scholarly journals Smartphones and Video Cameras: Future Methods for Blood Pressure Measurement

2021 ◽  
Vol 3 ◽  
Author(s):  
Joe Steinman ◽  
Andrew Barszczyk ◽  
Hong-Shuo Sun ◽  
Kang Lee ◽  
Zhong-Ping Feng
Keyword(s):  
Blood Pressure ◽  
Cardiovascular Disease ◽  
Cardiovascular Health ◽  
Blood Pressure Measurement ◽  
Video Camera ◽  
Machine Learning Techniques ◽  
Video Cameras ◽  
Lighting Conditions ◽  
Home Based ◽  
Learning Techniques

Regular blood pressure (BP) monitoring enables earlier detection of hypertension and reduces cardiovascular disease. Cuff-based BP measurements require equipment that is inconvenient for some individuals and deters regular home-based monitoring. Since smartphones contain sensors such as video cameras that detect arterial pulsations, they could also be used to assess cardiovascular health. Researchers have developed a variety of image processing and machine learning techniques for predicting BP via smartphone or video camera. This review highlights research behind smartphone and video camera methods for measuring BP. These methods may in future be used at home or in clinics, but must be tested over a larger range of BP and lighting conditions. The review concludes with a discussion of the advantages of the various techniques, their potential clinical applications, and future directions and challenges. Video cameras may potentially measure multiple cardiovascular metrics including and beyond BP, reducing the risk of cardiovascular disease.

scholarly journals A Comparison of Deep Learning Techniques for Arterial Blood Pressure Prediction

2021 ◽  
Author(s):  
Annunziata Paviglianiti ◽  
Vincenzo Randazzo ◽  
Stefano Villata ◽  
Giansalvo Cirrincione ◽  
Eros Pasero
Keyword(s):  
Blood Pressure ◽  
Neural Networks ◽  
Deep Learning ◽  
Arterial Blood Pressure ◽  
Blood Pressure Measurement ◽  
National Standards ◽  
Detection Methods ◽  
Physiological Parameter ◽  
Arterial Blood ◽  
Learning Techniques

AbstractContinuous vital signal monitoring is becoming more relevant in preventing diseases that afflict a large part of the world’s population; for this reason, healthcare equipment should be easy to wear and simple to use. Non-intrusive and non-invasive detection methods are a basic requirement for wearable medical devices, especially when these are used in sports applications or by the elderly for self-monitoring. Arterial blood pressure (ABP) is an essential physiological parameter for health monitoring. Most blood pressure measurement devices determine the systolic and diastolic arterial blood pressure through the inflation and the deflation of a cuff. This technique is uncomfortable for the user and may result in anxiety, and consequently affect the blood pressure and its measurement. The purpose of this paper is the continuous measurement of the ABP through a cuffless, non-intrusive approach. The approach of this paper is based on deep learning techniques where several neural networks are used to infer ABP, starting from photoplethysmogram (PPG) and electrocardiogram (ECG) signals. The ABP was predicted first by utilizing only PPG and then by using both PPG and ECG. Convolutional neural networks (ResNet and WaveNet) and recurrent neural networks (LSTM) were compared and analyzed for the regression task. Results show that the use of the ECG has resulted in improved performance for every proposed configuration. The best performing configuration was obtained with a ResNet followed by three LSTM layers: this led to a mean absolute error (MAE) of 4.118 mmHg on and 2.228 mmHg on systolic and diastolic blood pressures, respectively. The results comply with the American National Standards of the Association for the Advancement of Medical Instrumentation. ECG, PPG, and ABP measurements were extracted from the MIMIC database, which contains clinical signal data reflecting real measurements. The results were validated on a custom dataset created at Neuronica Lab, Politecnico di Torino.

