{'fraction_non_alphanumeric': 0.013440860215053764, 'fraction_numerical': 0.013440860215053764, 'mean_word_length': 4.730769230769231, '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}}
Classification of Branch Block Beats using Higher Order Spectral Analysis and Neural Networks

In this study, it is aimed to classify branch block beats. A total number of 6170 beats related to 3 types of classes are extracted from MIT/BIH arrhythmia database and their bispectrums are calculated using TOR method. The area defined by the frequency values where the value of the energy of bispectrum is 95% of the maximum value in both axes is calculated. This area information is used as a one dimensional feature vector to feed the neural network designed as a classifier. The overall performance of the system is calculated as 94.2%. This study shows that higher order spectral analysis is a promising tool for arrhythmia beat classification