scholarly journals Non-Invasive Arterial Pressure Estimating With the Cardiac Monitor CardioQvark

2017 ◽  
Vol 12 (2) ◽  
pp. 536-546 ◽  
Author(s):  
О.В. Сенько ◽  
O.V. Senko
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Arterial Pressure ◽  
Systolic Blood Pressure ◽  
General Purpose ◽  
Machine Learning Techniques ◽  
Non Invasive ◽  
Cardiac Monitor ◽  
Learning Techniques ◽  
Invasive Arterial Pressure

The outcome of the research on possibility to non-invasively estimate systolic blood pressure is presented. The estimating was performed by applying machine learning techniques to the data acquired with the cardiac monitor CardioQvark. The developed in Russia cardiac monitor represents a portable device capable of registering synchronous electrocardiogram and photoplethysmogram. The presented results confirm the possibility of constructing algorithms capable of estimating systolic blood pressure of individual patients. Also the possibility to construct general purpose algorithms, i.e. algorithms capable of estimating blood pressure of any patient without additional setup, was confirmed.

scholarly journals Non-Invasive Blood Pressure Estimation from ECG Using Machine Learning Techniques

Sensors ◽  
2018 ◽  
Vol 18 (4) ◽  
pp. 1160 ◽  
Author(s):  
Monika Simjanoska ◽  
Martin Gjoreski ◽  
Matjaž Gams ◽  
Ana Madevska Bogdanova
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Machine Learning Techniques ◽  
Invasive Blood Pressure ◽  
Non Invasive ◽  
Learning Techniques ◽  
Pressure Estimation ◽  
Blood Pressure Estimation

Non-invasive Deception Detection in Videos Using Machine Learning Techniques

2021 ◽  
Author(s):  
Siam Islam ◽  
Popin Saha ◽  
Touhidul Chowdhury ◽  
Asif Sorowar ◽  
Raqeebir Rab
Keyword(s):  
Machine Learning ◽  
Deception Detection ◽  
Machine Learning Techniques ◽  
Non Invasive ◽  
Learning Techniques

Improving Reliability Estimation for Individual Numeric Predictions: A Machine Learning Approach

2021 ◽  
Author(s):  
Gediminas Adomavicius ◽  
Yaqiong Wang
Keyword(s):  
Machine Learning ◽  
General Purpose ◽  
Reliability Estimation ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Real World Data ◽  
Learning Techniques ◽  
Reliability Indicator ◽  
Machine Learning Approach ◽  
Prediction Reliability

Numerical predictive modeling is widely used in different application domains. Although many modeling techniques have been proposed, and a number of different aggregate accuracy metrics exist for evaluating the overall performance of predictive models, other important aspects, such as the reliability (or confidence and uncertainty) of individual predictions, have been underexplored. We propose to use estimated absolute prediction error as the indicator of individual prediction reliability, which has the benefits of being intuitive and providing highly interpretable information to decision makers, as well as allowing for more precise evaluation of reliability estimation quality. As importantly, the proposed reliability indicator allows the reframing of reliability estimation itself as a canonical numeric prediction problem, which makes the proposed approach general-purpose (i.e., it can work in conjunction with any outcome prediction model), alleviates the need for distributional assumptions, and enables the use of advanced, state-of-the-art machine learning techniques to learn individual prediction reliability patterns directly from data. Extensive experimental results on multiple real-world data sets show that the proposed machine learning-based approach can significantly improve individual prediction reliability estimation as compared with a number of baselines from prior work, especially in more complex predictive scenarios.

scholarly journals NON-INVASIVE MODELING OF INTRACRANIAL HYPERTENSION FROM PHYSIOLOGICAL CHANNELS

2021 ◽  
Author(s):  
Parisa Naraei ◽  
Alireza Sadeghian
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Intracranial Hypertension ◽  
Clinical Decision Making ◽  
Machine Learning Techniques ◽  
Non Invasive ◽  
Arterial Blood ◽  
Secondary Damage ◽  
Invasive Techniques ◽  
Present Thesis