Abstract 12944: Geographical Variations in Cardiovascular Health in China: A Nationwide Population-based Survey of 74,771 Adults

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Mei Zhang ◽  
Yu Shi ◽  
Oumin Shi ◽  
Zhenping Zhao ◽  
Xiao Zhang ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Cardiovascular Disease ◽  
Health Behavior ◽  
Health Behaviors ◽  
Cardiovascular Health ◽  
Significant Heterogeneity ◽  
Chinese Adults ◽  
Ideal Cardiovascular Health ◽  
Geographical Variations ◽  
Health Factors

Background: Cardiovascular disease is the leading cause of death in China. Objectives: We aimed to evaluate the levels of cardiovascular health among Chinese adults and to understand the geographic patterns based on a nationally and provincially representative survey. Methods: In 2015, a total of 74,771 respondents aged ≥ 20 years with no history of cardiovascular disease were randomly sampled from 298 counties/districts of 31 provinces in mainland China and were interviewed. Seven metrics, including smoking, body mass index, physical activity, diet, total cholesterol, blood pressure, and fasting glucose, were determined. Ideal cardiovascular health was defined as the simultaneous presence of all metrics at the ideal level. A score ranging from 0 to 14 was calculated as the sum of all seven metrics for each province. Scores for four health behaviors and four health factors were also calculated. Results: The age-adjusted prevalence of ideal cardiovascular health was only 1.13% among Chinese adults above 20 years old in 2015 (0.50% among men and 1.77% among women; 1.63% among urban residents and 0.68% among rural residents). The age-adjusted prevalence varied greatly across provinces, ranging from 0.05% in Qinghai to 2.97% in Heilongjiang. Ideal diet (7.4%) was the least common among seven cardiovascular health metrics and ideal blood pressure (32.2%) was the second least one. We also saw significant heterogeneity among provinces in age-adjusted cardiovascular health score, health behavior score, and health factors score. In all provinces, women had higher scores than men for cardiovascular health, health behaviors and health factors. Differences in cardiovascular health and health behavior scores between urban and rural areas were associated with levels of socio-economic development. Conclusions: Strategies for addressing poor cardiovascular health require geographic targeting and localized consideration.

Abstract 026: Attended Versus Unattended Blood Pressure Measurement in Patients With Cardiovascular Disease: Does It Matter?

Hypertension ◽  
2019 ◽  
Vol 74 (Suppl_1) ◽  
Author(s):  
Matthew Villanueva ◽  
Yiqing E Chen ◽  
Steven M Smith ◽  
Yan Gong ◽  
Eileen M Handberg ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Cardiovascular Disease ◽  
Pressure Measurement ◽  
Blood Pressure Measurement

scholarly journals Tree‐Based Machine Learning to Identify and Understand Major Determinants for Stroke at the Neighborhood Level

2020 ◽  
Vol 9 (22) ◽  
Author(s):  
Liangyuan Hu ◽  
Bian Liu ◽  
Jiayi Ji ◽  
Yan Li
Keyword(s):  
Physical Activity ◽  
Machine Learning ◽  
Cardiovascular Disease ◽  
Cardiovascular Health ◽  
Black People ◽  
The United States ◽  
Machine Learning Techniques ◽  
Data Set ◽  
Wide Range ◽  
Neighborhood Level

Background Stroke is a major cardiovascular disease that causes significant health and economic burden in the United States. Neighborhood community‐based interventions have been shown to be both effective and cost‐effective in preventing cardiovascular disease. There is a dearth of robust studies identifying the key determinants of cardiovascular disease and the underlying effect mechanisms at the neighborhood level. We aim to contribute to the evidence base for neighborhood cardiovascular health research. Methods and Results We created a new neighborhood health data set at the census tract level by integrating 4 types of potential predictors, including unhealthy behaviors, prevention measures, sociodemographic factors, and environmental measures from multiple data sources. We used 4 tree‐based machine learning techniques to identify the most critical neighborhood‐level factors in predicting the neighborhood‐level prevalence of stroke, and compared their predictive performance for variable selection. We further quantified the effects of the identified determinants on stroke prevalence using a Bayesian linear regression model. Of the 5 most important predictors identified by our method, higher prevalence of low physical activity, larger share of older adults, higher percentage of non‐Hispanic Black people, and higher ozone levels were associated with higher prevalence of stroke at the neighborhood level. Higher median household income was linked to lower prevalence. The most important interaction term showed an exacerbated adverse effect of aging and low physical activity on the neighborhood‐level prevalence of stroke. Conclusions Tree‐based machine learning provides insights into underlying drivers of neighborhood cardiovascular health by discovering the most important determinants from a wide range of factors in an agnostic, data‐driven, and reproducible way. The identified major determinants and the interactive mechanism can be used to prioritize and allocate resources to optimize community‐level interventions for stroke prevention.

