Betta, Monica and Bernardi, Giulio and Ricciardi, Emiliano and Pietrini, Pietro and Haba-Rubio, J. and Siclari, Francesca and Heinzer, R. Quantifying peripheral sympathetic activation during sleep by means of an automatic method for pulse wave amplitude drop detection. In: World Sleep 2017, 7-11 October 2017, Prague (Submitted)
Full text not available from this repository.Abstract
Introduction: drops in pulse wave amplitude (PWA) measured by finger photoplethysmography (PPG) are known to reflect peripheral vasoconstriction resulting from sympathetic activation. Quantifying the amount of sympathetic activation during sleep would be useful to investigate the link between sleep disorders, like sleep apnea, and cardio-vascular morbidity-mortality. However, automatic algorithms allowing for a simple and rapid extraction and characterization of PWA parameters are not readily available. Therefore, in the present study we developed and validated a novel automatic approach to detect and characterize PWA-drops in whole-night polysomnographic (PSG) data. Materials and Methods: PSG recordings of 9 patients (52±5yrs, 7F) from the HypnoLaus Sleep Cohort were analyzed. The PPG signal was smoothed and detrended before extraction of the PWA signal, defined at each cardiac cycle as the difference between the peak and nadir values of the corresponding PPG-waveform. The time-courses of PWA variance and first-derivative were then evaluated using a moving-window over 5 heartbeats. Candidate time-points for potential PWA-drops were defined as local peaks in the PWA-variance showing correspondent first-derivative negative values. For each PWA-drop candidate, an observation interval was delimited between the closest previous and subsequent PWA maxima, and the maximum percent decrease (amplitude) was computed with respect to the mean of the previous 5 PWA values extracted from stable signal tracts (low local variance and duration >2sec). Then, PWA-drops with amplitude >30% and duration >4 heartbeats were identified, and their amplitude (%), descending slope (%/s) and total duration (s) were estimated. The PWA-drop index was calculated as the number of drops per hour. The algorithm detections were compared with those of an expert scorer who marked PWA-drops with amplitudes >30% (3min scoring window). Results: with respect to the human scorer, the algorithm achieved a sensitivity of 97.4%, a specificity of 89.5%, and a precision of 49.6%. In spite of the apparently low precision, both visual inspection and a direct comparison between false positive (FP) and true positive (TP) detections showed that the algorithm correctly identified above-threshold drops that were missed by the human scorer (minimum amplitude was 32.1±1.5% for FP, and 37.6±3.7% for TP). Only ~31% of all detected PWA-drops were associated with a (visually scored) EEG-arousal, whereas most EEG-arousals (~72%) showed an association with a PWA-drop. Interestingly, among PWA-drops that were not associated with a scored EEG-arousal, 19-55% (depending on sleep stage) were nevertheless accompanied by a strong increase in high-frequency EEG-power, potentially reflecting a cortical activation not visible to the human eye. Finally, the index, amplitude and duration tended to decrease from light (N1) to deep (N3) NREM sleep (p<0.05, rmANOVA), while REM sleep showed a significantly higher PWA-drop index compared to NREM stages (53.5±19.3d/h vs. 42.1±18.7d/h in N1). Discussion: the automatic algorithm allowed to reliably detect PWA-drops occurring in all sleep stages, including events not recognized upon standard visual inspection. This automatic algorithm may represent a simple and useful tool to quantify the degree of peripheral sympathetic activation during sleep and may provide relevant information about associated ‘cortical activations’ during sleep.
| Item Type: | Conference or Workshop Item (Poster) |
|---|---|
| Subjects: | R Medicine > R Medicine (General) |
| Research Area: | Computer Science and Applications |
| Depositing User: | Monica Betta |
| Date Deposited: | 04 Sep 2017 15:21 |
| Last Modified: | 04 Sep 2017 15:21 |
| URI: | http://eprints.imtlucca.it/id/eprint/3780 |
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