Intracranial pressure (ICP), the pressure within the cranium reflects three elements: cerebrospinal fluid, brain tissue and blood pressure. High ICP (above 20 mmHg) is called intracranial hypertension (ICH) which is due to the tumour, swelling or the internal bleeding of brain and may cause secondary damage to the brain. ICP is a crucial parameter in diagnosis of brain injuries. Two models which utilize machine learning techniques to anticipate ICH and assist in clinical decision making were developed in the present thesis. ICP can be monitored through the invasive techniques (i.e., inserting an intraventricular catheter through the skull). Despite the high accuracy, the episodes of ICH can also be manually identified only after placement of catheter which is accompanied by lots of technical difficulties. Furthermore, the ICP signal might not be available continuously or may include unwanted noise that could introduce more complication to the diagnosis and treatment procedure. Considering the difficulties of the invasive techniques, a non-invasive model, capable to predict the ICH helps to save time, estimate the missing ICPs, predict the ICP in advance and accelerate medical intervention. The present thesis introduces two machine learning models to resolve the current limitations: 1- Non-invasive prediction of ICP labels 10 minutes in advance where the status of ICP (normal / ICH) is predicted based on the two components extracted from the physiological signals such as mean arterial blood pressure and respiration rate. 2- Wavelet – clustering where a machine learning solution for ICP estimation using a hybrid wavelet clustering is proposed. The episodes of ICP and derived from ICP (such as cerebral perfusion pressure) are excluded from the second model.

scholarly journals Non-Intrusive Load Monitoring of Residential Water-Heating Circuit Using Ensemble Machine Learning Techniques

Inventions ◽  
2020 ◽  
Vol 5 (4) ◽  
pp. 57
Author(s):  
Attique Ur Rehman ◽  
Tek Tjing Lie ◽  
Brice Vallès ◽  
Shafiqur Rahman Tito
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Machine Learning Techniques ◽  
Learning Models ◽  
Water Heating ◽  
Energy Monitoring ◽  
Non Invasive ◽  
Ensemble Machine Learning ◽  
Learning Techniques ◽  
Load Monitoring

The recent advancement in computational capabilities and deployment of smart meters have caused non-intrusive load monitoring to revive itself as one of the promising techniques of energy monitoring. Toward effective energy monitoring, this paper presents a non-invasive load inference approach assisted by feature selection and ensemble machine learning techniques. For evaluation and validation purposes of the proposed approach, one of the major residential load elements having solid potential toward energy efficiency applications, i.e., water heating, is considered. Moreover, to realize the real-life deployment, digital simulations are carried out on low-sampling real-world load measurements: New Zealand GREEN Grid Database. For said purposes, MATLAB and Python (Scikit-Learn) are used as simulation tools. The employed learning models, i.e., standalone and ensemble, are trained on a single household’s load data and later tested rigorously on a set of diverse households’ load data, to validate the generalization capability of the employed models. This paper presents a comprehensive performance evaluation of the presented approach in the context of event detection, feature selection, and learning models. Based on the presented study and corresponding analysis of the results, it is concluded that the proposed approach generalizes well to the unseen testing data and yields promising results in terms of non-invasive load inference.

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 Detection of hypotension during spinal anesthesia for caesarean section with continuous non-invasive arterial pressure monitoring and intermittent oscillometric blood pressure monitoring in patients treated with ephedrine or phenylephrine

2021 ◽  
pp. 30-30
Author(s):  
Aleksandra Vukotic ◽  
Jasna Jevdjic ◽  
David Green ◽  
Milovan Vukotic ◽  
Nina Petrovic ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Arterial Pressure ◽  
Spinal Anesthesia ◽  
Blood Pressure Monitoring ◽  
Umbilical Vein ◽  
Elective Cesarean Section ◽  
Pressure Monitoring ◽  
Non Invasive ◽  
Elective Cesarean ◽  
Invasive Arterial Pressure

Introduction/Objective. Despite frequent side effects such as hypotension, spinal anesthesia (SA) is still one of the best anesthetic methods for elective cesarean section (CS). Intermittent, oscillometric, non-invasive blood pressure monitoring (NIBP) frequently leads to the missed hypotensive episodes. Our goal was to compare continuous non-invasive arterial pressure (CNAP) monitoring with NIBP in the terms of efficiency to detect hypotension. Methods. In this study, we compared CNAP and NIBP monitoring for hypotension detection in 76 patients divided into two groups of 38 patients treated with ephedrine (E) or phenylephrine (P), during 3 min intervals, starting from SA, by the end of the surgery. Results. In group E, significantly lower mean systolic blood pressure (SBP) values with CNAP compared with NIBP (p = 0.008) was detected. CNAP detected 31 (81.6%) hypotensive patients in E group and significantly lower number 20 (52.6%) with NIBP (p = 0.001), while in P group CNAP detected 34 patients (89.5%) and NIBP, only 18 (47.3%), p = 0.001. CNAP detected significantly higher number of hypotensive intervals in E and P groups (p < 0.001). Umbilical vein pH was lower within hypotensive compared with normotensive patients in E and P groups, with CNAP and NIBP, respectively (p < 0.001, p = 0.027 in E, and p = 0.009, p < 0.001, in P group). Conclusion. CNAP is much more efficient in hypotension detection for CS during SA, which allows faster treatment of hypotension, thus improving fetal and maternal outcome.

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