Finger Position and Force Simultaneous Prediction Using A-mode Ultrasound

2019 ◽  
Author(s):  
Yu Zhou ◽  
Jia Zeng ◽  
Yicheng Yang ◽  
Honghai Liu
Keyword(s):  
Finger Position ◽  
Simultaneous Prediction

scholarly journals Combined deep CNN–LSTM network-based multitasking learning architecture for noninvasive continuous blood pressure estimation using difference in ECG-PPG features

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Da Un Jeong ◽  
Ki Moo Lim
Keyword(s):  
Blood Pressure ◽  
Blood Pressure Measurement ◽  
Patient Specific ◽  
Peak Time ◽  
Simultaneous Prediction ◽  
Deep Cnn ◽  
Pressure Estimation ◽  
The Difference ◽  
Blood Pressure Estimation ◽  
Continuous Blood Pressure

AbstractThe pulse arrival time (PAT), the difference between the R-peak time of electrocardiogram (ECG) signal and the systolic peak of photoplethysmography (PPG) signal, is an indicator that enables noninvasive and continuous blood pressure estimation. However, it is difficult to accurately measure PAT from ECG and PPG signals because they have inconsistent shapes owing to patient-specific physical characteristics, pathological conditions, and movements. Accordingly, complex preprocessing is required to estimate blood pressure based on PAT. In this paper, as an alternative solution, we propose a noninvasive continuous algorithm using the difference between ECG and PPG as a new feature that can include PAT information. The proposed algorithm is a deep CNN–LSTM-based multitasking machine learning model that outputs simultaneous prediction results of systolic (SBP) and diastolic blood pressures (DBP). We used a total of 48 patients on the PhysioNet website by splitting them into 38 patients for training and 10 patients for testing. The prediction accuracies of SBP and DBP were 0.0 ± 1.6 mmHg and 0.2 ± 1.3 mmHg, respectively. Even though the proposed model was assessed with only 10 patients, this result was satisfied with three guidelines, which are the BHS, AAMI, and IEEE standards for blood pressure measurement devices.

scholarly journals Simultaneous Prediction of Binding Capacity for Multiple Molecules of the HLA B44 Supertype

2003 ◽  
Vol 171 (11) ◽  
pp. 5964-5974 ◽  
Author(s):  
John Sidney ◽  
Scott Southwood ◽  
Valerie Pasquetto ◽  
Alessandro Sette
Keyword(s):  
Binding Capacity ◽  
Simultaneous Prediction ◽  
Multiple Molecules

Simultaneous prediction intervals for ARMA processes with stable innovations

2009 ◽  
Vol 28 (3) ◽  
pp. 235-246 ◽  
Author(s):  
John P. Nolan ◽  
Nalini Ravishanker
Keyword(s):  
Prediction Intervals ◽  
Arma Processes ◽  
Simultaneous Prediction

scholarly journals Combined Deep CNN–LSTM Network-based Multitasking Learning Architecture for Noninvasive Continuous Blood Pressure Estimation using Difference in ECG-PPG Features

2021 ◽  
Author(s):  
Da Un Jeong ◽  
Ki Moo Lim
Keyword(s):  
Blood Pressure ◽  
Pulse Transit Time ◽  
Patient Specific ◽  
Peak Time ◽  
Simultaneous Prediction ◽  
Deep Cnn ◽  
Pressure Estimation ◽  
The Difference ◽  
Blood Pressure Estimation ◽  
Continuous Blood Pressure

Abstract The pulse transit time (PTT), which is the difference between the R-peak time of the electrocardiogram (ECG) signal and the systolic peak of the photoplethysmography (PPG) signal, is an indicator that enables noninvasive and continuous blood pressure estimation. However, it is difficult to accurately measure the PTT from the ECG and PPG signals because they have inconsistent shapes owing to patient-specific physical characteristics, pathological conditions, and movements. Accordingly, complex preprocessing is required to estimate blood pressure based on PTT. In this paper, as an alternative solution, we propose a noninvasive continuous algorithm using the difference between the ECG and PPG as a new feature that can include PTT information. The proposed algorithm is a deep CNN–LSTM-based multitasking machine learning model that outputs simultaneous prediction results of systolic (SBP) and diastolic blood pressures (DBP). The prediction accuracies of SBP and DBP using the proposed model were 0.017±1.624 mmHg and 0.164±1.297 mmHg, respectively. This result corresponded to Grade A according to the BHS and AAMI standards, which are the validation standards for blood pressure measuring devices.

scholarly journals Simultaneous Prediction of Wrist/Hand Motion via Wearable Ultrasound Sensing

2020 ◽  
Vol 28 (4) ◽  
pp. 970-977 ◽  
Author(s):  
Xingchen Yang ◽  
Jipeng Yan ◽  
Yinfeng Fang ◽  
Dalin Zhou ◽  
Honghai Liu
Keyword(s):  
Hand Motion ◽  
Simultaneous Prediction ◽  
Ultrasound Sensing

scholarly journals Use of rapid small-scale column tests for simultaneous prediction of phosphorus and nitrogen retention in large-scale filters

2020 ◽  
Vol 37 ◽  
pp. 101473 ◽  
Author(s):  
G. Ezzati ◽  
M.G. Healy ◽  
L. Christianson ◽  
K. Daly ◽  
O. Fenton ◽  
...  
Keyword(s):  
Large Scale ◽  
Nitrogen Retention ◽  
Small Scale ◽  
Column Tests ◽  
Simultaneous Prediction
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