scholarly journals Understanding the Relationship between Atherogenic Index of Plasma and Cardiovascular Disease Risk Factors among Staff of an University in Malaysia

2018 ◽  
Vol 2018 ◽  
pp. 1-6 ◽  
Author(s):  
Myat Su Bo ◽  
Whye Lian Cheah ◽  
Soe Lwin ◽  
Tin Moe Nwe ◽  
Than Than Win ◽  
...  
Keyword(s):  
Risk Factors ◽  
Blood Pressure ◽  
Cardiovascular Disease ◽  
Visceral Fat ◽  
Blood Pressure Measurement ◽  
Measurement Data ◽  
Atherogenic Index ◽  
Cvd Risk Factors ◽  
Cvd Risk ◽  
Atherogenic Index Of Plasma

Background. Atherogenic index of plasma (AIP) was found to be one of the strongest markers in predicting the cardiovascular disease (CVD) risk. This study was to determine the AIP and its relationship with other CVD risk factors. Materials and Methods. This cross-sectional study was done among 349 staff of a public university in Sarawak. Data were collected using questionnaire, blood sampling, and anthropometric and blood pressure measurement. Data were analyzed using IBM SPSS version 20. Results. A total of 349 respondents participated with majority females (66.8%), aged 38.5 ± 7.82 years. Nearly 80% of the respondents were overweight and obese, 87.1% with high and very high body fat, and 46.9% with abnormal visceral fat. For AIP category, 8.9% were found to be in intermediate and 16.4% were at high risk. Elevated lipid profile showed that total cholesterol (TC) is 15.5%, low density lipoprotein (LDL) is 16.1%, and triglyceride (TG) is 10.6%. AIP was significantly correlated with body mass index (r=0.25), visceral fat (r=0.37), TC (r=0.22), LDL (0.24), HDL (r=−0.72), TG (r=0.84), glucose (r=0.32), systolic blood pressure (r=0.22), and diastolic blood pressure (r=0.28). Conclusion. It indicated that AIP is associated with other CVD risk factors. Modification of lifestyle is strongly recommended.

scholarly journals Assessment of Non-Invasive Blood Pressure Prediction from PPG and rPPG Signals Using Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6022
Author(s):  
Fabian Schrumpf ◽  
Patrick Frenzel ◽  
Christoph Aust ◽  
Georg Osterhoff ◽  
Mirco Fuchs
Keyword(s):  
Blood Pressure ◽  
Prediction Error ◽  
Machine Learning Techniques ◽  
Systematic Evaluation ◽  
Average Error ◽  
Prediction Errors ◽  
Invasive Blood Pressure ◽  
Specific Data ◽  
Non Invasive ◽  
Learning Techniques

Exploiting photoplethysmography signals (PPG) for non-invasive blood pressure (BP) measurement is interesting for various reasons. First, PPG can easily be measured using fingerclip sensors. Second, camera based approaches allow to derive remote PPG (rPPG) signals similar to PPG and therefore provide the opportunity for non-invasive measurements of BP. Various methods relying on machine learning techniques have recently been published. Performances are often reported as the mean average error (MAE) on the data which is problematic. This work aims to analyze the PPG- and rPPG based BP prediction error with respect to the underlying data distribution. First, we train established neural network (NN) architectures and derive an appropriate parameterization of input segments drawn from continuous PPG signals. Second, we use this parameterization to train NNs with a larger PPG dataset and carry out a systematic evaluation of the predicted blood pressure. The analysis revealed a strong systematic increase of the prediction error towards less frequent BP values across NN architectures. Moreover, we tested different train/test set split configurations which underpin the importance of a careful subject-aware dataset assignment to prevent overly optimistic results. Third, we use transfer learning to train the NNs for rPPG based BP prediction. The resulting performances are similar to the PPG-only case. Finally, we apply different personalization techniques and retrain our NNs with subject-specific data for both the PPG-only and rPPG case. Whilst the particular technique is less important, personalization reduces the prediction errors significantly.

