INVESTIGATION OF OCULAR ARTEFACTS IN THE HUMAN EEG AND THEIR REMOVAL BY A MICROPROCESSOR-BASED INSTRUMENT

The Electroencephalogram (EEG) is widely used in clinical and psychological situations, but it is often seriously obscured by ocular artefacts (OAs) resulting from movements in the ocular system (eyeball, eyelids etc). 'The work described in this thesis is concerned with the problems of OAs in the human EEG, their removal both off-line and on-line, and the design and development of an on-line OA removal system, together with a critical review of the literature on the subject. The work of Jervis and his co-workers was extended to further study OAs, to obtain improved measures of the effectiveness of OA removal, and to find the most effective model for removing OA on-line. A number of criteria were devised to compare the performance of several models, including a more reliable pictorial method. It was found unnecessary to use the vertical and horizontal EOGs for both eyes (ie. four EOGs) in a removal model, as previously reported. This was shown to be due to strong correlation between the EOGs. It was shown that the assumption of uncorrelated error terms, implicit in present removal models, is invalid. To remedy this, the error terms were modelled as an autoregressive series. New on-line removal algorithms based on numerically stable factorization algorithms were developed. Compared to the present on-line methods the algorithms are superior, requiring no subjective manual adjustments, or the co-operation of subjects which cannot always be guarranteed. The algorithms were shown to give similar results to their off-line equivalents. A simpler algorithm based on the present on-line method is also proposed as an alternative, but may lead to a reduced performance. An important part of this research lay in the application of the results to the design and development of a new automatic OA removal system utilizing the algorithms described above.Department of Neurological Sciences, Freedom Fields Hospital, Plymout

Welcome to Source Code for Biology and Medicine

This editorial introduces Source Code for Biology and Medicine, a new journal for publication of programming source code used in biology and medicine. Source Code for Biology and Medicine is an open access independent journal published by BioMed Central. We describe the journal aims, scope, benefits of open access, article processing charges, competing interests, content and article format, peer review policy and publication, and introduce the Editorial Board

Early Detection of Alzheimer's Disease with Blood Plasma Proteins using Support Vector Machines

The successful development of amyloid-based biomarkers and tests for Alzheimer's disease (AD) represents an important milestone in AD diagnosis. However, two major limitations remain. Amyloid-based diagnostic biomarkers and tests provide limited information about the disease process and they are unable to identify individuals with the disease before significant amyloid-beta accumulation in the brain develops. The objective in this study is to develop a method to identify potential blood-based non-amyloid biomarkers for early AD detection. The use of blood is attractive because it is accessible and relatively inexpensive. Our method is mainly based on machine learning (ML) techniques (support vector machines in particular) because of their ability to create multivariable models by learning patterns from complex data. Using novel feature selection and evaluation modalities, we identified 5 novel panels of non-amyloid proteins with the potential to serve as biomarkers of early AD. In particular, we found that the combination of A2M, ApoE, BNP, Eot3, RAGE and SGOT may be a key biomarker profile of early disease. Disease detection models based on the identified panels achieved sensitivity (SN) > 80%, specificity (SP) > 70%, and area under receiver operating curve (AUC) of at least 0.80 at prodromal stage (with higher performance at later stages) of the disease. Existing ML models performed poorly in comparison at this stage of the disease, suggesting that the underlying protein panels may not be suitable for early disease detection. Our results demonstrate the feasibility of early detection of AD using non-amyloid based biomarkers

Electroencephalogram Based Biomarkers for Detection of Alzheimer’s Disease

Alzheimer’s disease (AD) is an age-related progressive and neurodegenerative disorder, which is characterized by loss of memory and cognitive decline. It is the main cause of disability among older people. The rapid increase in the number of people living with AD and other forms of dementia due to the aging population represents a major challenge to health and social care systems worldwide. Degeneration of brain cells due to AD starts many years before the clinical manifestations become clear. Early diagnosis of AD will contribute to the development of effective treatments that could slow, stop, or prevent significant cognitive decline. Consequently, early diagnosis of AD may also be valuable in detecting patients with dementia who have not obtained a formal early diagnosis, and this may provide them with a chance to access suitable healthcare facilities. An early diagnosis biomarker capable of measuring brain cell degeneration due to AD would be valuable. Potentially, electroencephalogram (EEG) can play a valuable role in the early diagnosis of AD. EEG is noninvasive and low cost, and provides valuable information about brain dynamics in AD. Thus, EEG-based biomarkers may be used as a first-line decision-support tool in AD diagnosis and could complement other AD biomarkers

Bioprofile Analysis:A New Approach for the Analysis of Biomedical Data in Alzheimer's Disease

This article presents a new approach for the analysis of biomedical data to support the management and care of patients with Alzheimer's disease (AD). The increase in prevalence of neurodegenerative disorders such as AD has led to a need for objective means to assist clinicians with the analysis and interpretation of complex biomedical data. To this end, we propose a "Bioprofile" analysis to reveal the pattern of disease in the subject's biodata. From the Bioprofile, personal "Bioindices" that indicate how closely a subject's data resemble the pattern of AD can be derived. We used an unsupervised technique (k-means) to cluster variables of the ADNI database so that subjects are divisible into those with the Bioprofile of AD and those without it. Results revealed that there is an "AD pattern" in the biodata of most AD and mild cognitive impairment (MCI) patients and some controls. This pattern agrees with a recent hypothetical model of AD evolution. We also assessed how the Bioindices changed with time and we found that the Bioprofile was associated with the risk of progressing from MCI to AD. Hence, the Bioprofile analysis is a promising methodology that may potentially provide a complementary new way of interpreting biomedical data. Furthermore, the concept of the Bioprofile could be extended to other neurodegenerative diseases.</p

Machine Learning-Based Method for Personalized and Cost-Effective Detection of Alzheimer's Disease

Diagnosis of Alzheimer's disease (AD) is often difficult, especially early in the disease process at the stage of mild cognitive impairment (MCI). Yet, it is at this stage that treatment is most likely to be effective, so there would be great advantages in improving the diagnosis process. We describe and test a machine learning approach for personalized and cost-effective diagnosis of AD. It uses locally weighted learning to tailor a classifier model to each patient and computes the sequence of biomarkers most informative or cost-effective to diagnose patients. Using ADNI data, we classified AD versus controls and MCI patients who progressed to AD within a year, against those who did not. The approach performed similarly to considering all data at once, while significantly reducing the number (and cost) of the biomarkers needed to achieve a confident diagnosis for each patient. Thus, it may contribute to a personalized and effective detection of AD, and may prove useful in clinical settings.</p

QualitySDN: Improving Video Quality using MPTCP and Segment Routing in SDN/NFV

In this paper, we present a novel QoE-aware SDN/NFV system by utilizing and integrating Multi-path TCP (MPTCP) and Segment Routing (SR) paradigms. We propose a QoE-based Multipath Source Routing (QoEMuSoRo) algorithm that achieve an optimized end-to-end QoE for the end-user by forwarding MPTCP subflows using SR over SDN/NFV. We implement and validate the proposed scheme through DASH experiments using Mininet and POX controller. To demonstrate the effectiveness of our proposal, we compare the performance of our QoE-aware MPTCP SDN/NFV SR-based proposal, the MPTCP and regular TCP in terms of system throughput and the end-user's QoE. Preliminary results shows that, our approach outperforms the other aforementioned methods