{'fraction_non_alphanumeric': 0.04363312555654497, 'fraction_numerical': 0.034728406055209264, 'mean_word_length': 4.854166666666667, '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}}
Automated Identification of Paced Beats in Holter ECG

Background: Identification of paced beats is a necessity for Holter ECG monitoring reports. However, lower sampling frequencies make the pacing stimuli hard to identify. In this study, we present a method to distinguish between paced and non-paced beats. Method: One-hour ECG recordings (158 patients, single lead, 250 Hz) were recorded during usual daily activities. A total of 44,918 QRS complexes were detected and marked as paced (19,004) or non-paced (25,914). This dataset was split (60%, out-of-patient) to training and testing datasets. Three features based on amplitude envelopes in two frequency bands were used to build a logistic regression model. An additional external dataset (2,193 recordings with 16,941 QRS) was assessed at a different facility and was used for the cross-database test. Results: The model showed the test Fl-score of 0.93, cross-database test shown Fl-score of 0.924. Conclusion: The presented method recognizes paced and non-paced heartbeats in lower sampling frequency, even if it can hardly be visually observed in the raw signal.