Blood Pressure Prediction by a Smartphone Sensor using Fully Convolutional Networks

Baek, Sang-Min; Jang, Ji-Yong; Cho, Sung-Hwan; Choi, Jong Min; Yoon, Sungroh

doi:10.1109/embc44109.2020.9175902

Cited by 10 publications

(8 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although biological mechanisms underlying hypertension at the single omic level have been discovered, multi-omics integrative analyses using continuous variations in blood pressure values remain limited. Evaluation of the integrated predictive value of various molecular substrates of hypertension is also actively being pursued (Baek et al, 2020 ; Kwong et al, 2018 ; Wang et al, 2018 ). A better understanding of the mechanisms reflecting unitary changes in blood pressure could allow for fine mapping of interindividual differences than those captured by discriminant or categorical analyses.…”

Section: Introductionmentioning

confidence: 99%

Multi-Omics Integration in a Twin Cohort and Predictive Modeling of Blood Pressure Values

Drouard

Ollikainen

Mykkänen

et al. 2022

OMICS: A Journal of Integrative Biology

View full text Add to dashboard Cite

Abnormal blood pressure is strongly associated with risk of high-prevalence diseases, making the study of blood pressure a major public health challenge. Although biological mechanisms underlying hypertension at the single omic level have been discovered, multi-omics integrative analyses using continuous variations in blood pressure values remain limited. We used a multi-omics regression-based method, called sparse multi-block partial least square, for integrative, explanatory, and predictive interests in study of systolic and diastolic blood pressure values. Various datasets were obtained from the Finnish Twin Cohort for up to 444 twins. Blocks of omics—including transcriptomic, methylation, metabolomic—data as well as polygenic risk scores and clinical data were integrated into the modeling and supported by cross-validation. The predictive contribution of each omics block when predicting blood pressure values was investigated using external participants from the Young Finns Study. In addition to revealing interesting inter-omics associations, we found that each block of omics heterogeneously improved the predictions of blood pressure values once the multi-omics data were integrated. The modeling revealed a plurality of clinical, transcriptomic, and metabolomic factors consistent with the literature and that play a leading role in explaining unit variations in blood pressure. These findings demonstrate (1) the robustness of our integrative method to harness results obtained by single omics discriminant analyses, and (2) the added value of predictive and exploratory gains of a multi-omics approach in studies of complex phenotypes such as blood pressure.

show abstract

Section: Introductionmentioning

confidence: 99%

Multi-Omics Integration in a Twin Cohort and Predictive Modeling of Blood Pressure Values

Drouard

Ollikainen

Mykkänen

et al. 2022

OMICS: A Journal of Integrative Biology

View full text Add to dashboard Cite

show abstract

“…This is advantageous since each region is differentially innervated, possibly influencing pressure prediction. The potential to reduce dependence on feature extraction is exemplified in the study by Baek et al ( 21 ), who applied convolutional neural networks to PPG data without feature extraction to predict BP.…”

Section: Discussion Perspectives and Future Outlookmentioning

confidence: 99%

“…Possibilities for successful BP monitoring was demonstrated in early studies, such as Lamonaca et al ( 5 ), that used the rear camera of the phone in combination with the LED to extract a strong pulse signal in the finger. Later studies included more features for analysis ( 18 ), or used convolutional neural networks to predict BP without waveform feature extraction ( 21 ). In parallel, non-contact methods were developed that overcame deficiencies of contact methods, such as BP prediction limited to a small field of view (fingertip).…”

Section: Discussion Perspectives and Future Outlookmentioning

confidence: 99%

“…These studies used feature extraction to predict BP, which may be influenced by sensor and signal quality, and vary between studies. It is possible to eliminate feature extraction, as demonstrated by Baek et al ( 21 ). In this study, a convolutional neural network was applied to PPG signals without feature extraction.…”

Section: Non-invasive Smartphone Contact Measurements Of Blood Pressurementioning

confidence: 99%

See 1 more Smart Citation

Smartphones and Video Cameras: Future Methods for Blood Pressure Measurement

Steinman

Barszczyk

Sun

et al. 2021

Front. Digit. Health

View full text Add to dashboard Cite

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.

show abstract

“…Finally, a convolutional neural network model was employed to estimate blood pressure, with the green light as the most accurate indicator. The mean absolute error and standard deviation of SBP and DBP were 5.28 ± 1.80 and 4.92 ± 2.42 mmHg, respectively [10]. Panwar et al proposed PP-Net, a deep learning framework that combines a convolutional neural network with a long short-term memory network to estimate SBP, DBP, and heart rate.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning-Based Continuous Blood Pressure Measurement by Dual Photoplethysmography Signals

Yen¹,

Chang²,

Liao³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

This study proposed a measurement platform for continuous blood pressure estimation based on dual photoplethysmography (PPG) sensors and a deep learning (DL) that can be used for continuous and rapid measurement of blood pressure and analysis of cardiovascular-related indicators. The proposed platform measured the signal changes in PPG and converted them into physiological indicators, such as pulse transit time (PTT), pulse wave velocity (PWV), perfusion index (PI) and heart rate (HR); these indicators were then fed into the DL to calculate blood pressure. The hardware of the experiment comprised 2 PPG components (i.e., Raspberry Pi 3 Model B and analog-todigital converter [MCP3008]), which were connected using a serial peripheral interface. The DL algorithm converted the stable dual PPG signals acquired from the strictly standardized experimental process into various physiological indicators as input parameters and finally obtained the systolic blood pressure (SBP), diastolic blood pressure (DBP) and mean arterial pressure (MAP). To increase the robustness of the DL model, this study input data of 100 Asian participants into the training database, including those with and without cardiovascular disease, each with a proportion of approximately 50%. The experimental results revealed that the mean absolute error and standard deviation of SBP was 0.17 ± 0.46 mmHg. The mean absolute error and standard deviation of DBP was 0.27 ± 0.52 mmHg. The mean absolute error and standard deviation of MAP was 0.16 ± 0.40 mmHg.

show abstract

Blood Pressure Prediction by a Smartphone Sensor using Fully Convolutional Networks

Cited by 10 publications

References 13 publications

Multi-Omics Integration in a Twin Cohort and Predictive Modeling of Blood Pressure Values

Multi-Omics Integration in a Twin Cohort and Predictive Modeling of Blood Pressure Values

Smartphones and Video Cameras: Future Methods for Blood Pressure Measurement

A Deep Learning-Based Continuous Blood Pressure Measurement by Dual Photoplethysmography Signals

Contact Info

Product

Resources

About