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The Neurophysiological Biomarker Toolbox (NBT) is an open source MATLAB toolbox for the computation and integration of neurophysiological biomarkers (e.g., biomarkers based on EEG or MEG recordings). [1] The NBT toolbox has so far been used in seven peer-reviewed research articles, and has a broad user base of more than 1000 users. [2]
To see the brain's response to a stimulus, the experimenter must conduct many trials and average the results together, causing random brain activity to be averaged out and the relevant waveform to remain, called the ERP. [6] [7]
In clinical practice, post-test probabilities are often just estimated or even guessed. This is usually acceptable in the finding of a pathognomonic sign or symptom, in which case it is almost certain that the target condition is present; or in the absence of finding a sine qua non sign or symptom, in which case it is almost certain that the target condition is absent.
The number of points accessible is multiplied by the portion of surface area each point represents to calculate the ASA. The choice of the 'probe radius' does have an effect on the observed surface area, as using a smaller probe radius detects more surface details and therefore reports a larger surface.
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To complement the CHADS 2 score, by the inclusion of additional 'stroke risk modifier' risk factors, the CHA 2 DS 2-VASc-score has been proposed. [8]In clinical use, the CHADS 2 score has been superseded by the CHA 2 DS 2-VASc score, which gives a better stratification of low-risk patients.
For the figure that shows high sensitivity and low specificity, there are 3 FN and 8 FP. Using the fact that positive results = true positives (TP) + FP, we get TP = positive results - FP, or TP = 40 - 8 = 32. The number of sick people in the data set is equal to TP + FN, or 32 + 3 = 35. The sensitivity is therefore 32 / 35 = 91.4%.
In evidence-based medicine, likelihood ratios are used for assessing the value of performing a diagnostic test.They use the sensitivity and specificity of the test to determine whether a test result usefully changes the probability that a condition (such as a disease state) exists.