Abstract
An early diagnosis of a neurodegenerative process like the Alzheimer’s disease needs a tele-surveillance at home based on the recording of pathologic signals coming both from the cardiac activity (for detecting the loss of the sinus respiratory arrhythmia) and from the repetition of tasks of the daily life (signing a pathologic behavior called perseveration), whose non-invasive detection can lead to an early diagnosis, if it triggers secondly a battery of tests based on brain imaging, clinical neurology and cognitive sciences to confirm the suspicion of neuronal degeneration. For increasing the efficiency of alarms triggering these tests, we use dedicated tailored data compression methods, whose two examples will be presented, the Dynalets method for quantitative compression of the physiologic signals and the monotonic signature for qualitative compression.
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Demongeot, J., Hansen, O., Hamie, A., Hazgui, H., Virone, G., Vuillerme, N. (2015). Actimetry@home: Actimetric Tele-surveillance and Tailored to the Signal Data Compression. In: Bodine, C., Helal, S., Gu, T., Mokhtari, M. (eds) Smart Homes and Health Telematics. ICOST 2014. Lecture Notes in Computer Science(), vol 8456. Springer, Cham. https://doi.org/10.1007/978-3-319-14424-5_7
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