Simultaneous Monitoring of Ballistocardiogram and Photoplethysmogram Using a Camera

Shao, Dangdang; Tsow, Francis; Liu, Chenbin; Yang, Yuting; Tao, Nongjian

doi:10.1109/tbme.2016.2585109

Cited by 63 publications

(43 citation statements)

References 41 publications

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“…Some sensor technologies can now integrate multiple modalities, such as chest patches that monitor heart rate, heart rhythm, respiration rate and skin temperature 7,9 . Sensors are being developed to measure myocardial contractility and cardiac output (ballistocardiography), cardiac acoustic data (phonocardiography) and other indices 10 . We describe various biosensors in the following sections, with reference to their target biosignals and potential clinical applications.…”

Section: Biosignal Acquisitionmentioning

confidence: 99%

Integration of novel monitoring devices with machine learning technology for scalable cardiovascular management

et al. 2020

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Ambulatory monitoring is increasingly important for cardiovascular care but is often limited by the unpredictability of cardiovascular events, the intermittent nature of ambulatory monitors and the variable clinical significance of recorded data in patients. Technological advances in computing have led to the introduction of novel physiological biosignals that can increase the frequency at which abnormalities in cardiovascular parameters can be detected, making expert-level, automated diagnosis a reality. However, use of these biosignals for diagnosis also raises numerous concerns related to accuracy and actionability within clinical guidelines, in addition to medico-legal and ethical issues. Analytical methods such as machine learning can potentially increase the accuracy and improve the actionability of device-based diagnoses. Coupled with interoperability of data to widen access to all stakeholders, seamless connectivity (an internet of things) and maintenance of anonymity, this approach could ultimately facilitate near-real-time diagnosis and therapy. T he se tools are increasingly recognized by regulatory a g e n c ies a n d p r o f e s s ional m e d ic al s oc ie ti es, but several technical and ethical issues remain. In this Review, we describe the current state of cardiovascular monitoring along the continuum from biosignal acquisition to the identification of novel biosensors and the development of analytical techniques and ultimately to regulatory and ethical issues. Furthermore, we outline new paradigms for cardiovascular monitoring.

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Section: Biosignal Acquisitionmentioning

confidence: 99%

Integration of novel monitoring devices with machine learning technology for scalable cardiovascular management

et al. 2020

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“…at the wrist. A common approach 52 to estimate BCG motion is to compute optical flow of small patches on the skin surface in regions around the arterial walls. Based on our findings related to conventional optical flow algorithms, it will be important to revisit some of the earlier work to verify that the BCG motion other researchers observed is actually due to the motion of the skin surface i.e., is a true-motion, and is not due to the flawed brightness constancy assumption inherent in the optical flow algorithm used during video analysis, i.e., false-motion highlighted here.…”

Section: Video Stabilization In Blood Perfusion Imaging a Correspondmentioning

confidence: 99%

PulseCam: a camera-based, motion-robust and highly sensitive blood perfusion imaging modality

Kumar

Suliburk

Veeraraghavan

et al. 2020

Sci Rep

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Blood carries oxygen and nutrients to the trillions of cells in our body to sustain vital life processes. Lack of blood perfusion can cause irreversible cell damage. therefore, blood perfusion measurement has widespread clinical applications. in this paper, we develop pulsecam -a new camera-based, motionrobust, and highly sensitive blood perfusion imaging modality with 1 mm spatial resolution and 1 frame-per-second temporal resolution. Existing camera-only blood perfusion imaging modality suffers from two core challenges: (i) motion artifact, and (ii) small signal recovery in the presence of large surface reflection and measurement noise. PulseCam addresses these challenges by robustly combining the video recording from the camera with a pulse waveform measured using a conventional pulse oximeter to obtain reliable blood perfusion maps in the presence of motion artifacts and outliers in the video recordings. For video stabilization, we adopt a novel brightness-invariant optical flow algorithm that helps us reduce error in blood perfusion estimate below 10% in different motion scenarios compared to 20-30% error when using current approaches. PulseCam can detect subtle changes in blood perfusion below the skin with at least two times better sensitivity, three times better response time, and is significantly cheaper compared to infrared thermography. PulseCam can also detect venous or partial blood flow occlusion that is difficult to identify using existing modalities such as the perfusion index measured using a pulse oximeter. With the help of a pilot clinical study, we also demonstrate that pulsecam is robust and reliable in an operationally challenging surgery room setting. We anticipate that pulsecam will be used both at the bedside as well as a point-of-care blood perfusion imaging device to visualize and analyze blood perfusion in an easy-to-use and cost-effective manner. open Scientific RepoRtS | (2020) 10:4825 | https://doi.comparing pulsecam with an infrared thermal camera. To compare PulseCam and infrared thermography, we perform incremental vascular occlusion experiment on 9 participants (5 male and 4 female) of varying skin tones (Fitzpatrick Scale I to VI) and record their palm videos using both a color camera as well as Scientific RepoRtS | (2020) 10:4825 | https://doi.

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“…Developing better methods to combine several BCG axes could be a topic of future study. Also, increasing the total number of axes beyond three – for example, with a gyroscope to measure angular motion – or even analyzing the limb vibrations based on non-contact measurements [25], [26] could also be a topic for future study.…”

Section: Resultsmentioning

confidence: 99%

Wearable Sensing of Cardiac Timing Intervals From Cardiogenic Limb Vibration Signals

2017

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In this paper we describe a new method to measure aortic valve opening (AVO) and closing (AVC) from cardiogenic limb vibrations (i.e., wearable ballistocardiogram [BCG] signals). AVO and AVC were detected for each heartbeat with accelerometers on the upper arm (A), wrist (W), and knee (K) of 22 subjects following isometric exercise. Exercise-induced changes were recorded with impedance cardiography. The method, Filter BCG, detects peaks in distal vibrations after filtering with individually-tuned bandpass filters. In agreement with recent studies, we did not find peaks at AVO and AVC in limb vibrations directly. Interestingly, distal vibrations filtered with FilterBCG yielded reliable peaks at AVO (r2 = 0.95 A, 0.94 W, 0.77 K) and AVC (r2= 0.92 A, 0.89 W, 0.68 K). FilterBCG measures AVO and AVC accurately from arm, wrist, and knee vibrations, and it outperforms the standard R-J interval method.

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Simultaneous Monitoring of Ballistocardiogram and Photoplethysmogram Using a Camera

Cited by 63 publications

References 41 publications

Integration of novel monitoring devices with machine learning technology for scalable cardiovascular management

Integration of novel monitoring devices with machine learning technology for scalable cardiovascular management

PulseCam: a camera-based, motion-robust and highly sensitive blood perfusion imaging modality

Wearable Sensing of Cardiac Timing Intervals From Cardiogenic Limb Vibration Signals

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