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Session: Sleep
Session starts: Thursday 24 January, 10:30
Presentation starts: 10:45
Room: Lecture room 558

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Huysmans Dorien (KU Leuven)
Bertien Buyse (UZ Leuven)
Dries Testelmans (UZ Leuven)
Sabine Van Huffel (KU Leuven, imec)
Carolina Varon (KU Leuven, imec)

Abstract:
Aim: The commercial Emfit QS (Emfit) pressure sensor is prone to large deviations in the signal (artefacts) due to posture changes and movements. The aim is to identify these artefacts while not relying on visual scoring or information from other sensors. Therefore, an unsupervised approach is developed. It was hypothesized that the percentage of artefacts increases with the patient’s Apnea-Hypopnea Index (AHI). Methods: The Emfit sensor and polysomnography recorded data of 37 patients during sleep diagnosis in UZ Leuven. The pressure signal was preprocessed, decomposed by wavelet transformation and analysed with time-domain features. Next, unsupervised feature selection was based on Robust Spectral Learning (RSFS). The parameters of RSFS, number of features and clusters were optimized by a grid search and running the k-means algorithm for two clusters. The optimised parameters clustered the training points in two clusters, i.e. clean and artefact. Every test data point was mapped to the closest centroid of both clusters in Euclidean distance to label it clean or artefact. The percentage of artefacts per patient was grouped according to standard sleep apnea classes based on AHI. To separate severe sleep apnea (AHI≥30) patients from other sleep apnea patients (AHI<30), a receiver operating characteristic curve analysis was performed. Results: Three features highlighting peak variation were optimal for artefact detection. The percentage of artefacts in the data was a potential parameter to target severe sleep apnea patients. A threshold of 14.6% artefacts resulted in a sensitivity of 80% and specificity of 87%. Conclusion: The Emfit pressure sensor was explored in its potential for sleep apnea screening. An unsupervised algorithmic pipeline based on clustering was developed to detect artefacts and relate these artefacts to sleep apnea classes. The percentage of artefacts in the data was shown a potential parameter to target severe sleep apnea patients.