An integrated blood pressure measurement system for suppression of motion artifacts

Abderahman, Huthaifa N.; Dajani, Hilmi R.; Bolić, Miodrag; Groza, Voicu

doi:10.1016/j.cmpb.2017.03.007

Cited by 8 publications

(2 citation statements)

References 22 publications

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“…Motion artifacts are sources of inaccuracy, and they can be produced by arm motion, muscle tremor, shaking, or any external vibration. [ 12 ] The proposed method uses a lower number of deflation steps than similar equipment to decrease the time of measurement. In addition, it does not take into account all the available detected pulses, and some of them are removed to increase accuracy.…”

Section: Methodsmentioning

confidence: 99%

Implementation and validation of a novel noninvasive blood pressure measurement algorithm based on oscillometric method with obtaining international hypertension standards

2022

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Section: Methodsmentioning

confidence: 99%

Implementation and validation of a novel noninvasive blood pressure measurement algorithm based on oscillometric method with obtaining international hypertension standards

2022

View full text Add to dashboard Cite

“…Wavelet decomposition [106] and the median filter [107,108] are used as base correction algorithms. As the ECG and PPG signals are measured, sampling frequency equalization is performed if the sampling frequencies are different.…”

Section: Preprocessingmentioning

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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Machine learning and deep learning for blood pressure prediction: a methodological review from multiple perspectives

Qin¹,

Huang

Zhang³

et al. 2022

Artif Intell Rev

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An integrated blood pressure measurement system for suppression of motion artifacts

Cited by 8 publications

References 22 publications

Implementation and validation of a novel noninvasive blood pressure measurement algorithm based on oscillometric method with obtaining international hypertension standards

Implementation and validation of a novel noninvasive blood pressure measurement algorithm based on oscillometric method with obtaining international hypertension standards

Towards wearable blood pressure measurement systems from biosignals: a review

Machine learning and deep learning for blood pressure prediction: a methodological review from multiple perspectives

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