Non-Invasive Blood Pressure Estimation from ECG Using Machine Learning Techniques

Simjanoska, Monika; Gjoreski, Martin; Gams, Matjaž; Bogdanova, Ana Madevska

doi:10.3390/s18041160

Cited by 135 publications

(77 citation statements)

References 72 publications

(73 reference statements)

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“…Apart from the PTT-based BP estimation method, some other physiological features have been investigated to indicate BP changes, including PPG intensity ratio [109], Womersley number [110], radial electrical bioimpedance [111], modified normalized pulse volume [112], acceleration plethysmography (APG) [113], and diameter of a pulsating blood vessel [114]. Additionally, machine learning has also been applied to BP estimation to develop regression models between signal features and BP, and demonstrating promising estimation accuracy [115][116][117][118][119][120]. However, the interpretation of the datadriven model is nontrivial.…”

Section: B Continuous Blood Pressure Monitoringmentioning

confidence: 99%

Wearable Sensing and Telehealth Technology with Potential Applications in the Coronavirus Pandemic

Ding

Clifton

et al. 2021

IEEE Rev. Biomed. Eng.

204

155

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Coronavirus disease 2019 (COVID-19) has emerged as a pandemic with serious clinical manifestations including death.A pandemic at the large-scale like COVID-19 places extraordinary demands on the world's health systems, dramatically devastates vulnerable populations, and critically threatens the global communities in an unprecedented way. While tremendous efforts at the frontline are placed on detecting the virus, providing treatments and developing vaccines, it is also critically important to examine the technologies and systems for tackling disease emergence, arresting its spread and especially the strategy for diseases prevention. The objective of this article is to review enabling technologies and systems with various application scenarios for handling the COVID-19 crisis. The article will focus specifically on 1) wearable devices suitable for monitoring the populations at risk and those in quarantine, both for evaluating the health status of caregivers and management personnel, and for facilitating triage processes for admission to hospitals; 2) unobtrusive sensing systems for detecting the disease and for monitoring patients with relatively mild symptoms whose clinical situation could suddenly worsen in improvised hospitals; and 3) telehealth technologies for the remote monitoring and diagnosis of COVID-19 and related diseases. Finally, further challenges and opportunities for future directions of development are highlighted.

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Section: B Continuous Blood Pressure Monitoringmentioning

confidence: 99%

Wearable Sensing and Telehealth Technology with Potential Applications in the Coronavirus Pandemic

Ding

Clifton

et al. 2021

IEEE Rev. Biomed. Eng.

204

155

View full text Add to dashboard Cite

show abstract

“…Novel technologies have been developed that utilise ML techniques to measure heart rate and respiratory rate using only video, without any patient contact . ML has also been applied to cuff‐less BP monitoring using a single arm band, and to accurately predict BP using ECG recording …”

Section: Clinical Monitoringmentioning

confidence: 99%

“…28 ML has also been applied to cuff-less BP monitoring using a single arm band, and to accurately predict BP using ECG recording. 29,30 Clinical outcome predictions AI algorithms are becoming better at predicting the future, often outperforming current clinical scoring systems. Using raw data obtained from over 200 000 patients entire electronic medical records (over 46 billion data points), Rajkomar and Oren et al were able to predict in-hospital mortality with an area under the receiver operating characteristic curve of 0.93-0.94, validated across two sites.…”

Section: Clinical Monitoringmentioning

confidence: 99%

Artificial intelligence and machine learning in emergency medicine

Stewart

Sprivulis

Dwivedi

2018

Emerg Medicine Australasia

113

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Interest in artificial intelligence (AI) research has grown rapidly over the past few years, in part thanks to the numerous successes of modern machine learning techniques such as deep learning, the availability of large datasets and improvements in computing power. AI is proving to be increasingly applicable to healthcare and there is a growing list of tasks where algorithms have matched or surpassed physician performance. Despite the successes there remain significant concerns and challenges surrounding algorithm opacity, trust and patient data security. Notwithstanding these challenges, AI technologies will likely become increasingly integrated into emergency medicine in the coming years. This perspective presents an overview of current AI research relevant to emergency medicine.

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“…Other studies have shown that the ECG signal from Faros can also be used to calculate other variables such as BP [66], or to extract core body temperature with respect to clothing and persons' activity [67].…”

Section: Introductionmentioning

confidence: 99%

Assessing Drivers’ Physiological Responses Using Consumer Grade Devices

et al. 2019

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The ability to measure drivers' physiological responses is important for understanding their state and behavior under different driving conditions. Such measurements can be used in the development of novel user interfaces, driver profiling, advanced driver assistance systems, etc. In this paper, we present a user study in which we performed an evaluation of two commercially available wearable devices for assessment of drivers' physiological signals. Empatica's E4 wristband measures blood volume pulse (BVP), inter-beat interval (IBI), galvanic skin response (GSR), temperature, and acceleration. Bittium's Faros 360 is an electrocardiographic (ECG) device that can record up to 3-channel ECG signals. The aim of this study was to explore the use of such devices in a dynamic driving environment and their ability to differentiate between different levels of driving demand. Twenty-two participants (eight female, 14 male) aged between 18 and 45 years old participated in the study. The experiment compared three phases: Baseline (no driving), easy driving scenario, and demanding driving scenario. Mean and median heart rate variability (HRV), standard deviation of R-R intervals (SDNN), HRV variables for shorter time frames (standard deviation of the average R-R intervals over a shorter period-SDANN and mean value of the standard deviations calculated over a shorter period-SDNN index), HRV variables based on successive differences (root mean square of successive differences-RMSSD and percentage of successive differences, greater than 50 ms-pNN50), skin temperature, and GSR were observed in each phase. The results showed that motion artefacts due to driving affect the GSR recordings, which may limit the use of wrist-based wearable devices in a driving environment. In this case, due to the limitations of the photoplethysmography (PPG) sensor, E4 only showed differences between non-driving and driving phases but could not differentiate between different levels of driving demand. On the other hand, the results obtained from the ECG signals from Faros 360 showed statistically significant differences also between the two levels of driving demand.

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Non-Invasive Blood Pressure Estimation from ECG Using Machine Learning Techniques

Cited by 135 publications

References 72 publications

Wearable Sensing and Telehealth Technology with Potential Applications in the Coronavirus Pandemic

Wearable Sensing and Telehealth Technology with Potential Applications in the Coronavirus Pandemic

Artificial intelligence and machine learning in emergency medicine

Assessing Drivers’ Physiological Responses Using Consumer Grade Devices

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