Cuffless Blood Pressure Estimation Algorithms for Continuous Health-Care Monitoring

Kachuee, Mohammad; Kiani, Mohammad Mahdi; Mohammadzade, Hoda; Shabany, Mahdi

doi:10.1109/tbme.2016.2580904

Cited by 443 publications

(339 citation statements)

References 31 publications

(33 reference statements)

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“…In their work, various machine learning-based techniques were compared and they found that non-linear algorithms, like kernel machines or ensemble-learning methods as well as the AdaBoost model, gave better performances than linear approximations. Other techniques such as linear regression and decision tree were found not appropriate in predicting BP [114]. …”

Section: Comparison Between Modelsmentioning

confidence: 97%

“…PIR was found mainly dependent on the arterial diameter and correlated with DBP [90,113]. Recently, machine learning-based techniques have been investigated [114] and promising results have been reported. In their work, various machine learning-based techniques were compared and they found that non-linear algorithms, like kernel machines or ensemble-learning methods as well as the AdaBoost model, gave better performances than linear approximations.…”

Section: Comparison Between Modelsmentioning

confidence: 99%

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Cuff-Less and Continuous Blood Pressure Monitoring: A Methodological Review

et al. 2017

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Blood pressure (BP) is one of the most important monitoring parameters in clinical medicine. For years, the cuff-based sphygmomanometer and the arterial invasive line have been the gold standards for care professionals to assess BP. During the past few decades, the wide spread of the oscillometry-based BP arm or wrist cuffs have made home-based BP assessment more convenient and accessible. However, the discontinuous nature, the inability to interface with mobile applications, the relative inaccuracy with movement, and the need for calibration have rendered those BP oscillometry devices inadequate for next-generation healthcare infrastructure where integration and continuous data acquisition and communication are required. Recently, the indirect approach to obtain BP values has been intensively investigated, where BP is mathematically derived through the "Time Delay" in propagation of pressure waves in the vascular system. This holds promise for the realization of cuffless and continuous BP monitoring systems, for both patients and healthy populations in both inpatient and outpatient settings. This review highlights recent efforts in developing these next-generation blood pressure monitoring devices and compares various mathematical models. The unmet challenges and further developments that are crucial to develop "Time Delay"-based BP devices are also discussed.

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Section: Comparison Between Modelsmentioning

confidence: 97%

Section: Comparison Between Modelsmentioning

confidence: 99%

Cuff-Less and Continuous Blood Pressure Monitoring: A Methodological Review

et al. 2017

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“…We calculated a single target score (the aggregated value using a weighted average of the scores for each signal portion where a score for each signal portion is the median of the top 5% of the highest EMG amplitudes) from these scores as the outcome of each experiment. The simplest approach would have been to average these values [20]; however, many scores were contaminated by noise and some could have been limited by subject effort. Accordingly, using a simple average or median of scores may not be an accurate measure of the real expected target values.…”

Section: Methodsmentioning

confidence: 99%

Predicting the effects of epidural stimulation to improve hand function in patients with spinal cord injury: An active learning-based solution using dynamic sample weighting

