1,157 research outputs found
The 2023 wearable photoplethysmography roadmap
Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology
Mobile Personal Healthcare System for Non-Invasive, Pervasive and Continuous Blood Pressure Monitoring: A Feasibility Study
Background: Smartphone-based blood pressure (BP) monitor using photoplethysmogram (PPG) technology has emerged as a promising approach to empower users with self-monitoring for effective diagnosis and control ofhypertension (HT).
Objective: This study aimed to develop a mobile personal healthcare system for non-invasive, pervasive, and continuous estimation of BP level and variability to be user-friendly to elderly.
Methods: The proposed approach was integrated by a self-designed cuffless, calibration-free, wireless and wearable PPG-only sensor, and a native purposely-designed smartphone application using multilayer perceptron machine learning techniques from raw signals. We performed a pilot study with three elder adults (mean age 61.3 ± 1.5 years; 66% women) to test usability and accuracy of the smartphone-based BP monitor.
Results: The employed artificial neural network (ANN) model performed with high accuracy in terms of predicting the reference BP values of our validation sample (n=150). On average, our approach predicted BP measures with accuracy \u3e90% and correlations \u3e0.90 (P \u3c .0001). Bland-Altman plots showed that most of the errors for BP prediction were less than 10 mmHg.
Conclusions: With further development and validation, the proposed system could provide a cost-effective strategy to improve the quality and coverage of healthcare, particularly in rural zones, areas lacking physicians, and solitary elderly populations
Estimating blood pressure trends and the nocturnal dip from photoplethysmography
Objective: Evaluate a method for the estimation of the nocturnal systolic blood pressure dip from 24-hour blood pressure trends using a wrist-worn Photoplethysmography (PPG) sensor and a deep neural network in free-living individuals, comparing the deep neural network to traditional machine learning and non-machine learning baselines. Approach: A wrist-worn PPG sensor was worn by 106 healthy individuals for 226 days during which 5111 reference values for blood pressure were obtained with a 24-hour ambulatory blood pressure monitor as ground truth and matched with the PPG sensor data. Features based on heart rate variability and pulse morphology were extracted from the PPG waveforms. Machine learning models (linear regression, random forests, dense neural networks and long- and short-term memory neural networks) were then trained and evaluated in their capability of tracking trends in systolic and diastolic blood pressure, as well as the estimation of the nocturnal systolic blood pressure dip. Main results Best performance was obtained with a deep long- and shortterm memory neural network with a Root Mean Squared Error (RMSE) of 3.12±2.20 âmmHg and a correlation of 0.69 (p = 3 â 10â5) with the ground truth Systolic Blood Pressure (SBP) dip. This dip was derived from trend estimates of blood pressure which had an RMSE of 8.22±1.49 mmHg for systolic and 6.55±1.39 mmHg for diastolic blood pressure. The random forest model showed slightly lower average error magnitude for SBP trends (7.86±1.57 mmHg), however Bland-Altmann analysis revealed systematic problems in its predictions that were less present in the long- and short-term memory model. SigniïŹcance The work provides ïŹrst evidence for the unobtrusive estimation of the nocturnal blood pressure dip, a highly prognostic clinical parameter. It is also the ïŹrst to evaluate unobtrusive blood pressure measurement in a large data set of unconstrained 24-hour measurements in free-living individuals and provides evidence for the utility of long- and short-term models in this domain
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A review of machine learning techniques in photoplethysmography for the non-invasive cuff-less measurement of blood pressure
