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Gait analysis and the assessment of rehabilitation exercises are important processes that occur during fitness level monitoring and the treatment of neurological disorders. This paper presents the possibility of using oximetric, heart rate (HR), accelerometric, and global navigation satellite systems (GNSSs) to analyse signals recorded during uphill and downhill walking without and with a face mask to find its influence on physiological functions during selected walking patterns. The experimental dataset includes 86 signal segments acquired under different conditions. The proposed methodology is based on signal analysis in both the time and frequency domains. The results indicate that face mask use has a minimal effect on blood oxygen concentration and heart rate, with the average mean changes of these parameters being less than 2%. The support vector machine, a Bayesian method, the k-nearest neighbour method, and a two-layer neural network showed very good separation abilities and successfully classified different walking patterns only in the case when the effect of face mask wearing was not included in the classification process. Our methodology suggests that artificial intelligence and machine learning tools are efficient methods for the assessment of motion patterns in different motion conditions and that face masks have a negligible effect for short-duration experiments., [LTAIN19007]

Alzheimer’s disease is diagnosed via means of daily activity assessment. The EEG recording evaluation is a supporting tool that can assist the practitioner to recognize the illness, especially in the early stages. This paper presents a new approach for detecting Alzheimer’s disease and potentially mild cognitive impairment according to the measured EEG records. The proposed method evaluates the amount of novelty in the EEG signal as a feature for EEG record classification. The novelty is measured from the parameters of EEG signal adaptive filtration. A linear neuron with gradient descent adaptation was used as the filter in predictive settings. The extracted feature (novelty measure) is later classified to obtain Alzheimer’s disease diagnosis. The proposed approach was cross-validated on a dataset containing EEG records of 59 patients suffering from Alzheimer’s disease; seven patients with mild cognitive impairment (MCI) and 102 controls. The results of cross-validation yield 90.73% specificity and 89.51% sensitivity. The proposed method of feature extraction from EEG is completely new and can be used with any classifier for the diagnosis of Alzheimer’s disease from EEG records.

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., Paper is offered by the publisher as Open Acess: https://www.mdpi.com/1424-8220/20/9/2594

Multimodal signal analysis based on sophisticated noninvasive sensors, efficient communication systems, and machine learning, have a rapidly increasing range of different applications. The present paper is devoted to pattern recognition and the analysis of physiological data acquired by heart rate and thermal camera sensors during rehabilitation. A total number of 56 experimental data sets, each 40 min long, of the heart rate and breathing temperature recorded on an exercise bike have been processed to determine the fitness level and possible medical disorders. The proposed general methodology combines machine learning methods for the detection of the changing temperature ranges of the thermal camera and adaptive image processing methods to evaluate the frequency of breathing. To determine the individual temperature values, a neural network model with the sigmoidal and the probabilistic transfer function in the first and the second layers are applied. Appropriate statistical methods are then used to find the correspondence between the exercise activity and selected physiological functions. The evaluated mean delay of 21 s of the heart rate drop related to the change of the activity level corresponds to results obtained in real cycling conditions. Further results include the average value of the change of the breathing temperature (167 s) and breathing frequency (49 s).

The brain is a complex organ responsible for memory storage and reasoning; however, the mechanisms underlying these processes remain unknown. This paper forms a contribution to a lot of theoretical studies devoted to regular or chaotic oscillations of interconnected neurons assuming that the smallest information unit in the brain is not a neuron but, instead, a coupling of inhibitory and excitatory neurons forming a simple oscillator. Several coefficients of variation for peak intervals and correlation coefficients for peak interval histograms are evaluated and the sensitivity of such oscillator units is tested to changes in initial membrane potentials, interconnection signal delays, and changes in synaptic weights based on known histologically verified neuron couplings. Results present only a low dependence of oscillation patterns to changes in initial membrane potentials or interconnection signal delays in comparison to a strong sensitivity to changes in synaptic weights showing the stability and robustness of encoded oscillating patterns to signal outages or remoteness of interconnected neurons. Presented simulations prove that the selected neuronal couplings are able to produce a variety of different behavioural patterns, with periodicity ranging from milliseconds to thousands of milliseconds between the spikes. Many detected different intrinsic frequencies then support the idea of possibly large informational capacity of such memory units.

