Neuro-critical multimodal Edge-AI monitoring algorithm and IoT system design and development

In recent years, with the continuous development of neurocritical medicine, the success rate of treatment of patients with traumatic brain injury (TBI) has continued to increase, and the prognosis has also improved. TBI patients' condition is usually very complicated, and after treatment, patients often need a more extended time to recover. The degree of recovery is also related to prognosis. However, as a young discipline, neurocritical medicine still has many shortcomings. Especially in most hospitals, the condition of Neuro-intensive Care Unit (NICU) is uneven, the equipment has limited functionality, and there is no unified data specification. Most of the instruments are cumbersome and expensive, and patients often need to pay high medical expenses. Recent years have seen a rapid development of big data and artificial intelligence (AI) technology, which are advancing the medical IoT field. However, further development and a wider range of applications of these technologies are needed to achieve widespread adoption. Based on the above premises, the main contributions of this thesis are the following. First, the design and development of a multi-modal brain monitoring system including 8-channel electroencephalography (EEG) signals, dual-channel NIRS signals, and intracranial pressure (ICP) signals acquisition. Furthermore, an integrated display platform for multi-modal physiological data to display and analysis signals in real-time was designed. This thesis also introduces the use of the Qt signal and slot event processing mechanism and multi-threaded to improve the real-time performance of data processing to a higher level. In addition, multi-modal electrophysiological data storage and processing was realized on cloud server. The system also includes a custom built Django cloud server which realizes real-time transmission between server and WeChat applet. Based on WebSocket protocol, the data transmission delay is less than 10ms. The analysis platform can be equipped with deep learning models to realize the monitoring of patients with epileptic seizures and assess the level of consciousness of Disorders of Consciousness (DOC) patients. This thesis combines the standard open-source data set CHB-MIT, a clinical data set provided by Huashan Hospital, and additional data collected by the system described in this thesis. These data sets are merged to build a deep learning network model and develop related applications for automatic disease diagnosis for smart medical IoT systems. It mainly includes the use of the clinical data to analyze the characteristics of the EEG signal of DOC patients and building a CNN model to evaluate the patient's level of consciousness automatically. Also, epilepsy is a common disease in neuro-intensive care. In this regard, this thesis also analyzes the differences of various deep learning model between the CHB-MIT data set and clinical data set for epilepsy monitoring, in order to select the most appropriate model for the system being designed and developed. Finally, this thesis also verifies the AI-assisted analysis model.. The results show that the accuracy of the CNN network model based on the evaluation of consciousness disorder on the clinical data set reaches 82%. The CNN+STFT network model based on epilepsy monitoring reaches 90% of the accuracy rate in clinical data. Also, the multi-modal brain monitoring system built is fully verified. The EEG signal collected by this system has a high signal-to-noise ratio, strong anti-interference ability, and is very stable. The built brain monitoring system performs well in real-time and stability. Keywords: TBI, Neurocritical care, Multi-modal, Consciousness Assessment, seizures detection, deep learning, CNN, IoT

Brain Injury

The present two volume book "Brain Injury" is distinctive in its presentation and includes a wealth of updated information on many aspects in the field of brain injury. The Book is devoted to the pathogenesis of brain injury, concepts in cerebral blood flow and metabolism, investigative approaches and monitoring of brain injured, different protective mechanisms and recovery and management approach to these individuals, functional and endocrine aspects of brain injuries, approaches to rehabilitation of brain injured and preventive aspects of traumatic brain injuries. The collective contribution from experts in brain injury research area would be successfully conveyed to the readers and readers will find this book to be a valuable guide to further develop their understanding about brain injury

