fast sample entropy
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OBJECTIVE: High-frequency oscillations are considered among the most promising interictal biomarkers of the epileptogenic zone in patients suffering from pharmacoresistant focal epilepsy. However, there is no clear definition of pathological high-frequency oscillations, and the existing detectors vary in methodology, performance, and computational costs. This study proposes relative entropy as an easy-to-use novel interictal biomarker of the epileptic tissue. METHODS: We evaluated relative entropy and high-frequency oscillation biomarkers on intracranial electroencephalographic data from 39 patients with seizure-free postoperative outcome (Engel Ia) from three institutions. We tested their capability to localize the epileptogenic zone, defined as resected contacts located in the seizure onset zone. The performance was compared using areas under the receiver operating curves (AUROCs) and precision-recall curves. Then we tested whether a universal threshold can be used to delineate the epileptogenic zone across patients from different institutions. RESULTS: Relative entropy in the ripple band (80-250 Hz) achieved an average AUROC of .85. The normalized high-frequency oscillation rate in the ripple band showed an identical AUROC of .85. In contrast to high-frequency oscillations, relative entropy did not require any patient-level normalization and was easy and fast to calculate due to its clear and straightforward definition. One threshold could be set across different patients and institutions, because relative entropy is independent of signal amplitude and sampling frequency. SIGNIFICANCE: Although both relative entropy and high-frequency oscillations have a similar performance, relative entropy has significant advantages such as straightforward definition, computational speed, and universal interpatient threshold, making it an easy-to-use promising biomarker of the epileptogenic zone.
OBJECTIVE: Amyotrophic lateral sclerosis (ALS) is a heterogeneous disease with a complex etiology that lacks biomarkers predicting disease progression. The objective of this study was to use longitudinal cerebrospinal fluid (CSF) samples to identify biomarkers that distinguish fast progression (FP) from slow progression (SP) and assess their temporal response. METHODS: We utilized mass spectrometry (MS)-based proteomics to identify candidate biomarkers using longitudinal CSF from a discovery cohort of SP and FP ALS patients. Immunoassays were used to quantify and validate levels of the top biomarkers. A state-transition mathematical model was created using the longitudinal MS data that also predicted FP versus SP. RESULTS: We identified a total of 1148 proteins in the CSF of all ALS patients. Pathway analysis determined enrichment of pathways related to complement and coagulation cascades in FPs and synaptogenesis and glucose metabolism in SPs. Longitudinal analysis revealed a panel of 59 candidate markers that could segregate FP and SP ALS. Based on multivariate analysis, we identified three biomarkers (F12, RBP4, and SERPINA4) as top candidates that segregate ALS based on rate of disease progression. These proteins were validated in the discovery and a separate validation cohort. Our state-transition model determined that the overall variance of the proteome over time was predictive of the disease progression rate. INTERPRETATION: We identified pathways and protein biomarkers that distinguish rate of ALS disease progression. A mathematical model of the CSF proteome determined that the change in entropy of the proteome over time was predictive of FP versus SP.
- MeSH
- amyotrofická laterální skleróza * MeSH
- biologické markery mozkomíšní mok MeSH
- lidé MeSH
- plazmatické proteiny vázající retinol MeSH
- progrese nemoci MeSH
- proteom metabolismus MeSH
- proteomika metody MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Research Support, N.I.H., Extramural MeSH
The aim of our study was to evaluate rapid insulin pulses and insulin secretion regularity in fasting state in lean women with polycystic ovary syndrome (PCOS) in comparison to lean healthy women. PCOS (n=8) and controls (n=7) underwent every minute blood sampling for 60 min. Insulin pulsatility was assessed by deconvolution and insulin secretion regularity by approximate entropy methodology. PCOS had higher testosterone (p<0.02), prolactin (p<0.05) and lower sex hormone binding globulin (SHBG) (p<0.0006) levels than controls. Approximate entropy, insulin pulse frequency, mass, amplitude and interpulse interval did not differ between PCOS and controls. PCOS had broader insulin peaks determined by a common half-duration (p<0.07). Burst mass correlated positively with testosterone (p<0.05) and negatively with SHBG (p 0.0004) and common half-duration correlated positively with prolactin (p<0.008) and cortisol levels (p<0.03). Approximate entropy positively correlated with BMI (p<0.04) and prolactin (p<0.03). Lean PCOS patients tended to have broader insulin peaks in comparison to healthy controls. Prolactin, androgens and cortisol might participate in alteration of insulin secretion in PCOS-affected women. Body weight and prolactin levels could influence insulin secretion regularity.
- MeSH
- diabetes mellitus 2. typu metabolismus MeSH
- dospělí MeSH
- globulin vázající pohlavní hormony metabolismus MeSH
- hydrokortison krev MeSH
- inzulin krev sekrece MeSH
- inzulinová rezistence fyziologie MeSH
- lidé MeSH
- omezení příjmu potravy MeSH
- prolaktin krev MeSH
- pulzatilní průtok MeSH
- syndrom polycystických ovarií metabolismus MeSH
- tělesná hmotnost fyziologie MeSH
- testosteron krev MeSH
- Check Tag
- dospělí MeSH
- lidé MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
... Fourier Transform 600 -- 12.0 Introduction 600 -- 12.1 Fourier Transform of Discretely Sampled Data ... ... 605 -- 12.2 Fast Fourier Transform (FFT) 608 -- 12.3 FFT of Real Functions 617 -- 12.4 Fast Sine and ... ... Linear Prediction and Linear Predictive Coding 673 -- 13.7 Power Spectrum Estimation by the Maximum Entropy ... ... (All-Poles) -- Method 681 -- 13.8 Spectral Analysis of Unevenly Sampled Data 685 -- 13.9 Computing Fourier ... ... Linear Regularization Methods 1006 -- 19.6 Backus-Gilbert Method 1014 -- Contents -- 19.7 Maximum Entropy ...
3rd ed. xxi, 1235 s. : il. ; 27 cm + 1 CD-ROM
- MeSH
- matematické výpočty počítačové MeSH
- matematika MeSH
- numerická analýza pomocí počítače * MeSH
- Publikační typ
- monografie MeSH
