Non-invasive Calibration-Free Blood Pressure Estimation Based on Artificial Neural Network

Maher, Nashat; El-Sheikh, G. A.; Anis, Wagdy R.; Emara, Tamer

doi:10.1007/978-3-030-14118-9_69

Cited by 8 publications

(10 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed results of MAE and STD are 4.77 mmHg and 6.06 mmHg for SP, respectively. These results are better than the literature [ 19 , 21 ] as the MAE and STD of the DP are 4.8 and 6.56 mmHg which meet international organization for standardization (ISO). Number of samples which are used in this work is more than two thousand and the result of regression model is shown in Fig.…”

Section: Results and Dissuctioncontrasting

confidence: 51%

“…Table 2 introduces the literature and the obtained results of the proposed model, where the estimation of BP based on the pulse transit time (PTT) is developed by M. Kachuee [ 21 ] and N.Maher [ 19 ] using machine learning techniques. The proposed results of MAE and STD are 4.77 mmHg and 6.06 mmHg for SP, respectively.…”

Section: Results and Dissuctionmentioning

confidence: 99%

Section: The Medical Robot Structure and Featuresmentioning

confidence: 99%

“…The design and development of PPG measuring device is presented in [ 19 ]. Many of vital signs can be estimated by PPG sensor, such as the respiratory rate [ 20 ], Blood Pressure [ 19 , 21 , 22 ], heart rate and body temperature [ 23 ]. These researches' result should be used in order to minimize the robot size and its measuring tools and consequently reduce complexity and cost.…”

Section: The Medical Robot Structure and Featuresmentioning

confidence: 99%

See 3 more Smart Citations

Design and Implementation of a Wireless Medical Robot for Communication Within Hazardous Environments

Maher

El-Sheikh²,

Ouda³

et al. 2021

Wireless Pers Commun

Self Cite

View full text Add to dashboard Cite

The huge spreading of COVID-19 viral outbreak to several countries motivates many of the research institutions everywhere in numerous disciplines to try decreasing the spread rate of this pandemic. Among these researches are the robotics with different payloads and sensory devices with wireless communications to remotely track patients’ diagnosis and their treatment. That is, it reduces direct contact between the patients and the medical team members. Thus, this paper is devoted to design and implement a prototype of wireless medical robot (MR) that can communicate between patients and medical consultants. The prototype includes the modelling of a four-wheeled MR using systems' identification methodology, from which the model is utilized in control design and analysis. The required controller is designed using the proportional-integral-derivative (PID) and Fuzzy logic (FLC) techniques. The MR is equipped onboard with some medical sensors and a camera to acquire vital signs and physical parameters of patients. The MR model is obtained via an experimental test with input/output signals in open-loop configuration as single–input–single–output from which the estimation and validation results demonstrate that the identified model possess about 89% of the output variation/dynamics. This model is used for controllers' design with PID and FLC, the response of which is good for heading angle tracking. Concerning the medical measurements, more than two thousand real recorded Photo-plethysmography (PPG) signals and Blood Pressure (BP) are used to find the appropriate BP estimation model. Towards this objective, some experiments are designed and conducted to measure the PPG signal. Finally, the BP is estimated with mean absolute error of about 4.7 mmHg in systolic and 4.8 mmHg in diastolic using Artificial Neural Network.

show abstract

Section: Results and Dissuctioncontrasting

confidence: 51%

Section: Results and Dissuctionmentioning

confidence: 99%

Section: The Medical Robot Structure and Featuresmentioning

confidence: 99%

Section: The Medical Robot Structure and Featuresmentioning

confidence: 99%

See 2 more Smart Citations

Design and Implementation of a Wireless Medical Robot for Communication Within Hazardous Environments

Maher

El-Sheikh²,

Ouda³

et al. 2021

Wireless Pers Commun

Self Cite

View full text Add to dashboard Cite

show abstract

“…With the development of machine learning, the characteristic parameters of models were further enriched, including the amplitude, phase characteristics of pulse waves extracted with fast Fourier transform [23], spectral characteristics [24], and the features of the photoplethysmography (PPG) waveform and related first and second (time) derivatives [25,26]. Moreover, the model construction methods were expanded, such as neural network [24,[27][28][29], support vector machine [30], adaptive boosting regression [31], and random forest algorithm [32]. The blood-pressure estimation methods based on machine learning and big data covered more blood-pressure information and improved the estimation accuracies of the models to some extent.…”

Section: Introductionmentioning

confidence: 99%

Non-Invasive Continuous Blood-Pressure Monitoring Models Based on Photoplethysmography and Electrocardiography

2019

Sensors

View full text Add to dashboard Cite

Blood pressure is an extremely important blood hemodynamic parameter. The pulse wave contains abundant blood-pressure information, and the convenience and non-invasivity of its measurement make it ideal for non-invasive continuous monitoring of blood pressure. Based on combined photoplethysmography and electrocardiogram signals, this study aimed to extract the waveform information, introduce individual characteristics, and construct systolic and diastolic blood-pressure (SBP and DBP) estimation models using the back-propagation error (BP) neural network. During the model construction process, the mean impact value method was employed to investigate the impact of each feature on the model output and reduce feature redundancy. Moreover, the multiple population genetic algorithm was applied to optimize the BP neural network and determine the initial weights and threshold of the network. Finally, the models were integrated for further optimization to generate the final individualized continuous blood-pressure monitoring models. The results showed that the predicted values of the model in this study correlated significantly with the measured values of the electronic sphygmomanometer. The estimation errors of the model met the Association for the Advancement of Medical Instrumentation (AAMI) criteria (the SBP error was 2.5909 ± 3.4148 mmHg, and the DBP error was 2.6890 ± 3.3117 mmHg) and the Grade A British Hypertension Society criteria.

show abstract

Blood pressure estimation and its recalibration assessment using wrist cuff blood pressure monitor

et al. 2023

View full text Add to dashboard Cite

Non-invasive Calibration-Free Blood Pressure Estimation Based on Artificial Neural Network

Cited by 8 publications

References 10 publications

Design and Implementation of a Wireless Medical Robot for Communication Within Hazardous Environments

Design and Implementation of a Wireless Medical Robot for Communication Within Hazardous Environments

Non-Invasive Continuous Blood-Pressure Monitoring Models Based on Photoplethysmography and Electrocardiography

Blood pressure estimation and its recalibration assessment using wrist cuff blood pressure monitor

Contact Info

Product

Resources

About