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.
A Novel Non-Invasive Estimation of Arterial Blood Pressure from Electrocardiography and Photoplethysmography Signals using Machine Learning