AIM To assess the safety and efficacy of mechanical thrombectomy (MT) with stent-retrievers in anterior circulation stroke (ACS) patients due to the occlusion of major cerebral arteries, and to compare the results achieved in patients on oral anticoagulation (OAC) and those not on OAC. MATERIALS AND METHODS The present retrospective study comprised 285 consecutive patients (115 males; mean age 74±13 years). The following data were collected: baseline characteristics, occurrence of risk factors, pre-event treatment with OAC, neurological deficit at the time of treatment, time to therapy, recanalisation rate (successful recanalisation defined as Thrombolysis in Cerebral Infarction score ≥2b), post-treatment imaging findings. The 90-day clinical outcome was assessed using modified Rankin scale (good outcome defined as 0–2). RESULTS The following statistically insignificant differences were found in 26 patients on OAC versus 259 patients without OAC: occurrence of symptomatic intracerebral haemorrhage 7.7% versus 8.1%, achievement of successful recanalisation 69.2% versus 82.6%, good 90-day clinical outcome 34.6% versus 56.8%, 90-day mortality 26.9% versus 20.8% (p>0.05 in all cases). Age and neurological deficit at the time of treatment were identified as independent negative predictors of good 90-day clinical outcome (odds ratio [OR]=0.90, 95% confidence interval [CI]: 0.88–0.94 and OR=0.83, 95% CI: 0.77–0.90, respectively) and as independent positive predictors of mortality (OR=1.12, 95% CI: 1.06–1.18 and, OR=1.17, 95% CI: 1.07–1.27, respectively; p CONCLUSIONS MT with stent-retrievers is safe and effective in ACS patients on OAC.

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.

Although some studies have described the pharmacokinetics and pharmacodynamics of donepezil in the peripheral compartment, studies focused on drug transport across the blood–brain barrier are still very rare. To our knowledge, the fluctuation in the cerebrospinal fluid concentration of donepezil after administration of the drug has not been described in the literature so far. We recruited 16 patients regularly taking a standard therapeutic dose of donepezil (10 mg per day). All patients (Caucasian race) were treated for at least three months with a stable dose of 10 mg per day prior to sample collection. Patients were divided into two groups depending on the time of plasma and cerebrospinal fluid sampling: 12 h (n = 9; 4 M/5F aged 78.68 ± 7.35 years) and 24 h (n = 7; 3 M/4F aged 77.14 ± 5.87 years) after donepezil administration. The cerebrospinal fluid sample was collected by standard lumbar puncture technique using a single-use traumatic needle. The samples were analysed on an Agilent 1260 Series liquid chromatograph comprising a degasser, a quaternary pump, a light-tight autosampler unit set, a thermostated column compartment, and a UV/VIS detector. Agilent ChemStation software, the statistical software Prism4, version 5.0 (GraphPad Software, USA), and IBM® SPSS® Statistics were used for the analysis of the results. The difference in plasma concentration of donepezil after 12 h (mean ± SEM; 39.99 ± 5.90 ng/ml) and after 24 h (29.38 ± 1.71 ng/ml) was nonsignificant. In contrast, the donepezil concentration in the cerebrospinal fluid was significantly higher in the 24-h interval (7.54 ± 0.55 ng/ml) compared with the 12-h interval (5.19 ± 0.83 ng/ml, which is ~70 % based on mean cerebrospinal fluid values). Based on these data, it is plausible to predict that donepezil might produce a stronger AChE inhibition in the brain at 24 h compared with 12 h following the administration. This information may help physicians individually adjust the time of drug administration in the patients according to time course of the disease symptoms.

Objective To evaluate the frequencies of symptoms and signs in patients with posterior circulation ischemia in a large case series of prospectively collected patients. Design Case series. Setting Outpatient and inpatient setting at the New England Medical Center, a tertiary care referral center in Boston, Massachusetts. Patients Consecutive sample of 407 adult patients who had stroke and/or transient ischemic attacks in the posterior circulation within 6 months of study inclusion. All patients were examined by senior stroke neurologists. All patients had either computed tomography or magnetic resonance imaging of the brain as well as vascular imaging of the head and neck. The study included 256 men (63%) and 151 women (37%). Main Outcome Measures Frequencies of posterior circulation ischemic symptoms and signs. These outcome measures were planned before data collection began. Correlations between symptoms and signs with separate vascular territories of the posterior circulation were then analyzed. Results The most frequent posterior circulation symptoms were dizziness (47%), unilateral limb weakness (41%), dysarthria (31%), headache (28%), and nausea or vomiting (27%). The most frequent signs were unilateral limb weakness (38%), gait ataxia (31%), unilateral limb ataxia (30%), dysarthria (28%), and nystagmus (24%). Logistic regression analysis reveals that the clinical features dysphagia (P = .004; 95% CI, 1.8-24.4), nausea or vomiting (P = .002; 95% CI, 1.6-8.2), dizziness (P = .047; 95% CI, 1.0-5.4), and Horner syndrome (P = .001; 95% CI, 2.4-26.6) were positively correlated with the proximal vascular territory. Unilateral limb weakness (P = .001; 95% CI, 1.7-8.7) and cranial nerve VII deficits (P = .02; 95% CI, 1.1-5.3) were positively correlated with the middle territory. Limb sensory deficit (P = .001; 95% CI, 1.8-7.8), lethargy (P = .001; 95% CI, 2.3-12.4), and visual field loss (P = .001; 95% CI, 5.3-23.9) were positively correlated with the distal territory. Conclusions We report the most frequent symptoms and signs in the largest published registry, the New England Medical Center Posterior Circulation Registry, of patients with posterior circulation ischemia who had complete neurological examinations and extensive cerebrovascular imaging. Knowledge of the vascular territory involved aids in the diagnosis of the causative vascular lesion and stroke mechanism.