{'fraction_non_alphanumeric': 0.03192584963954686, 'fraction_numerical': 0.016477857878475798, 'mean_word_length': 4.785714285714286, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
K-complex identification in sleep EEG using MELM-GRBF classifier

K-complexes like spindles are hallmark patterns of stage 2 sleep. Due to correlation between these patterns and some diseases, it is necessary to develop algorithms to detect them. In this study, a new method is used to detect K-complexes automatically. 10 time-series and chaotic features were used in order to extract the K-complex waves from stage 2 sleep. To use the most effective features, feature space dimension is reduced with Sequential Forward Selection method. The reduced feature space is classified using Generalized Radial Basis Function Extreme Learning Machine (MELM-GRBF) algorithm. GRBFs make the modification of the RBF possible by adjusting a new parameter τ. We're applied this methodology to K-complex classification for the first time. The classifier gives noticeably better results compared to ELM-RBF method for sensitivity and accuracy 61.00 ± 6.6 and 96.15 ± 3.7, respectively.