{'fraction_non_alphanumeric': 0.01155115511551155, 'fraction_numerical': 0.0, 'mean_word_length': 5.664835164835165, '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 Cardiac Arrhythmias using Machine learning techniques based on ECG Signal Matching

an electrocardiogram (ECG) signal becomes very important in diagnosing cardiac arrhythmias. The techniques that classify cardiac arrhythmias based on ECG-features still have poor accuracy and much learning time. In this paper, we proposed framework to drive the best classification technique that builds an accurate model for classifying cardiac arrhythmias based on ECG-signal matching. Our framework is consisted of two steps, the preprocessing step which is introduced to match the ECG-templates of the arrhythmias with ECG window beats to find the similarity between them and store these similarities in form of numeric dataset to be used later with classification techniques. The classification step uses four classification techniques and applied in the MITBIH-arrhythmias database to classify the numeric dataset so as to drive best technique. Our results proved that decision trees classifier has higher accuracy and lower learning time than other techniques and classification cardiac arrhythmias based on signal matching is better than classification cardiac arrhythmias based on features extraction.