Detecting Obstructive Sleep Apnea/Hypopnea Using Micromovements

Apnea-hypopnea detection includes obtaining accelerometer data from an accelerometer configured to measure micro-movements that are due to respiration. Displacement values are obtained from the accelerometer data. Features are obtained using the accelerometer data. An apnea-hypopnea index (AHI) is o...

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Bibliographische Detailangaben
Hauptverfasser:	Vule, Yan, Zakeri, Vahid, Galeev, Artem, Wang, Kongqiao
Format:	Patent
Sprache:	eng
Schlagworte:	DIAGNOSIS HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA HUMAN NECESSITIES HYGIENE IDENTIFICATION INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS MEDICAL OR VETERINARY SCIENCE PHYSICS SURGERY
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Beschreibung
Zusammenfassung:	Apnea-hypopnea detection includes obtaining accelerometer data from an accelerometer configured to measure micro-movements that are due to respiration. Displacement values are obtained from the accelerometer data. Features are obtained using the accelerometer data. An apnea-hypopnea index (AHI) is obtained from a machine learning model that uses the features as inputs. The displacement values correspond to peaks in the accelerometer data.