Abdel Rahman Alkharabsheh

Design of an intelligent and personalised shunting system for hydrocephalus

Hydrocephalus is a neurological disease that manifests itself in an elevated fluid pressure within the brain, and if left untreated, may be fatal. It is currently treated using shunt implants, which consist of a mechanical valve and tubes that regulate the pressure of cerebrospinal fluid (CSF) by draining excess fluid into the abdomen. Hydrocephalus shunting systems are no longer expected simply to regulate the intracranial pressure (ICP), but also to offer the option of regaining independence of the shunt. Additionally, they could offer personalised valve management which is one of the main limitations of current shunts. This paper describes the design of a multi-agent system for an intelligent and personalised CSF management system. Patient feedback and intracranial pressure readings will play important roles in the process of CSF regulation and weaning, introduces an element of personalisation to the treatment. The new shunting system would deliver both reactive and goal-driven solutions for the treatment, at the same time the intelligent part of the system will be monitoring how well the shunt is performing. These tasks can be achieved by implementing an agent approach in designing this system. Such system would help us to understand more about the dynamics of hydrocephalus.

An expert system for hydrocephalus patient feedback

Diagnosis of hydrocephalus symptoms and shunting system faults currently are based on clinical observation, monitoring of cranial growth, transfontanelle pressure, imaging techniques and, on occasion, studies of cerebrospinal fluid (CSF) dynamics. Up to date, the patient has to visit the hospital or meet consultant to diagnose the symptoms that occur due to rising of intracranial pressure or any shunt complications, which cause suffering for the patient and his family. This work presents the design and implementation of an expert system based on real-time patient feedback that aims to provide a suitable decision for hydrocephalus management and shunt diagnosis. Such decision would help in personalising the management as well as detecting and identifying of any shunt malfunctions without the need to contact or visit the hospital. In this paper, the development of patient feedback expert system is described. The outcome of such system would help satisfy the patients needs regarding his/her shunt.

An Intelligent Implantable Wireless Shunting System for Hydrocephalus Patients

Hydrocephalus is a neurological disorder whereby the cerebrospinal fluid surrounding the brain is improperly drained, causing severe pain and swelling of the head. Existing treatments rely on passive implantable shunts with differential pressure valves; these have many limitations, and lifethreatening complications often arise. In addition, the inability of such devices to autonomously and spontaneously adapt to the needs of the patients results in frequent hospital visits and shunt revisions.

Multivariate analysis of intracranial pressure (ICP) signal using principal component analysis

The diagnosis and treatment of hydrocephalus and other neurological disorders often involve the acquisition and analysis of large amount of intracranial pressure (ICP) signal. Although the analysis and subsequent interpretation of this data is an essential part of the clinical management of the disorders, it is typically done manually by a trained clinician, and the difficulty in interpreting some of the features of this complex time series can sometimes lead to issues of subjectivity and reliability. This paper presents a method for the quantitative analysis of this data using a multivariate approach based on principal component analysis, with the aim of optimising symptom diagnosis, patient characterisation and treatment simulation and personalisation. In this method, 10 features are extracted from the ICP signal and principal components that represent these features are defined and analysed. Results from ICP traces of 40 patients show that the chosen features have relevant information about the ICP signal and can be represented with a few components of the PCA (approximately 91% of the total variance of the data is represented by the first four components of the PCA) and that these components can be helpful in characterising subgroups in the patient population that would otherwise not have been apparent. The introduction of supplementaty (non-ICP) variables has offered insight into additional groupings and relationships which may prove to be a fruitful avenue for exploration.