2019
DOI: 10.1038/s41598-019-53403-y
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Estimation of the apnea-hypopnea index in a heterogeneous sleep-disordered population using optimised cardiovascular features

Abstract: Obstructive sleep apnea (OSA) is a highly prevalent sleep disorder, which results in daytime symptoms, a reduced quality of life as well as long-term negative health consequences. OSA diagnosis and severity rating is typically based on the apnea-hypopnea index (AHI) retrieved from overnight poly(somno)graphy. However, polysomnography is costly, obtrusive and not suitable for long-term recordings. Here, we present a method for unobtrusive estimation of the AHI using ECG-based features to detect OSA-related even… Show more

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Cited by 14 publications
(19 citation statements)
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References 82 publications
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“…The sinus rhythm pulses of good quality were then used to derive the inter-beat intervals (IBIs) necessary for the HRV analysis. Besides the rejection of pulses based on the pulse quality index, we removed an IBI and its preceding IBI when their ratio was larger than 1.5, due to the suspicion of being related to ectopic beats 27 . We derived the amplitude of each sinus rhythm pulse to extract a surrogate respiratory activity signal 15 and, finally, from this surrogate determined the length and amplitude of each breath.…”
Section: Methodsmentioning
confidence: 99%
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“…The sinus rhythm pulses of good quality were then used to derive the inter-beat intervals (IBIs) necessary for the HRV analysis. Besides the rejection of pulses based on the pulse quality index, we removed an IBI and its preceding IBI when their ratio was larger than 1.5, due to the suspicion of being related to ectopic beats 27 . We derived the amplitude of each sinus rhythm pulse to extract a surrogate respiratory activity signal 15 and, finally, from this surrogate determined the length and amplitude of each breath.…”
Section: Methodsmentioning
confidence: 99%
“…Our deep learning model had the task of classifying 30-s epochs of each overnight recording as influenced by a respiratory event (RE-epochs, positive class) or not (non-RE epochs, negative class). Similar to our previous research, a 30-s epoch was labelled as RE-epoch if it includes at least 10 s of a respiratory event or if the beginning of an epoch is closer than 5 s to the ending of a respiratory event 27 . The model took all 212 features per epoch as inputs for the entire recording night (maximum 1150 epochs) of each participant, and its output was the probability of each epoch of being a positive class (values from 0 to 1).…”
Section: Methodsmentioning
confidence: 99%
See 3 more Smart Citations