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. Author manuscript; available in PMC: 2020 Mar 15.
Published in final edited form as: J Neurosci Methods. 2019 Jan 30;316:12–21. doi: 10.1016/j.jneumeth.2019.01.009

Table 3:

Comparison of detection methods for all data

Method Mean
percentage of
human-scored
spindles		 Mean
length [s]		 Mean F1 False
discovery
rate		 False
negative
rate		 CPU*
time [s]
per
recording
Mölle 105.0457 0.4871 0.4871 0.2856 0.5994 30.5645
Martin 141.9600 0.4754 0.4754 0.3427 0.5441 2.5615
Andrillon 46.3362 0.4028 0.4028 0.2078 0.7022 0.3922
Hagler 116.2967 0.4591 0.4591 0.2963 0.6225 1.8177
DDA 89.8979 0.4970 0.4970 0.3861 0.4969 1.6389
*

All methods were implemented in MATLAB 9.4 (R2018a) and tested on the same 12-core (Intel Xeon X5690 @ 3.47 GHz) system. The DDA detector calls an executable written in C for a key step in the procedure.