A distributed architecture for the monitoring and analysis of time series data

It is estimated that the quantity of digital data being transferred, processed or stored at any one time currently stands at 4.4 zettabytes (4.4 × 2 70 bytes) and this figure is expected to have grown by a factor of 10 to 44 zettabytes by 2020. Exploiting this data is, and will remain, a significant challenge. At present there is the capacity to store 33% of digital data in existence at any one time; by 2020 this capacity is expected to fall to 15%. These statistics suggest that, in the era of Big Data, the identification of important, exploitable data will need to be done in a timely manner. Systems for the monitoring and analysis of data, e.g. stock markets, smart grids and sensor networks, can be made up of massive numbers of individual components. These components can be geographically distributed yet may interact with one another via continuous data streams, which in turn may affect the state of the sender or receiver. This introduces a dynamic causality, which further complicates the overall system by introducing a temporal constraint that is difficult to accommodate. Practical approaches to realising the system described above have led to a multiplicity of analysis techniques, each of which concentrates on specific characteristics of the system being analysed and treats these characteristics as the dominant component affecting the results being sought. The multiplicity of analysis techniques introduces another layer of heterogeneity, that is heterogeneity of approach, partitioning the field to the extent that results from one domain are difficult to exploit in another. The question is asked can a generic solution for the monitoring and analysis of data that: accommodates temporal constraints; bridges the gap between expert knowledge and raw data; and enables data to be effectively interpreted and exploited in a transparent manner, be identified? The approach proposed in this dissertation acquires, analyses and processes data in a manner that is free of the constraints of any particular analysis technique, while at the same time facilitating these techniques where appropriate. Constraints are applied by defining a workflow based on the production, interpretation and consumption of data. This supports the application of different analysis techniques on the same raw data without the danger of incorporating hidden bias that may exist. To illustrate and to realise this approach a software platform has been created that allows for the transparent analysis of data, combining analysis techniques with a maintainable record of provenance so that independent third party analysis can be applied to verify any derived conclusions. In order to demonstrate these concepts, a complex real world example involving the near real-time capturing and analysis of neurophysiological data from a neonatal intensive care unit (NICU) was chosen. A system was engineered to gather raw data, analyse that data using different analysis techniques, uncover information, incorporate that information into the system and curate the evolution of the discovered knowledge. The application domain was chosen for three reasons: firstly because it is complex and no comprehensive solution exists; secondly, it requires tight interaction with domain experts, thus requiring the handling of subjective knowledge and inference; and thirdly, given the dearth of neurophysiologists, there is a real world need to provide a solution for this domai

Neurology of Covid-19

The authors will present a comprehensive account of the neurological aspects of SARS-CoV-2 infection. The aim is to provide a practical clinical book which will serve as a guide for clinicians from all specialties involved in the management of COVID-19 patients. The authors share the extensive clinical experience gained in major hospitals in Lombardy, the first European region to face the COVID-19 emergency in 2020. All are recognized international experts in their respective fields and have been involved in the management of COVID-19 cases from the very beginning of the Italian SARS-CoV-2 outbreak. The text begins with a description of pathobiological and pathophysiological aspects related to the involvement of the nervous system, moving on to the discussion of the neurological complications observed in COVID-19 patients; these range from central to peripheral symptoms, and can occur in the acute or post-acute phases of the disease. Further topics are: neuropathology, seizures and EEG, neuroimaging, delirium, encephalomyelitis, stroke, psychopathology and psychiatry, neuropsychology and cognitive impairment, neuromuscu-lar disorders, and the impact of COVID-19 on other pre-existing neurological disorders. In addi-tion, the book will discuss the new developments in teleneurology approaches, which have been a direct response to the ongoing pandemic. Finally, the possible neurological complications of the COVID-19 vaccines and the neurological complications in children will be considered.Each chapter will present a critical review of the existing literature concerning the specific subject matter, followed by practical clinical recommendations, as well as personal considerations based on the experience gained by each author during the course of the COVID-19 pandemic. Neurology of COVID-19 will be an original and innovative reference book for clinicians of all the specialties involved in the management of patients with SARS-CoV-2 infection.illustrato

Neurology of Covid-19

The authors will present a comprehensive account of the neurological aspects of SARS-CoV-2 infection. The aim is to provide a practical clinical book which will serve as a guide for clinicians from all specialties involved in the management of COVID-19 patients. The authors share the extensive clinical experience gained in major hospitals in Lombardy, the first European region to face the COVID-19 emergency in 2020. All are recognized international experts in their respective fields and have been involved in the management of COVID-19 cases from the very beginning of the Italian SARS-CoV-2 outbreak. The text begins with a description of pathobiological and pathophysiological aspects related to the involvement of the nervous system, moving on to the discussion of the neurological complications observed in COVID-19 patients; these range from central to peripheral symptoms, and can occur in the acute or post-acute phases of the disease. Further topics are: neuropathology, seizures and EEG, neuroimaging, delirium, encephalomyelitis, stroke, psychopathology and psychiatry, neuropsychology and cognitive impairment, neuromuscu-lar disorders, and the impact of COVID-19 on other pre-existing neurological disorders. In addi-tion, the book will discuss the new developments in teleneurology approaches, which have been a direct response to the ongoing pandemic. Finally, the possible neurological complications of the COVID-19 vaccines and the neurological complications in children will be considered.Each chapter will present a critical review of the existing literature concerning the specific subject matter, followed by practical clinical recommendations, as well as personal considerations based on the experience gained by each author during the course of the COVID-19 pandemic. Neurology of COVID-19 will be an original and innovative reference book for clinicians of all the specialties involved in the management of patients with SARS-CoV-2 infection.Publishe