…”

Factitious Hemoperfusion Based ROI Optimization in PPGi Analysis and its Application for Blood Pressure Determination

“…There have been lots of reported works for each of them. For instance, studies about video collection are mainly focused on camera [7], light-source [8] and their spatial distribution [9]; nowadays, phone collected [10] and ambient light excited [11] PPGi signal collections have been realized. Image processing calculation is an inescapable step in PPGi technique, in order to get a correct and valuable waveform, lots of strategies have been exploited to enhance the Signal-to-Noise Ratio (SNR), such as the methods of motion compensation [12,13], Fourier Transform (FFT) [14], wavelet transform [15,16].…”