Running wavelet archetype aids the determination of heart rate from the video photoplethysmogram during motion

Addison, Paul; Foo, David M. H.; Jacquel, Dominique

doi:10.1109/embc.2017.8036929

Cited by 3 publications

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In clinical practice, however, an algorithm would have to cope with more significant patient motion, including change of posture or position in bed, hand and limb movements, (including hand movements within the line of sight) and interactions with clinical staff. Recently, our group proposed a novel motion protocol for RGB video monitoring including yaw, pitch and roll maneuvers of the head [24]. Although this is at a very early stage and specific to idealized maneuvers for the subject's head it could perhaps be extended to whole body maneuvers for testing depth-based respiratory technology.…”

Section: Discussionmentioning

confidence: 99%

Continuous respiratory rate monitoring during an acute hypoxic challenge using a depth sensing camera

Addison

Smit

Jacquel

et al. 2019

J Clin Monit Comput

Self Cite

View full text Add to dashboard Cite

Respiratory rate is a well-known to be a clinically important parameter with numerous clinical uses including the assessment of disease state and the prediction of deterioration. It is frequently monitored using simple spot checks where reporting is intermittent and often prone to error. We report here on an algorithm to determine respiratory rate continuously and robustly using a non-contact method based on depth sensing camera technology. The respiratory rate of 14 healthy volunteers was studied during an acute hypoxic challenge where blood oxygen saturation was reduced in steps to a target 70% oxygen saturation and which elicited a wide range of respiratory rates. Depth sensing data streams were acquired and processed to generate a respiratory rate (RR depth). This was compared to a reference respiratory rate determined from a capnograph (RR cap). The bias and root mean squared difference (RMSD) accuracy between RR depth and the reference RR cap was found to be 0.04 bpm and 0.66 bpm respectively. The least squares fit regression equation was determined to be: RR depth = 0.99 × RR cap + 0.13 and the resulting Pearson correlation coefficient, R, was 0.99 (p < 0.001). These results were achieved with a 100% reporting uptime. In conclusion, excellent agreement was found between RR depth and RR cap. Further work should include a larger cohort combined with a protocol to further test algorithmic performance in the face of motion and interference typical of that experienced in the clinical setting.

show abstract

Section: Discussionmentioning

confidence: 99%

Continuous respiratory rate monitoring during an acute hypoxic challenge using a depth sensing camera

Addison

Smit

Jacquel

et al. 2019

J Clin Monit Comput

Self Cite

View full text Add to dashboard Cite

show abstract

Video-based heart rate monitoring across a range of skin pigmentations during an acute hypoxic challenge

Addison

Jacquel

Foo

et al. 2017

J Clin Monit Comput

Self Cite

View full text Add to dashboard Cite

The robust monitoring of heart rate from the video-photoplethysmogram (video-PPG) during challenging conditions requires new analysis techniques. The work reported here extends current research in this area by applying a motion tolerant algorithm to extract high quality video-PPGs from a cohort of subjects undergoing marked heart rate changes during a hypoxic challenge, and exhibiting a full range of skin pigmentation types. High uptimes in reported video-based heart rate (HRvid) were targeted, while retaining high accuracy in the results. Ten healthy volunteers were studied during a double desaturation hypoxic challenge. Video-PPGs were generated from the acquired video image stream and processed to generate heart rate. HRvid was compared to the pulse rate posted by a reference pulse oximeter device (HRp). Agreement between video-based heart rate and that provided by the pulse oximeter was as follows: Bias = − 0.21 bpm, RMSD = 2.15 bpm, least squares fit gradient = 1.00 (Pearson R = 0.99, p < 0.0001), with a 98.78% reporting uptime. The difference between the HRvid and HRp exceeded 5 and 10 bpm, for 3.59 and 0.35% of the reporting time respectively, and at no point did these differences exceed 25 bpm. Excellent agreement was found between the HRvid and HRp in a study covering the whole range of skin pigmentation types (Fitzpatrick scales I–VI), using standard room lighting and with moderate subject motion. Although promising, further work should include a larger cohort with multiple subjects per Fitzpatrick class combined with a more rigorous motion and lighting protocol.

show abstract

A Survey on Heart Biometrics

Rathore

Zhu

et al. 2020

ACM Comput. Surv.

View full text Add to dashboard Cite

In recent years, biometrics (e.g., fingerprint or face recognition) has replaced traditional passwords and PINs as a widely used method for user authentication, particularly in personal or mobile devices. Differing from state-of-the-art biometrics, heart biometrics offer the advantages of liveness detection, which provides strong tolerance to spoofing attacks. To date, several authentication methods primarily focusing on electrocardiogram (ECG) have demonstrated remarkable success; however, the degree of exploration with other cardiac signals is still limited. To this end, we discuss the challenges in various cardiac domains and propose future prospectives for developing effective heart biometrics systems in real-world applications.

show abstract

Running wavelet archetype aids the determination of heart rate from the video photoplethysmogram during motion

Cited by 3 publications

References 17 publications

Continuous respiratory rate monitoring during an acute hypoxic challenge using a depth sensing camera

Continuous respiratory rate monitoring during an acute hypoxic challenge using a depth sensing camera

Video-based heart rate monitoring across a range of skin pigmentations during an acute hypoxic challenge

A Survey on Heart Biometrics

Contact Info

Product

Resources

About