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Continuous Blood Pr...
Continuous Blood Pressure Estimation From Non-Invasive Measurements Using Support Vector Regression
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- Rastegar, Solmaz (författare)
- Auckland Univ Technol, Elect & Elect Engn Dept, Auckland 1010, New Zealand.
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- GholamHosseini, Hamid (författare)
- Auckland Univ Technol, Elect & Elect Engn Dept, Auckland 1010, New Zealand.
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- Lowe, Andrew (författare)
- Auckland Univ Technol, Inst Biomed Technol, Auckland 1010, New Zealand.
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- Lindén, Maria, 1965- (författare)
- Mälardalens universitet,Inbyggda system
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Auckland Univ Technol, Elect & Elect Engn Dept, Auckland 1010, New Zealand Auckland Univ Technol, Inst Biomed Technol, Auckland 1010, New Zealand. (creator_code:org_t)
- IEEE, 2021
- 2021
- Engelska.
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Ingår i: 2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC). - : IEEE. - 9781728111797 ; , s. 1487-1490
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
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- Blood pressure (BP) is one of the most crucial vital signs of the human body that can be assessed as a critical risk factor for severe health conditions such as cardiovascular diseases (CVD) and hypertension. An accurate, continuous, and cuff-less BP monitoring technique could help clinicians improve the prevention, detection, and diagnosis of hypertension and manage related treatment plans. Notably, the complex and dynamic nature of the cardiovascular system necessitates that any BP monitoring system could benefit from an intelligent technology that can extract and analyze compelling BP features. In this study, a support vector regression (SVR) model was developed to estimate systolic blood pressure (SBP) and diastolic blood pressure (DBP) continuously. We selected a set of features commonly used in previous studies to train the proposed SVR model. A total of 120 patients with available ECG, PPG, DBP and SBP data were chosen from the Medical Information Mart for Intensive Care (MIMIC III) dataset to validate the proposed model. The results showed that the average root mean square error (RMSE) of 2.37 mmHg and 4.18 mmHg were achieved for SBP and DBP, respectively.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine (hsv//eng)
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