{'fraction_non_alphanumeric': 0.013852242744063324, 'fraction_numerical': 0.0, 'mean_word_length': 5.217213114754099, '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}}
Arrhythmia classification in long-term data using relative RR intervals

Background: The automated heart beat detection and the classification of arrhythmic beats from ECG recordings are difficult tasks. Even thoroughly annotated standard databases have a wealth of misplaced heart beats and a visual identification of all affected ECG parts is time-consuming. The study of relative RR intervals can help in this regard. Methods & Results: Relative RR intervals are defined as the change of two successive RR intervals weighted by their mean. I have analyzed the return maps of relative RR intervals of the Normal Sinus Rhythm Database as well as the MIT-BIH Arrhythmia Database. Different structures have been investigated analytically and are reproducible due to modeling. I am able to distinguish structures regarding arrhythmia types and cases of insufficient heart beat detection. Upon that approach, I have developed filtering criteria of artifacts from RR interval sequences. Utilization: The return map of relative RR intervals can be used as visualization technique for the detection of arrhythmia types or failures in the automated beat detection. Further, it can be used to apply filtering criteria for the removal of irregular heart beats in RR interval data or to improve heart beat detection algorithms by applying a more sensitive method in the effected parts. This may help to distinguish irregular beats from false positive alarms caused by interference effects, possibly in postprocessing routines.