Existing Clinical Papers – in order of publication – most recent first
Blood Pressure Estimation from Photoplethysmogram
Using a Spectro-Temporal Deep Neural Network
Gašper Slapničar, Nejc Mlakar & Mitja Luštrek
Published online: 4 August 2019
© 2019 by the authors. Licensee MDPI, Basel, Switzerland
Blood pressure (BP) is a direct indicator of hypertension, a dangerous and potentially deadly condition. Regular monitoring of BP is thus important, but many people have aversion towards cuff-based devices, and their limitation is that they can only be used at rest. Using just a photoplethysmogram (PPG) to estimate BP is a potential solution investigated in our study. We analyzed the MIMIC III database for high-quality PPG and arterial BP waveforms, resulting in over 700 h of signals after preprocessing, belonging to 510 subjects. We then used the PPG alongside its first and second derivative as inputs into a novel spectro-temporal deep neural network with residual connections. We have shown in a leave-one-subject-out experiment that the network is able to model the dependency between PPG and BP, achieving mean absolute errors of 9.43 for systolic and 6.88 for diastolic BP. Additionally we have shown that personalization of models is important and substantially improves the results, while deriving a good general predictive model is difficult. We have made crucial parts of our study, especially the list of used subjects and our neural network code, publicly available, in an effort to provide a solid baseline and simplify potential comparison between future studies on an explicit MIMIC III subset.
Accuracy of blood pressure monitoring devices: a critical need for improvement that could resolve discrepancy in hypertension guidelines
James E. Sharman & Thomas H. Marwick
Published online: 31 October 2018
© Springer Nature Limited 2018
Hypertension is the most significant modifiable risk factor for cardiovascular disease and contributes to the highest global burden of disease. Blood pressure (BP) measurement is among the most important of all medical tests, and it is critical for BP monitoring devices to be accurate. Comprehensive new evidence from meta-analyses clearly shows that many BP monitoring devices (including oscillometric machines and “gold standard” mercury auscultation) do not accurately represent the BP within the arteries at the upper arm (brachial) or central aorta.
Altogether, there is a critical need to improve the accuracy standards of BP monitoring devices.
Optical blood pressure estimation with photoplethysmography and FFT-based neural networks
Xiaoman Xing & Mingshan Sun
Published online: 12 July 2016
© Articles from Biomedical Optics Express are provided here courtesy of Optical Society of America
We introduce and validate a beat-to-beat optical blood pressure (BP) estimation paradigm using only photoplethysmogram (PPG) signal from finger tips. The scheme determines subject-specific contribution to PPG signal and removes most of its influence by proper normalization. Key features such as amplitudes and phases of cardiac components were extracted by a fast Fourier transform and were used to train an artificial neural network, which was then used to estimate BP from PPG. Validation was done on 69 patients from the MIMIC II database plus 23 volunteers. All estimations showed a good correlation with the reference values. This method is fast and robust, and can potentially be used to perform pulse wave analysis in addition to BP estimation.
Real-time Quantifying Heart Beat Rate from Facial Video Recording on a Smart Phone using Kalman Filters
Wen Jun Jiang, Shi Chao Gao, Peter Wittek & Li Zhao
Published at: 15 – 18 October 2014
IEEE 16th International Conference on e-Health Networking, Applications and Services (Healthcom)
Photoplethysmography (PPG) can be carried out through facial video recording by a smart phone camera in ambient light. The main challenge is to eliminate motion artifacts and ambient noise. We describe a real-time algorithm to quantify the heart beat rate from facial video recording captured by the camera of a smart phone. We extract the green channel from the video. Then we normalize it and use a Kalman filter with a particular structure to eliminate ambient noise. This filter also enhances the heart pulse component in the signal distorted by Gaussian noise and white noise. After that we employ a band-pass FIR filter to remove the remaining motion artifacts. This is followed by peak detection or Lomb periodogram to estimate heart rate. The algorithm has low computational overhead, low delay and high robustness, making it suitable for real-time interaction on a smart phone. Finally we describe an Android application based on this study.
Remote plethysmographic imaging using ambient light
Wim Verkruysse, Lars O Svaasand & J Stuart Nelson
Published online: 12 December 2008
© 2008 Optical Society of America
Plethysmographic signals were measured remotely (>1m) using ambient light and a simple consumer level digital camera in movie mode. Heart and respiration rates could be quantified up to several harmonics. Although the green channel featuring the strongest plethysmographic signal, corresponding to an absorption peak by (oxy-) hemoglobin, the red and blue channels also contained plethysmographic information. The results show that ambient light photo-plethysmography may be useful for medical purposes such as characterization of vascular skin lesions (e.g., port wine stains) and remote sensing of vital signs (e.g., heart and respiration rates) for triage or sports purposes.