Kachuee

Hoffman

Moore

et al. 2018

Preprint

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In patients with chronic spinal cord injury (SCI), few therapies are available to improve neurological function. Neuromodulation of the spinal cord with epidural stimulation (EDS) has shown promise enabling the voluntary activation of motor pools caudal to the level of the injury. EDS is performed with multiple electrode arrays in which several stimulation variables such as the frequency, amplitude, and location of the stimulation significantly affect the type and amplitude of motor responses. This paper presents a novel technique to predict the final functionality of a patient with SCI after cervical EDS within a deep learning framework. Additionally, we suggest a committee-based active learning method to reduce the number of clinical experiments required to optimize EDS stimulation variables by exploring the stimulation configuration space more efficiently. We also developed a novel method to dynamically weight the results of different experiments using neural networks to create an optimal estimate of the quantity of interest. The essence of our approach was to use machine learning methods to predict the hand contraction force in a patient with chronic SCI based on different EDS parameters. The accuracy of the prediction of stimulation outcomes was evaluated based on three measurements: mean absolute error, standard deviation, and correlation coefficient. The results show that the proposed method can be used to reliably predict the outcome of cervical EDS on maximum voluntary contraction force of the hand with a prediction error of approximately 15%. This model could allow scientists to establish stimulation parameters more efficiently for SCI patients to produce enhanced motor responses in this novel application.Author SummarySpinal cord injury (SCI) can lead to permanent sensorimotor deficits that have a major impact on quality of life. In patients with a motor complete injury, there is no therapy available to reliably improve motor function. Recently, neuromodulation of the spinal cord with epidural stimulation (EDS) has allowed patients with motor-complete SCI regain voluntary movement below the level of injury in the cervical and thoracic spine. EDS is performed using multi-electrode arrays placed in the dorsal epidural space spanning several spinal segments. There are numerous stimulation parameters that can be modified to produce different effects on motor function. Previously, defining these parameters was based on observation and empiric testing, which are time-consuming and inefficient processes. There is a need for an automated method to predict motor and sensory function based on a given combination of EDS settings. We developed a novel method to predict the gripping function of a patient with SCI undergoing cervical EDS based on a set of stimulation parameters within a deep learning framework. We also addressed a limiting factor in machine learning methods in EDS, which is a general lack of training measurements for the learning model. We proposed a novel active learning method to minimize the number of training measurements required. The model for predicting responses to EDS could be used by scientists and clinicians to efficiently determine a set of stimulation parameters that produce a desired effect on motor function.

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“…In machine learning methods, it is provided to produce output values for different inputs by training the corresponding output values of certain input values. Models such as random forest [115,116], regression tree [117], support vector machines [118], K-nearest neighbors, and deep learning [119] are used in the analysis of blood pressure measurements. There is a standard of IEEE on wearable blood pressure gauges [120].…”

Section: Prediction Algorithms Used For Predicting Blood Pressure Usimentioning

confidence: 99%

Towards wearable blood pressure measurement systems from biosignals: a review

Şentürk¹,

Polat²,

Yücedağ³

2019

Turk J Elec Eng & Comp Sci

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Blood pressure is the pressure by the blood to the vein wall. High blood pressure, which is called silent death, is the cause of nearly 13% of mortality all over the world. Blood pressure is not only measured in the medical environment, but the blood pressure measurement is also a need for people in their daily life. Blood pressure estimation systems with low error rates have been developed besides the new technologies and algorithms. Blood pressure measurements are differentiated as invasive blood pressure (IBP) measurement and noninvasive blood pressure (NIBP) measurement methods. Although IBP measurement provides the most accurate results, it cannot be used in daily life because it can only be performed by qualified medical staff with specialized medical equipment. NIBP measurement is based on measuring physiological signals taken from the body and producing results with decision mechanisms. Oscillometric, pulse transit time (PTT), pulse wave velocity, and feature extraction methods are mentioned in the literature as NIBP.In the oscillometric method of the sphygmomanometer, an electrocardiogram is used in PTT methods as a result of the comparison of signals such as electrocardiography, photoplethysmography, ballistocardiography, and seismocardiography.The increase in the human population and worldwide deaths due to the highly elevated blood pressure makes the need for precise measurements and technological devices more clear. Today, wearable technologies and sensors have been frequently used in the health sector. In this review article, the invasive and noninvasive blood pressure methods, including various biosignals, have been investigated and then compared with each other concerning the measurement of comfort and robust estimation.

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Cuffless Blood Pressure Estimation Algorithms for Continuous Health-Care Monitoring

Abstract: The results indicate that the proposed algorithm for the cuffless estimation of the BP can potentially enable mobile health-care gadgets to monitor the BP continuously.

Cited by 443 publications

References 31 publications

Cuff-Less and Continuous Blood Pressure Monitoring: A Methodological Review

Cuff-Less and Continuous Blood Pressure Monitoring: A Methodological Review

Predicting the effects of epidural stimulation to improve hand function in patients with spinal cord injury: An active learning-based solution using dynamic sample weighting

Towards wearable blood pressure measurement systems from biosignals: a review

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