IoT in the Field of Healthcare

The chapter at first focuses on IoT being used in healthcare in general and then it moves to Parkinson's disease, specifically, and how people with it can be benefitted by IoT.

Stroke Rehabilitation and Parkinson's Disease Tremor Reduction Using BCIs Combined With FES

This article describes how Stroke and Parkinson's disease are two illnesses that particularly affect motor functions. With the advancements in technology, there is a lot of research focusing on finding solutions: to contribute to neuroplasticity in the first case, and to reduce symptoms in the second case. This manuscript describes the design of a brain-computer interface system (BCI) system paired with an electrical muscle stimulation suit for stroke rehabilitation and the reduction of tremors caused by Parkinson's disease. The idea is to strengthen the sensory-motor feedback loop, which will allow a more stabilized control of the affected extremities by taking into account the patient's motivation. To do so, his brain signals are measured to detect his intention to attempt to execute a movement, in contrast to the classical approach where the movement executions are imposed. A first feasibility study was completed. The author's next step is planning to test the system first with healthy subjects and finally with patients.

Soft Computing-Based Early Detection of Parkinson's Disease Using Non-Invasive Method Based on Speech Analysis

This chapter aims to use the speech signals that are a behavioral bio-marker for Parkinson's disease. The victim's vocabulary is mostly lost, or big gaps are observed when they are talking or the conversation is abruptly stopped. Therefore, speech analysis could help to identify the complications in conversation from the inception of the symptoms of Parkinson's disease in initial phases itself. Speech can be regularly logged in an unobstructed approach and machine learning techniques can be applied and analyzed. Fuzzy logic-based classifier is proposed for learning from the training speech signals and classifying the test speech signals. Brainstorm optimization algorithm is proposed for extracting the fuzzy rules from the speech data, which is used by fuzzy classifier for learning and classification. The performance of the proposed classifier is evaluated using metrics like accuracy, specificity, and sensitivity, and compared with benchmark classifiers like SVM, naïve Bayes, k-means, and decision tree. It is observed that the proposed classifier outperforms the benchmark classifiers.

Neuro-Imaging Machine Learning Techniques for Alzheimer's Disease Diagnosis

Alzheimer's disease (AD) is considered one of the most common dementia's forms affecting senior's age staring from 65 and over. The standard method for identifying AD are usually based on behavioral, neuropsychological and cognitive tests and sometimes followed by a brain scan. Advanced medical imagining modalities such as MRI and pattern recognition techniques are became good tools for predicting AD. In this chapter, an automatic AD diagnosis system from MRI images based on using machine learning tools is proposed. A bench mark dataset is used to evaluate the performance of the proposed system. The adopted dataset consists of 20 patients for each diagnosis case including cognitive impairment, Alzheimer's disease and normal. Several evaluation measurements are used to evaluate the robustness of the proposed diagnosis system. The experimental results reveal the good performance of the proposed system.

Cognitive Functions in the Geriatric Population

This chapter describes how one of the challenging issues of clinical diagnosis is distinguishing between the cognitive deficits manifested in normal aging, depression, mild cognitive impairment (MCI) and dementia. The diagnostic challenge is that there is a great deal of overlap in the symptom constellations of these conditions. It is thus important to establish conceptual and clinical criteria with sufficient predictive validity to accurately identify differences and similarities in cognitive states to justify initiation of appropriate treatments.

Creutzfeldt-Jakob Disease

Creutzfeldt-Jakob disease (CJD) is a rare disease associated with neurodegeneration mostly characterized by damage to the neurons. CJD is caused by aggregation of misfolded proteins known as prions; thus, CJD is said to be a prion-related illness. CJD and other prion-related illnesses such as Kuru and Gerstmann-Sträussler-Scheinker disease (GSS) have been reported to have complex mechanisms due to their association with the brain and the nervous system in general. A lot of questions have been raised about the mechanism, diagnosis, and pathogenesis of this disease. The complexity of prion proteins themselves have contributed to more questions about the complications of CJD, whether misfolding of the prions are responsible for neurodegeneration or the misfolding are mere symptoms of the disease. This chapter attempts to explore some details about CJD and answers most related questions about the disease's mechanism. The author finally attempts to explore recent development in pathogenesis, diagnosis, and treatment of CJD.

