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A minimum detectable signal is a signal at the input of a system whose power allows it to be detected over the background electronic noise of the detector system. It can alternately be defined as a signal that produces a signal-to-noise ratio of a given value m at the output. In practice, m is usually chosen to be greater than unity.
Frequency domain, polyphonic detection is possible, usually utilizing the periodogram to convert the signal to an estimate of the frequency spectrum [4].This requires more processing power as the desired accuracy increases, although the well-known efficiency of the FFT, a key part of the periodogram algorithm, makes it suitably efficient for many purposes.
Detection occurs when the cell under test exceeds the threshold. In most simple CFAR detection schemes, the threshold level is calculated by estimating the noise floor level around the cell under test (CUT). This can be found by taking a block of cells around the CUT and calculating the average power level.
Most analytical instruments produce a signal even when a blank (matrix without analyte) is analyzed.This signal is referred to as the noise level. The instrument detection limit (IDL) is the analyte concentration that is required to produce a signal greater than three times the standard deviation of the noise level.
The sensitivity index or discriminability index or detectability index is a dimensionless statistic used in signal detection theory. A higher index indicates that the signal can be more readily detected.
where PEAK I is the new peak found in the integrated signal. At the beginning of the QRS detection, a 2 seconds learning phase is needed to initialize SignalLevel I and NoiseLevel I as a percentage of the maximum and average amplitude of the integrated signal, respectively. If a new PEAK I is under the Threshold I, the noise level is updated.
In many practical signal processing problems, the objective is to estimate from measurements a set of constant parameters upon which the received signals depend. There have been several approaches to such problems including the so-called maximum likelihood (ML) method of Capon (1969) and Burg's maximum entropy (ME) method.
NeuroKit ("nk") is an open source toolbox for physiological signal processing. [1] The most recent version, NeuroKit2, is written in Python and is available from the PyPI package repository. [2] As of June 2022, the software was used in 94 scientific publications. [3]