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-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-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

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 cuff-less blood pressure estimation using a hybrid deep learning model

2021 ◽  
Vol 53 (2) ◽  
Author(s):  
Sen Yang ◽  
Yaping Zhang ◽  
Siu-Yeung Cho ◽  
Ricardo Correia ◽  
Stephen P. Morgan
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Deep Learning ◽  
Absolute Error ◽  
Learning Model ◽  
Physiological Measurement ◽  
British Hypertension Society ◽  
Non Invasive ◽  
Blood Pressure Estimation ◽  
Deep Learning Model

AbstractConventional blood pressure (BP) measurement methods have different drawbacks such as being invasive, cuff-based or requiring manual operations. There is significant interest in the development of non-invasive, cuff-less and continual BP measurement based on physiological measurement. However, in these methods, extracting features from signals is challenging in the presence of noise or signal distortion. When using machine learning, errors in feature extraction result in errors in BP estimation, therefore, this study explores the use of raw signals as a direct input to a deep learning model. To enable comparison with the traditional machine learning models which use features from the photoplethysmogram and electrocardiogram, a hybrid deep learning model that utilises both raw signals and physical characteristics (age, height, weight and gender) is developed. This hybrid model performs best in terms of both diastolic BP (DBP) and systolic BP (SBP) with the mean absolute error being 3.23 ± 4.75 mmHg and 4.43 ± 6.09 mmHg respectively. DBP and SBP meet the Grade A and Grade B performance requirements of the British Hypertension Society respectively.

scholarly journals An Estimation Method of Continuous Non-Invasive Arterial Blood Pressure Waveform Using Photoplethysmography: A U-Net Architecture-Based Approach

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1867
Author(s):  
Tasbiraha Athaya ◽  
Sunwoong Choi
Keyword(s):  
Blood Pressure ◽  
Cardiovascular Health ◽  
Estimation Method ◽  
Absolute Error ◽  
British Hypertension Society ◽  
Non Invasive ◽  
Arterial Blood ◽  
Blood Pressure Waveform ◽  
The Mean ◽  
Mimic Iii

Blood pressure (BP) monitoring has significant importance in the treatment of hypertension and different cardiovascular health diseases. As photoplethysmogram (PPG) signals can be recorded non-invasively, research has been highly conducted to measure BP using PPG recently. In this paper, we propose a U-net deep learning architecture that uses fingertip PPG signal as input to estimate arterial BP (ABP) waveform non-invasively. From this waveform, we have also measured systolic BP (SBP), diastolic BP (DBP), and mean arterial pressure (MAP). The proposed method was evaluated on a subset of 100 subjects from two publicly available databases: MIMIC and MIMIC-III. The predicted ABP waveforms correlated highly with the reference waveforms and we have obtained an average Pearson’s correlation coefficient of 0.993. The mean absolute error is 3.68 ± 4.42 mmHg for SBP, 1.97 ± 2.92 mmHg for DBP, and 2.17 ± 3.06 mmHg for MAP which satisfy the requirements of the Association for the Advancement of Medical Instrumentation (AAMI) standard and obtain grade A according to the British Hypertension Society (BHS) standard. The results show that the proposed method is an efficient process to estimate ABP waveform directly using fingertip PPG.

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