Hypertension or high blood pressure is a leading cause of death throughout the world and a critical factor for increasing the risk of serious diseases, including cardiovascular diseases such as stroke and heart failure. Blood pressure is a primary vital sign that must be monitored regularly for the early detection, prevention and treatment of cardiovascular diseases. Traditional blood pressure measurement techniques are either invasive or cuff-based, which are impractical, intermittent, and uncomfortable for patients. Over the past few decades, several indirect approaches using photoplethysmogram (PPG) have been investigated, namely, pulse transit time, pulse wave velocity, pulse arrival time and pulse wave analysis, in an effort to utilise PPG for estimating blood pressure. Recent advancements in signal processing techniques, including machine learning and artificial intelligence, have also opened up exciting new horizons for PPG-based cuff less and continuous monitoring of blood pressure. Such a device will have a significant and transformative impact in monitoring patientsâ vital signs, especially those at risk of cardiovascular disease. This paper provides a comprehensive review for non-invasive cuff-less blood pressure estimation using the PPG approach along with their challenges and limitations
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Deep tissue monitoring enabled by wearable ultrasonic devices and machine learning
Benefiting from the development of wearable electronic devices, various physiological signals, such as body temperature, hydration, glucose/lactate levels, and local field potentials, can already be monitored continuously and noninvasively. Among all physiological signals, those deeply beneath the skin, including central blood pressure, blood flow and activities of major organs, are particularly important since they are directly related to the subjectâs life-sustaining capability. However, there is a lack of devices that could give continuous and reliable readings of these vital signs. Their common limitations can be summarized as: limited penetration depth and operator dependence. Herein, we use human carotid artery as an example and demonstrate wearable ultrasonic devices supported by control electronics and adaptive algorithms to achieve automatic deep tissue monitoring. To eliminate the operator dependence of ultrasound technology, machine learning-based algorithms were developed addressing the blood vessel positioning and wall tracking tasks
Exploring remote photoplethysmography signals for deepfake detection in facial videos
Abstract. With the advent of deep learning-based facial forgeries, also called "deepfakes", the feld of accurately detecting forged videos has become a quickly growing area of research. For this endeavor, remote photoplethysmography, the process of extracting biological signals such as the blood volume pulse and heart rate from facial videos, offers an interesting avenue for detecting fake videos that appear utterly authentic to the human eye.
This thesis presents an end-to-end system for deepfake video classifcation using remote photoplethysmography. The minuscule facial pixel colour changes are used to extract the rPPG signal, from which various features are extracted and used to train an XGBoost classifer. The classifer is then tested using various colour-to-blood volume pulse methods (OMIT, POS, LGI and CHROM) and three feature extraction window lengths of two, four and eight seconds.
The classifer was found effective at detecting deepfake videos with an accuracy of 85 %, with minimal performance difference found between the window lengths. The GREEN channel signal was found to be important for this classifcationEtÀfotoplethysmografian hyödyntÀminen syvÀvÀÀrennösten tunnistamiseen. TiivistelmÀ. SyvÀvÀÀrennösten eli syvÀoppimiseen perustuvien kasvovÀÀrennöksien yleistyessÀ vÀÀrennösten tarkasta tunnistamisesta koneellisesti on tullut nopeasti kasvava tutkimusalue. EtÀfotoplethysmografa (rPPG) eli biologisten signaalien kuten veritilavuuspulssin tai sykkeen mittaaminen videokuvasta tarjoaa kiinnostavan keinon tunnistaa vÀÀrennöksiÀ, jotka vaikuttavat tÀysin aidoilta ihmissilmÀlle.
TÀssÀ diplomityössÀ esitellÀÀn etÀfotoplethysmografaan perustuva syvÀvÀÀrennösten tunnistusmetodi. Kasvojen minimaalisia vÀrimuutoksia hyvÀksikÀyttÀmÀllÀ mitataan fotoplethysmografasignaali, josta lasketuilla ominaisuuksilla koulutetaan XGBoost-luokittelija. Luokittelijaa testataan usealla eri vÀrisignaalista veritilavuussignaaliksi muuntavalla metodilla sekÀ kolmella eri ominaisuuksien ikkunapituudella.
Luokittelija pystyy tunnistamaan vÀÀrennetyn videon aidosta 85 % tarkkuudella. Eri ikkunapituuksien vÀlillÀ oli minimaalisia eroja, ja vihreÀn vÀrin signaalin havaittiin olevan luokittelun suorituskyvyn kannalta merkittÀvÀ
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