Lewy Body Disease

Fritz Heinrich Lewy described the intracytoplasmic inclusions found in the neurons for the very first time. In 1919 these inclusions were termed as “LBs” by Tretiakoff. LBs were found in the brain of the patients suffering from Lewy body disease (LBD). LBD is characterized by the presence of Parkinsonian symptoms in the earlier stages and dementia in the later stages of the disease. LBs were classified on the basis of the region of the brain in which they are distributed and so is the case of the LBD means the type of the LBD depends on the anatomical areas of the brain involved. LBD is not a single disorder. It is a spectrum of disorders. This chapter addresses the entire profile of LBs, types, composition, formation, and various LB pathologies as well as diagnostic criteria and pharmacotherapy.

Amyloid Beta

Alzheimer's disease (AD), exhibiting accumulation of amyloid beta (Aβ) peptide as a foremost protagonist, is one of the top five causes of deaths. It is a neurodegenerative disorder (ND) that causes a progressive decline in memory and cognitive abilities. It is characterized by deposition of Aβ plaques and neurofibrillary tangles (NFTs) in the neurons, which in turn causes a decline in the brain acetylcholine levels. Aβ hypothesis is the most accepted hypothesis pertaining to the pathogenesis of AD. Amyloid Precursor Protein (APP) is constitutively present in brain and it is cleaved by three proteolytic enzymes (i.e., alpha, beta, and gamma secretases). Beta and gamma secretases cleave APP to form Aβ. Ubiquitin Proteasome System (UPS) is involved in the clearing of Aβ plaques. AD also involves impairment in UPS. The novel disease-modifying approaches involve inhibition of beta and gamma secretases. A number of clinical trials are going on worldwide with moieties targeting beta and gamma secretases. This chapter deals with an overview of APP and its enzymatic cleavage leading to AD.

Using Technology to Empower People With Multiple Sclerosis (MS) to Make Shared Decisions About Managing Their Own Healthcare

In 2014, the UK National Health Service (NHS) ‘Five Year Forward View' plan set out key objectives to reform the NHS, which included empowering the population as a whole (particularly those with long-term health conditions) to take more responsibility for managing their own healthcare and introducing initiatives to use technology to improve services and reduce costs. The “Long Term Plan” explains how the 2014 initiatives will be further developed. This chapter presents a review of literature on digital health information and information usability. It presents the key findings from a mixed methods study that explored how people with MS (PwMS) access and use health digital information when trying to manage their MS. While the study found that there is much good quality digital health information available for PwMS, and that this facilitates shared decisions, some necessary information is still missing. The chapter concludes with recommendations for digital health information providers.

Assessment of Graph Metrics and Lateralization of Brain Connectivity in Progression of Alzheimer's Disease Using fMRI

Alzheimer's disease (AD) is a progressive brain disorder which has a long preclinical phase. The beta-amyloid plaques and tangles in the brain are considered as the main pathological causes. Functional connectivity is typically examined in capturing brain network dynamics in AD. A definitive underconnectivity is observed in patients through the progressive stages of AD. Graph theoretic modeling approaches have been effective in understanding the brain dynamics. In this article, the brain connectivity patterns and the functional topology through the progression of Alzheimer's disease are analysed using resting state fMRI. The altered network topology is analysed by graphed theoretical measures and explains cognitive deficits caused by the progression of this disease. Results show that the functional topology is disrupted in the default mode network regions as the disease progresses in patients. Further, it is observed that there is a lack of left lateralization involving default mode network regions as the severity in AD increases.