Data Fusion for Improving Sleep Apnoea Detection from Single-Lead ECG Derived Respiration

Autor:	Sara Garcia de Villa, Miguel A. Herrero Ramiro, Juan Jesús García Domínguez, Ana Jimenez Martin, Alejandro Cuevas Notario
Rok vydání:	2019
Předmět:	Frequency analysis Central sleep apnea Computer science business.industry Pattern recognition Sensor fusion medicine.disease Confidence interval law.invention Ecg derived respiration Amplitude Robustness (computer science) law medicine Sleep (system call) Artificial intelligence business
Zdroj:	Bioinformatics and Biomedical Engineering ISBN: 9783030179342 IWBBIO (2)
Popis:	This work presents two algorithms for detecting apnoeas from the single-lead electrocardiogram derived respiratory signal (EDR). One of the algorithms is based on the frequency analysis of the EDR amplitude variation applying the Lomb-Scargle periodogram. On the other hand, the sleep apnoeas detection is carried out from the temporal analysis of the EDR amplitude variation. Both algorithms provide accuracies around 90%. However, in order to improve the robustness of the detection process, it is proposed to fuse the results obtained with both techniques through the Dempster-Shafer evidence theory. The fusion of the EDR-based algorithm results indicates that, the 84% of the detected apnoeas have a confidence level over 90%.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::484938e391929cfa2bcdd95752297009 https://doi.org/10.1007/978-3-030-17935-9_5 Zobrazit plný text záznamu