An original multivariate multi-scale methodology for assessing the complexity of physiological signals is proposed. The technique is able to incorporate the simultaneous analysis of multi-channel ...data as a unique block within a multi-scale framework. The basic complexity measure is done by using Permutation Entropy, a methodology for time series processing based on ordinal analysis. Permutation Entropy is conceptually simple, structurally robust to noise and artifacts, computationally very fast, which is relevant for designing portable diagnostics. Since time series derived from biological systems show structures on multiple spatial-temporal scales, the proposed technique can be useful for other types of biomedical signal analysis. In this work, the possibility of distinguish among the brain states related to Alzheimer’s disease patients and Mild Cognitive Impaired subjects from normal healthy elderly is checked on a real, although quite limited, experimental database.
Alzheimer's disease (AD) impact is rapidly growing in western countries. The unavoidable progression of the disease, call for reliable ways to diagnose the AD in its early stages. Recently, it has ...been shown that the electroencephalography (EEG) complexity analysis could be used to predict the conversion from mild cognitive impairment (MCI) to AD. Despite the EEG analysis does not achieve yet the required clinical performance in terms of both sensitivity and specificity to be accepted as a clinically reliable technique of screening, the researchers count on the easiness and the non-invasiveness of the EEG measuring system. The aim of this paper is to analyze the efficacy of entropic complexity measures as a possible bio-marker to distinguish among the brain states related to the AD patients and MCI subjects from normal healthy elderly. The research is carried out on an experimental database. Three different emerging measures of complexity are compared, namely, permutation entropy, sample entropy, and Lempel-Ziv complexity. Because time series derived from biological systems show structures on multiple spatial-temporal scales and there exists a significant inter-channel correlation among the EEG channels, a multiscale multivariate approach is also implemented. Limited to the analyzed data, the results show that the severity of the AD reflects in the EEG dynamic complexity leaving the hope of early diagnosis based on simple EEG.
The respiratory signal can be accurately evaluated by single-channel electrocardiogram (ECG) processing, as shown in recent literature. Indirect methods to derive the respiratory signal from ECG can ...benefit from a simultaneous study of both respiratory and cardiac activities. These methods lead to major advantages such as low cost, high efficiency, and continuous noninvasive respiratory monitoring. The aim of this paper is to reconstruct the waveform of the respiratory signal by processing single-channel ECG. To achieve these goals, two techniques of decomposition of the ECG signal into suitable bases of functions are proposed, such as the empirical mode decomposition (EMD) and the wavelet analysis. The results highlight the main differences between them in terms of both theoretical foundations, and performance achieved by applying these algorithms to extract the respiratory waveform shape from single-channel ECG are presented. The results also show that both algorithms are able to reconstruct the respiratory waveform, although the EMD is able to break down the original signal without a preselected basis function, as it is necessary for wavelet decomposition. The EMD outperforms the wavelet approach. Some results on experimental data are presented.
A complex network approach is combined with time dynamics in order to conduct a space-time analysis applicable to longitudinal studies aimed to characterize the progression of Alzheimer's disease ...(AD) in individual patients. The network analysis reveals how patient-specific patterns are associated with disease progression, also capturing the widespread effect of local disruptions. This longitudinal study is carried out on resting electroence phalography (EEGs) of seven AD patients. The test is repeated after a three months' period. The proposed methodology allows to extract some averaged information and regularities on the patients' cohort and to quantify concisely the disease evolution. From the functional viewpoint, the progression of AD is shown to be characterized by a loss of connected areas here measured in terms of network parameters (characteristic path length, clustering coefficient, global efficiency, degree of connectivity and connectivity density). The differences found between baseline and at follow-up are statistically significant. Finally, an original topographic multiscale approach is proposed that yields additional results.
