Improving Cuff-Less Continuous Blood Pressure Estimation with Linear Regression Analysis

Figini, Valeria; Galici, Sofia; Russo, Daniele; Centonze, Ilenia; Visintin, Monica; Pagana, Guido

doi:10.3390/electronics11091442

Cited by 13 publications

(7 citation statements)

References 44 publications

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“…In 2022, Natarajan et al 101 integrated PTT features with PPG data extracted from the finger, ear, and toe to train LR models and eliminated errors in BP prediction. In 2022, Figini et al 102 used PTT and heart rate as features to train several regression algorithms, including LR, for predicting SBP and DBP. In 2021, Mamun 103 applied a penalty-based regression outperforming the conventional LR in both SBP and DBP prediction in terms of the mean absolute error (MAE).…”

Section: Beat-to-beat Bp Estimation Modelsmentioning

confidence: 99%

Emerging sensing and modeling technologies for wearable and cuffless blood pressure monitoring

et al. 2023

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Cardiovascular diseases (CVDs) are a leading cause of death worldwide. For early diagnosis, intervention and management of CVDs, it is highly desirable to frequently monitor blood pressure (BP), a vital sign closely related to CVDs, during people’s daily life, including sleep time. Towards this end, wearable and cuffless BP extraction methods have been extensively researched in recent years as part of the mobile healthcare initiative. This review focuses on the enabling technologies for wearable and cuffless BP monitoring platforms, covering both the emerging flexible sensor designs and BP extraction algorithms. Based on the signal type, the sensing devices are classified into electrical, optical, and mechanical sensors, and the state-of-the-art material choices, fabrication methods, and performances of each type of sensor are briefly reviewed. In the model part of the review, contemporary algorithmic BP estimation methods for beat-to-beat BP measurements and continuous BP waveform extraction are introduced. Mainstream approaches, such as pulse transit time-based analytical models and machine learning methods, are compared in terms of their input modalities, features, implementation algorithms, and performances. The review sheds light on the interdisciplinary research opportunities to combine the latest innovations in the sensor and signal processing research fields to achieve a new generation of cuffless BP measurement devices with improved wearability, reliability, and accuracy.

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Section: Beat-to-beat Bp Estimation Modelsmentioning

confidence: 99%

Emerging sensing and modeling technologies for wearable and cuffless blood pressure monitoring

et al. 2023

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“…The relationship between blood pressure and HR has been studied by many researchers, and integrating heart rate as an additional parameter when finding BP using PTT improves accuracy [29][30][31].…”

Section: Measurement Of Pulse Transit Timementioning

confidence: 99%

A Novel Contactless Blood Pressure Measurement System and Algorithm Based on Vision Intelligence

Khomidov,

Lee,

Lee

2023

Electronics

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The measurement of vital signs such as blood pressure plays a key role in human health. Usually, we encounter some problems when we check them in the traditional way; for example, it is impossible to check continuously, and measuring vital signs requires direct contact with the patient, which can be uncomfortable for certain individuals. In this research, we present a vision-based system for estimating blood pressure using pulse transit time (PTT) and the Eulerian video magnification (EVM) technique to amplify tiny color variations caused by blood flow to calculate arterial pulse waves traveling between two arterial sites. Calculating the PTT by processing the video signal for each subject, an oscillometer BP device was used to evaluate the performance between measurements in different conditions, including rest, exercise, and during recovery. Mean systolic BP was 115 mmHg at rest, 137 mmHg during high-intensity exercise, and 114 mmHg during recovery, respectively. The average value of diastolic blood pressure did not change significantly before, during, and after exercise. When we compared the systolic and diastolic blood pressure with ground-truth results, our system showed an accuracy of 91% for systolic blood pressure and 90% for diastolic blood pressure.

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“…Eq. (2) serves as the linear regression model employed in this study [15], [16]. To estimate maximum and minimum BP values, the assessment of Arterial Blood Pressure (ABP) is divided into Diastolic Blood Pressure (DBP) and Systolic Blood Pressure (SBP):…”

Section: Estimation Of Blood Pressure Using Bramwell-hills and Moens-...mentioning

confidence: 99%

Optimized Cuffless Blood Pressure Measurement Using ECG, PPG and Linear Regression

Boschi,

Figini,

Pagana

et al. 2023

Preprint

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This study investigates the suitability of the timestamp approach algorithm for predicting blood pressure (BP) using wearable and comfortable devices, showcasing its accuracy even with low-frequency sampled signals. Developed within the SINTEC European project, the algorithm utilizes Electrocardiogram (ECG) and Photoplethysmogram (PPG) sensors, demonstrating promising results in both laboratory and hospital settings. The research emphasizes the algorithm’s potential to transform continuous and non-invasive BP monitoring through wearable technology, offering frequent measurements with minimal discomfort. While the study shows promise, further exploration is needed, focusing on optimizing machine learning coefficients for long-term monitoring applications and addressing potential changes over time. Technological improvements, including the implementation of edge computing algorithms, hold the key to enhancing BP accuracy, denoising techniques, and integrating alarm systems for future wearable solutions. This study emphasizes the adaptability of the algorithm for diverse healthcare environments, paving the way for broader applications in convenient and reliable BP monitoring.

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Improving Cuff-Less Continuous Blood Pressure Estimation with Linear Regression Analysis

Cited by 13 publications

References 44 publications

Emerging sensing and modeling technologies for wearable and cuffless blood pressure monitoring

Emerging sensing and modeling technologies for wearable and cuffless blood pressure monitoring

A Novel Contactless Blood Pressure Measurement System and Algorithm Based on Vision Intelligence

Optimized Cuffless Blood Pressure Measurement Using ECG, PPG and Linear Regression

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