scholarly journals Efek Red Wine Terhadap Kesehatan Kardiovaskular

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Thalia W. Malingkas ◽  
Stefana H. M. Kaligis ◽  
Murniati Tiho
Keyword(s):  
Blood Pressure ◽  
Cardiovascular Disease ◽  
Literature Review ◽  
Prospective Study ◽  
Cardiovascular Health ◽  
Red Wine ◽  
Red Grape ◽  
Armed Intervention ◽  
Double Blinded ◽  
Single Blind

Abstract: Many studies showed that antioxidants contained polyphenols and flavonoids such as red wine could reduce the risk of cardiovascular disease. This study’s purpose is to determine the effect of red wine on cardiovascular health. This is a literature review using three databases: Pubmed, Google Scholar and Clinical Key. The keywords using to search the articles are red wine AND cardiovascular disease AND antioxidant OR antioksidan AND flavonoid. After being selected based on inclusion and exclusion criteria, ten literatures were found. The research methods using in the literatures were very varied, which were a cross-over study, single blind cross-over, double-blinded, comprised two study days, parallel four-armed intervention, experimental, randomized, and prospective study. The subjects in these studies were also varied, from healthy people to people with cardiovascular disorders and with other health problems. Besides red wine, interventions with dealcoholized red wine and red grape polyphenol extract (RGPE) also were used in some studies. However, the results from all studies showed that consuming red wine has a good effect on cardiovascular health, measured from LDL and HDL blood level, FMD and systolic blood pressure. In conclusion, red wine consumption has beneficial effects on cardiovascular health.Keywords: red wine, antioxidant, cardiovascular health, polyphenol, flavonoid  Abstrak: Banyak penelitian menunjukkan bahwa antioksidan yang mengandung polifenol dan flavonoid seperti red wine dapat menurunkan risiko penyakit kardiovaskular. Tujuan penelitian ini untuk mengetahui efek red wine terhadap kesehatan kardiovaskular. Penelitian ini berbentuk literature review menggunakan tiga database yaitu Pubmed, Google Scholar, dan Clinical Key.  Kata kunci yang digunakan dalam pencarian artikel yaitu red wine AND cardiovascular disease AND antioxidant OR antioksidan AND flavonoid. Setelah diseleksi berdasarkan kriteria inklusi dan eksklusi, didapatkan 10 literatur yang memenuhi kriteria. Literatur-literatur yang ditemukan menggunakan metode penelitian beragam yaitu cross-over study, single blind cross-over, double-blinded, comprised two study days, parallel four-armed intervention, experimental, randomized, dan prospective study. Subjek yang berpartisipasi dalam studi-studi tersebut juga bervariasi, yaitu terdiri dari orang yang sehat, orang dengan ganguan kardiovaskular dan orang dengan gangguan kesehatan lainnya. Selain red wine, intervensi menggunakan dealcoholized red wine, dan red grape polyphenol extract (RGPE) juga dilakukan pada beberapa studi. Meskipun demikian, hasil yang didapatkan dari semua studi menunjukkan bahwa mengonsumsi red wine memberikan efek yang baik terhadap kesehatan kardiovaskular, yang dilihat dari pengukuran LDL dan HDL darah, FMD dan systolic blood pressure. Sebagai simpulan konsumsi red wine memberikan efek yang bermanfaat bagi kesehatan kardiovaskular.Kata Kunci: red wine, antioksidan, kesehatan kardiovaskular, polifenol, flavonoid

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