Alzheimer's disease (AD) is considered to be the most common and the fastest growing neurological disease in the world. Biomarker tools for early diagnosis and disease progression in AD remain key ...issues for clinical applications (patient's control and monitoring), sanitary systems (growth of prevalence of AD in the near future), and pharmaceutical companies (drug developments). Electroencephalogram (EEG) yields, in principle, a powerful and relatively cheap way for screening of dementia and AD in their early stages although not reaching the specificity prescribed for clinical use. Portable EEG systems based on wireless sensors can be used for unobtrusive long term monitoring provided they can solve technological problems (e.g., size and operational lifetime of batteries). Clinical applications require intensive recording of massive EEG data, raising the need for efficient, and flexible compression techniques. In this paper, this compression problem is viewed from a different perspective: is it possible to imagine a compressive sampling/sensing system that not only can reduce the throughput data but can also discriminate among brain states? If this is the case, it would be possible to distinguish among AD patients, mild cognitive impaired (MCI) subjects and normal healthy elderly. Accordingly, this paper investigates the performance of EEG compression techniques to recover data with the quality required by clinical constraints when making an assessment of the patients' status simultaneously. A sparsity coefficient is shown to be a good marker of AD being able to differentiate AD EEG from both MCI and healthy controls. The research, carried out on an experimental database, also compare the thresholding wavelet approach to the emerging method of compressive sensing. The results show the relationships between the compression ratio and the entropic measures of EEG complexity, whose reduction is considered the hallmark of AD onset.
Epileptic seizures are thought to be generated and to evolve through an underlying anomaly of synchronization in the activity of groups of neuronal populations. The related dynamic scenario of state ...transitions is revealed by detecting changes in the dynamical properties of Electroencephalography (EEG) signals. The recruitment procedure ending with the crisis can be explored through a spatial-temporal plot from which to extract suitable descriptors that are able to monitor and quantify the evolving synchronization level from the EEG tracings. In this paper, a spatial-temporal analysis of EEG recordings based on the concept of permutation entropy (PE) is proposed. The performance of PE are tested on a database of 24 patients affected by absence (generalized) seizures. The results achieved are compared to the dynamical behavior of the EEG of 40 healthy subjects. Being PE a feature which is dependent on two parameters, an extensive study of the sensitivity of the performance of PE with respect to the parameters' setting was carried out on scalp EEG. Once the optimal PE configuration was determined, its ability to detect the different brain states was evaluated. According to the results here presented, it seems that the widely accepted model of "jump" transition to absence seizure should be in some cases coupled (or substituted) by a gradual transition model characteristic of self-organizing networks. Indeed, it appears that the transition to the epileptic status is heralded before the preictal state, ever since the interictal stages. As a matter of fact, within the limits of the analyzed database, the frontal-temporal scalp areas appear constantly associated to PE levels higher compared to the remaining electrodes, whereas the parieto-occipital areas appear associated to lower PE values. The EEG of healthy subjects neither shows any similar dynamic behavior nor exhibits any recurrent portrait in PE topography.
Low testosterone (T) levels are often found in obese men with impaired glucose tolerance (IGT) and overt type 2 diabetes (T2DM); however, the mechanisms underlying this condition and its correct ...therapy are still under debate.
To evaluate the effectiveness of clomiphene citrate (CC) in increasing endogenous T levels in obese men with low serum T and with IGT or T2DM treated with metformin (MET).
Cross-over, randomized, double-blind, placebo-controlled study.
24 obese men, aged 47.3 ±. 6.3 (range 35-55 years), with low T level (≤3 ng/mL) and naïve diagnosis of IGT or T2DM were included. Subjects were randomized to CC 25 mg/day or placebo (Plac) with MET 2 g/day for 3 months. After a 6-week wash-out period, subjects were moved to the alternative arm for additional 3 months. Clinical evaluation and blood exams performed prior to and at the end of treatment.
