Abstract
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%.
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Acknowledgment
This work was supported in part by Junta de Comunidades de Castilla La Mancha (FrailCheck project SBPLY/17/180501/000392) and the Spanish Ministry of Economy and Competitiveness (TARSIUS project, TIN2015-71564-c4-1-R).
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Jiménez Martín, A., Cuevas Notario, A., García Domínguez, J.J., García Villa, S., Herrero Ramiro, M.A. (2019). Data Fusion for Improving Sleep Apnoea Detection from Single-Lead ECG Derived Respiration. In: Rojas, I., Valenzuela, O., Rojas, F., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2019. Lecture Notes in Computer Science(), vol 11466. Springer, Cham. https://doi.org/10.1007/978-3-030-17935-9_5
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