Continuous Blood Pressure Monitoring as a Basis for Ambient Assisted Living (AAL) – Review of Methodologies and Devices

Stojanova, Aleksandra; Koceski, Saso; Koceska, Nataša

doi:10.1007/s10916-018-1138-8

Cited by 34 publications

(27 citation statements)

References 76 publications

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“…Most PPG approaches to estimate BP use pulse transit time calculation, a feature extraction stage, and PPG signals reflected from finger vascular tone [ 12 , 14 , 17 ]. However, the nonlinearity of the blood volume-pressure relation indicates that the robust performance of these PPG-based BP monitors depends on specific sensing and biological parameters, such as signal recording of high quality, and individual physical property valuation.…”

Section: Discussionmentioning

confidence: 99%

“…This type of approach requires two sensors to calculate the time that a single heartbeat pulse takes to travel from the heart to a peripheral location or from one arterial site to another. Generally, an initial or periodic calibration step using an additional BP monitor as a reference is required to obtain a more reliable BP measurement [ 14 - 17 ]. Another simple and less obtrusive approach uses a single PPG sensor.…”

Section: Introductionmentioning

confidence: 99%

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Mobile Personal Health Care System for Noninvasive, Pervasive, and Continuous Blood Pressure Monitoring: Development and Usability Study

Mena¹,

Felix²,

Ostos³

et al. 2020

JMIR Mhealth Uhealth

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Background Smartphone-based blood pressure (BP) monitoring using photoplethysmography (PPG) technology has emerged as a promising approach to empower users with self-monitoring for effective diagnosis and control of hypertension. Objective This study aimed to develop a mobile personal health care system for noninvasive, pervasive, and continuous estimation of BP level and variability, which is user friendly for elderly people. Methods The proposed approach was integrated by a self-designed cuffless, calibration-free, wireless, and wearable PPG-only sensor and a native purposely designed smartphone app using multilayer perceptron machine learning techniques from raw signals. We performed a development and usability study with three older adults (mean age 61.3 years, SD 1.5 years; 66% women) to test the usability and accuracy of the smartphone-based BP monitor. Results The employed artificial neural network model had good average accuracy (>90%) and very strong correlation (>0.90) (P<.001) for predicting the reference BP values of our validation sample (n=150). Bland-Altman plots showed that most of the errors for BP prediction were less than 10 mmHg. However, according to the Association for the Advancement of Medical Instrumentation and British Hypertension Society standards, only diastolic blood pressure prediction met the clinically accepted accuracy thresholds. Conclusions With further development and validation, the proposed system could provide a cost-effective strategy to improve the quality and coverage of health care, particularly in rural zones, areas lacking physicians, and areas with solitary elderly populations.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mobile Personal Health Care System for Noninvasive, Pervasive, and Continuous Blood Pressure Monitoring: Development and Usability Study

Mena¹,

Felix²,

Ostos³

et al. 2020

JMIR Mhealth Uhealth

View full text Add to dashboard Cite

show abstract

“…Oscillometry is another conventional and noninvasive method that requires a cuff [6], [7]. Tonometry is a clinical method for continuous BP monitoring, which obtain the arterial waveform using sensors located on the artery sites [8]- [10]. Volume clamping uses a photoplethysmogram (PPG) signal and an inflatable cuff to measure the BP [11].…”

Section: Introductionmentioning

confidence: 99%

“…Previous cuff-less and PTT-based studies are typically classified into three categories [4], [10]: 1) adopting only an electrocardiogram (ECG) signal [26], [27], 2) adopting both ECG and PPG signals [28]- [32], and 3) adopting only a PPG signal [22], [23], [33]. The research adopting the approaches of 1) and 2) can be difficult to apply in daily life, since the ECG signal is derived from sensors attached to the body.…”