Of 24 randomized, 21 were evaluable, classified as IGT (n = 11) or T2DM (n = 10). Compared to baseline levels, T levels increased significantly after 3 months of CC treatment (3.03±0.80 to 5.99±1.67 ng/mL P<0.001) but not after the Plac treatment (2.87±0.78 to 3.09±0.84 ng/mL P<0.001 between the treatments). T changes were similar in IGT and T2DM subjects. Gonadotropins as well raised significantly after CC treatment (LH 3.83±1.45 to 8.53±6.40 mU/mL; FSH 4.84±1.67 to 10.15±5.08 mU/mL P<0.001 respectively), whereas no changes for LH (3.51±1.59 to 3.63±1.39 mU/mL) but a smooth increased for FSH (4.61±2.49 to 5.39±2.65 mU/mL; P = 0.004) were shown after Plac treatment (LH P = 0.001 and FSH P = 0.002 between treatments). Furthermore, fasting glucose (106.8±23.2 to 101.1±25.7 mg/dL; P = 0.004), insulin (19.3±12.1 to 15.6±10.1 μU/mL; P = 0.010) and HOMA-IR (4.94±2.89 to 3.69±2.12; P = 0.001) decreased significantly during the CC treatment period, whereas no significant changes were observed in any of these parameters in the Plac treatment.
A low dose of CC therapy was able to significantly increase serum T levels in all participants with mild modifications of clinical and metabolic parameters.
EudraCT 2011-000439-10.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The epilepsies are commonly accompanied by widespread abnormalities in cerebral white matter. ENIGMA-Epilepsy is a large quantitative brain imaging consortium, aggregating data to investigate ...patterns of neuroimaging abnormalities in common epilepsy syndromes, including temporal lobe epilepsy, extratemporal epilepsy, and genetic generalized epilepsy. Our goal was to rank the most robust white matter microstructural differences across and within syndromes in a multicentre sample of adult epilepsy patients. Diffusion-weighted MRI data were analysed from 1069 healthy controls and 1249 patients: temporal lobe epilepsy with hippocampal sclerosis (n = 599), temporal lobe epilepsy with normal MRI (n = 275), genetic generalized epilepsy (n = 182) and non-lesional extratemporal epilepsy (n = 193). A harmonized protocol using tract-based spatial statistics was used to derive skeletonized maps of fractional anisotropy and mean diffusivity for each participant, and fibre tracts were segmented using a diffusion MRI atlas. Data were harmonized to correct for scanner-specific variations in diffusion measures using a batch-effect correction tool (ComBat). Analyses of covariance, adjusting for age and sex, examined differences between each epilepsy syndrome and controls for each white matter tract (Bonferroni corrected at P < 0.001). Across 'all epilepsies' lower fractional anisotropy was observed in most fibre tracts with small to medium effect sizes, especially in the corpus callosum, cingulum and external capsule. There were also less robust increases in mean diffusivity. Syndrome-specific fractional anisotropy and mean diffusivity differences were most pronounced in patients with hippocampal sclerosis in the ipsilateral parahippocampal cingulum and external capsule, with smaller effects across most other tracts. Individuals with temporal lobe epilepsy and normal MRI showed a similar pattern of greater ipsilateral than contralateral abnormalities, but less marked than those in patients with hippocampal sclerosis. Patients with generalized and extratemporal epilepsies had pronounced reductions in fractional anisotropy in the corpus callosum, corona radiata and external capsule, and increased mean diffusivity of the anterior corona radiata. Earlier age of seizure onset and longer disease duration were associated with a greater extent of diffusion abnormalities in patients with hippocampal sclerosis. We demonstrate microstructural abnormalities across major association, commissural, and projection fibres in a large multicentre study of epilepsy. Overall, patients with epilepsy showed white matter abnormalities in the corpus callosum, cingulum and external capsule, with differing severity across epilepsy syndromes. These data further define the spectrum of white matter abnormalities in common epilepsy syndromes, yielding more detailed insights into pathological substrates that may explain cognitive and psychiatric co-morbidities and be used to guide biomarker studies of treatment outcomes and/or genetic research.
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