Abstract
The aging population in advanced countries and the expensive costs of healthcare system has led to development of intelligent technology which is Wireless Body Sensor Network (WBSN). This intelligent healthcare system requires large amount of medical data produced from various types of biomedical sensors node to be collected, sent, and treated. This fact has created high latency and has increased network traffic. Therefore, health networks will suffer from congestions and bottlenecks. There is a need to minimize health network traffic volume and reduce latency especially in emergency condition where very short response time is required and improve networks performance by reducing the size of the transmitted data. This paper proposed a lossless Fractals compression approach to decrease the sent EEG from the gateway (Patient Data Aggregator (PDA)) cloud. The suggested approach improve the data communication in WBSNs by reducing the size of data traffic over the network. This approach is evaluated and compared with some existing methods and the results show that the introduced approach outperformed the other methods.
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Al-Nassrawy, K.K., Idrees, A.K., Al-Shammary, D. (2022). A Novel Lossless EEG Compression Model Using Fractal Combined with Fixed-Length Encoding Technique. In: Boulouard, Z., Ouaissa, M., Ouaissa, M., El Himer, S. (eds) AI and IoT for Sustainable Development in Emerging Countries. Lecture Notes on Data Engineering and Communications Technologies, vol 105. Springer, Cham. https://doi.org/10.1007/978-3-030-90618-4_21
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DOI: https://doi.org/10.1007/978-3-030-90618-4_21
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