  K-complexes are an important marker of brain activity and are used both in
clinical practice to perform sleep scoring, and in research. However, due to
the size of electroencephalography (EEG) records, as well as the subjective
nature of K-complex detection performed by somnologists, it is reasonable to
automate K-complex detection. Previous works in this field of research have
relied on the values of true positive rate and false positive rate to quantify
the effectiveness of proposed methods, however this set of metrics may be
misleading. The objective of the present research is to find a more accurate
set of metrics and use them to develop a new method of K-complex detection,
which would not rely on neural networks. Thus, the present article proposes two
new methods for K-complex detection based on the fast Fourier transform. The
results achieved demonstrated that the proposed methods offered a quality of
K-complex detection that is either similar or superior to the quality of the
methods demonstrated in previous works, including the methods employing neural
networks, while requiring less computational power, meaning that K-complex
detection does not require the use of neural networks. The proposed methods
were evaluated using a new set of metrics, which is more representative of the
quality of K-complex detection.
