K-complex Detection Using Fourier Spectrum Analysis In EEG

TL;DR

This paper introduces two novel Fourier spectrum analysis methods for automatic K-complex detection in EEG, achieving comparable or better accuracy than neural network-based approaches with less computational effort.

Contribution

It proposes two new Fourier-based K-complex detection methods and a new set of metrics for evaluation, avoiding neural networks and improving efficiency.

Findings

Methods achieve similar or better detection quality than neural network approaches.

Proposed methods require less computational power.

New evaluation metrics better reflect detection quality.

Abstract

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.