Section: Introductionmentioning

confidence: 99%

Cuff-Less Blood Pressure Monitoring System Using Smartphones

et al. 2020

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Recently, smartphones with mobile health applications have become promising tools in the healthcare industry due to their convenience, ubiquity for patients, and the ability to gather data in real time. In this paper, we propose a novel non-invasive, portable, and cuff-less method for monitoring BP by only using the smartphones' camera. Our experiment uses pulse transit time (PTT) between two separate photoplethysmogram (PPG) signals to estimate the subjects' systolic blood pressure (SBP) and diastolic blood pressure (DBP). Our proposed method first measures the subject's PPG signals from his/her index fingers using the smartphones' camera. Then, filtering and peak detection algorithms of the proposed method reduce the motion and noise artifacts in the PPG signals. Finally, the proposed method estimates SBP and DBP based on a linear regression model which was trained and tested on 30 trials with six healthy subjects. We evaluated the proposed method by comparing BP values of the proposed method with those of the reference (or gold-standard) device in terms of mean absolute error (MAE), standard deviation of error (SD), and R-squared (R 2) value of the cross-validation. Experimental results show that the proposed method estimates the average of MAE ± SD is 2.07 ± 2.06 mm Hg for SBP estimation, and 2.12 ± 1.85 mm Hg for DBP estimation. These estimates are lower than accurate BP estimation standard (5 ± 8 mmHg). INDEX TERMS Hypertension, blood pressure, cuff-less, smartphone, photoplethysmogram (PPG), pulse transit time (PTT).

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“…Hypertension is one of the most significant risk factors for cardiovascular diseases [1]. The outcome of a BP measurement consists of three parameters, namely the diastolic blood pressure (DBP), systolic blood pressure (SBP), and mean arterial pressure (MAP), in millimeters of mercury (mmHg) [2][3][4]. There are two categories of methods for determining BP: invasive and noninvasive methods.…”

Section: Introductionmentioning

confidence: 99%

Noninvasive Blood Pressure Classification Based on Photoplethysmography Using K-Nearest Neighbors Algorithm: A Feasibility Study

Tjahjadi¹,

Ramli²

2020

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Blood pressure (BP) is an important parameter for the early detection of heart disease because it is associated with symptoms of hypertension or hypotension. A single photoplethysmography (PPG) method for the classification of BP can automatically analyze BP symptoms. Users can immediately know the condition of their BP to ensure early detection. In recent years, deep learning methods have presented outstanding performance in classification applications. However, there are two main problems in deep learning classification methods: classification accuracy and time consumption during training. We attempt to address these limitations and propose a method for the classification of BP using the K-nearest neighbors (KNN) algorithm based on PPG. We collected data for 121 subjects from the PPG-BP figshare database. We divided the subjects into three classification levels, namely normotension, prehypertension, and hypertension, according to the BP levels of the Joint National Committee report. The F1 scores of these three classification trials were 100%, 100%, and 90.80%, respectively. Hence, it is validated that the proposed method can achieve improved classification accuracy without additional manual pre-processing of PPG. Our proposed method achieves higher accuracy than convolutional neural networks (deep learning), bagged tree, logistic regression, and AdaBoost tree.Information 2020, 11, 93 2 of 18 blood volume changes, which occur between the systolic and diastolic phases of the cardiac cycle. The essential frequency of the AC component depends on the heart rate and is covered onto the DC component [11]. An LED is a light source that can be used to illuminate blood vessels so minor perfusion changes can be supervised on the photodetectors [12]. Perfusion is measured as the degree at which blood is distributed to tissue [13].

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Continuous Blood Pressure Monitoring as a Basis for Ambient Assisted Living (AAL) – Review of Methodologies and Devices

Cited by 34 publications

References 76 publications

Mobile Personal Health Care System for Noninvasive, Pervasive, and Continuous Blood Pressure Monitoring: Development and Usability Study

Mobile Personal Health Care System for Noninvasive, Pervasive, and Continuous Blood Pressure Monitoring: Development and Usability Study

Cuff-Less Blood Pressure Monitoring System Using Smartphones

Noninvasive Blood Pressure Classification Based on Photoplethysmography Using K-Nearest Neighbors Algorithm: A Feasibility Study

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