Predictive Models in Diagnosis of Alzheimer’s Disease from EEG

The fluctuation of an EEG signal is a useful symptom of EEG quasi-stationarity. Linear predictive models of three types and their prediction error are studied via traditional and robust measures. The resulting EEG characteristics are applied to the diagnosis of Alzehimer’s disease. Our aim is to decide among: forward, backward, and predictive models, EEG channels, and also robust and non-robust variability measures, and then to find statistically significant measures for use in the diagnosis of Alzheimer’s disease from EEG

Age-related changes in EEG coherence

Background and purpose Coherence changes can reflect the pathophysiological processes involved in human ageing. We conducted a retrospective population study that sought to analyze the age-related changes in EEG coherence in a group of 17,722 healthy professional drivers. Materials and methods The EEGs were obtained using a standard 10–20 electrode configuration on the scalp. The recordings from 19 scalp electrodes were taken while the participants’ eyes were closed. The linear correlations between the age and coherence were estimated by linear regression analysis. Results Our results showed a significant decrease in coherence with age in the theta and alpha bands, and there was an increasing coherence with the beta bands. The most prominent changes occurred in the alpha bands. The delta bands contained movement artefacts, which most likely do not change with age. Conclusions The age-related EEG desynchrony can be partly explained by the age-related reduction of cortical connectivity. Higher frequencies of oscillations require less cortical area of high coherence. These findings explain why the lowest average coherence values were observed in the beta and sigma bands, as well as why the beta bands show borderline statistical significance and the sigma bands show non-significance. The age-dependent decrease in coherence may influence the estimation of age-related changes in EEG energy due to phase cancellation

Recognition of Patient Groups with Sleep Related Disorders using Bio-signal Processing and Deep Learning

Accurately diagnosing sleep disorders is essential for clinical assessments and treatments. Polysomnography (PSG) has long been used for detection of various sleep disorders. In this research, electrocardiography (ECG) and electromayography (EMG) have been used for recognition of breathing and movement-related sleep disorders. Bio-signal processing has been performed by extracting EMG features exploiting entropy and statistical moments, in addition to developing an iterative pulse peak detection algorithm using synchrosqueezed wavelet transform (SSWT) for reliable extraction of heart rate and breathing-related features from ECG. A deep learning framework has been designed to incorporate EMG and ECG features. The framework has been used to classify four groups: healthy subjects, patients with obstructive sleep apnea (OSA), patients with restless leg syndrome (RLS) and patients with both OSA and RLS. The proposed deep learning framework produced a mean accuracy of 72% and weighted F1 score of 0.57 across subjects for our formulated four-class problem

Trends in the treatment of risk factors for stroke in a Czech stroke unit

The goal of this study is to evaluate therapeutic trends for several diseases that represent risk factors for stroke. The relative frequency of therapy with compounds that influence the risk factors for stroke was monitored in a group of 3,290 patients who were hospitalised in the Stroke Unit at the University Hospital in Hradec Kralove between 2005 and 2012. For most drugs monitored, the reasons for the significant decrease or increase in use were causes other than the reduction of stroke risk. Despite this finding, the majority of statistically significant changes had, according to review of comparative studies, a posi- tive effect on prevention of stroke. Motivation to change treatment of stroke risk factors, such as hypertension, diabetes mellitus and hypercholesterolemia, was mainly aimed at sufficient disease management with a minimum of adverse effects. On the other hand, optimization of stroke recurrence and economic factors were motivations to treatment changes in prevention with antiplatelets. Antidiabetics were associated with an increase in met- formin use and reduction in insulin use. For antihyperten- sives, the most significant reduction was associated with the use of diuretics, although calcium channel blockers and beta-blockers are also less used. Additionally, the use of the ACE inhibitor ramipril increase

Computational Intelligence in Metric Analysis of the Skull in the Context of Maxillofacial Surgery

Anthropometric studies focusing on facial metrics and their proportions form an important research area devoted to observations of the appearance of the human skull. Many different applications include the use of craniometry for maxillofacial reconstruction and surgery. This paper explores the possibility of using selected craniometric points and associated metric to observe spatial changes during the maxillofacial surgery treatment. The experimental dataset includes observations of 27 individuals. The proposed method is associated with the processing of measurements by selected methods of signal processing and computational intelligence. The statistical results point to changes of facial measures before and after the maxillofacial surgery. The proposed method conclusively demonstrates that the area of the mean upper law triangle after surgical treatment is decreased by 8.5%, at the 5% significance level of the two-sample t-test. The classification of selected measurements by a neural network model reached an accuracy of 84.9%