[home] [Personal Program] [Help]

---

Session: Poster Session 1 (Even numbers)
Session starts: Thursday 26 January, 16:00
Presentation starts: 16:00

---

Luca Cerina (TU Eindhoven)

Abstract:
The accurate detection of respiratory effort during Polysomnography (PSG) is critical in the diagnosis of sleep disordered breathing conditions such as sleep apnea. Unfortunately, the sensors used in the clinical routine are invasive or do not capture upper airway dynamics. One promising Extraction of cardiac-related signal from suprasternal pressure sensor in polysomnigraphyalternative is the Suprasternal Notch Pressure Sensor (SSP): a small sensor placed on the skin that detects pressure swings in the thoracic cavity. Besides respiratory activity, the SSP may also pick up small pressure oscillations caused by the pumping heart. Although these are commonly removed as unwanted artifacts, they have potentially informative content regarding cardiac activity. In this study we propose a method to extract cardiac information from the SSP signal. First, the raw signal is filtered to attenuate respiratory frequencies. Then we get robust-to-noise estimates of the heart rate (HR) as local maxima in the signal’s autocorrelation. The frequency search range is determined through a-priori knowledge of HR dynamics and by tracking the temporal evolution of HR estimates. Finally, we tune time-variant filters on the HR to separate respiratory and cardiac signals. The performance in HR estimation is compared with a ground truth extracted from synchronized ECG recordings on a sample of 100 participants undergoing a full single-night PSG, including the SSP sensor, for various suspected sleep disorders. Since the transition to sleep apnea events may hinder our method, we also measured the loss of performance compared to normal breathing. The respiratory signal filtered using our method or a fixed frequency notch filter at 1.6Hz (currently employed) are compared qualitatively. Pooling all the estimates, the Bland-Altman agreement analysis resulted in a linear bias of -0.06bpm with 95% level-of-agreement of 5.09bpm. The coverage of SSP noise-free estimates compared to the ECG was 94.4±2.3%. A paired Wilcoxon Rank-Sum test determined that the error caused by respiratory events is significant across the experimental population, with an average increase of 0.38bpm (interquartile range 1.44bpm). We showed that besides thoracic respiration pressure swings, the SSP sensor contains reliable cardiac information. Our method achieved good results in estimating the HR without additional sensors and unlocked new research activities regarding the extracted cardiac signal.