{'fraction_non_alphanumeric': 0.03577106518282989, 'fraction_numerical': 0.010333863275039745, 'mean_word_length': 4.910798122065728, '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}}
Electrocardiogram-based detection of central sleep apnea: a full-record signal processing approach

The objective of this paper was to propose an electrocardiogram-based, full-record signal processing approach for detection of central sleep apnea (CSA). We designed an experimental setup on healthy human subjects and patients. We achieved high detection resolution of begin and end times of CSA through a novel sample-wise, threshold-less, automatic algorithm. Compared to the current research trends, we did not divide data into epochs, but rather process the full signal. We extracted a respiratory signal from the electrocardiogram (i.e., EDR). We performed spectral analysis, namely spectrogram, and extracted relevant frequency-domain features. Among them, the frequency at which the maximum power occurs is proposed for central sleep apnea labeling over time. Specifically, we found that the drops to 0 Hz are key features. By using their time location and time length we were able to achieve a (second-wise) accuracy of 87.96%, a sensitivity of 99.30% and a specificity of 97.96% on patients. These results prove that CSAs might be detected from electrocardiogram with performance similar to that of the state of the art, with no need of data epochs.