Table 2.

Review and comparison of characteristics of previously published automated sleep-scoring approaches using invasive electrodes.

Authors	Year	Type of analysis	Species	User-user	Auto-visual
van Luijtelaar et al.,³⁶	1984	Computer based automated spectral analysis	Rat	93%	93.6%
Mamelak et al.,²¹	1988	Period-amplitude analysis using artificial neural networks (ANN)	Cat	N/A	90%
Benington et al.,³	1994	Computer based algorithm based on thresholding The amount of delta, theta and sigma power.	Rat	N/A	~91%
Crisler et al.,⁸	2008	107 frequency and time parameters were extracted from ECOG and EMG to train support vector machine (SVM)	Rat	86.3%	96%
Kohtoh et al.,¹⁵	2008	Fast Fourier transform power spectrum analysis	Rat	N/A	90%
Stephenso n et al.,³⁴	2009	Automated system (ratSAS) to reject artifacts and differentiate sleep states	Rat	N/A	Wake=92% NREM 88% REM 80%.
Rytkonen et al.,²⁶	2011	Open source MATLAB algorithm using naive Bayes classifier	Rats and mice	88–92%	98%