Lossless EEG data compression using clustering and encoding for fog computing based IoMT networks
Ali Kadhum Idrees, Marwa Saieed Khlief · International Journal of Computer Applications in Technology · 2023
This paper proposes a Lossless Electroencephalography (EEG) Data Compression (LEDaC) method for fog computing networks based on the IoMT. The LEDaC combines two efficient data reduction techniques: DBSCAN clustering and Huffman encoding, to minimise the volume of IoMT data and then sends it to the cloud for further processing and analysis. The LEDaC works in periods. In each period, the DBSCAN groups the EEG data into groups of similar or identical values. The LEDaC then applies the Huffman encoding to each group, compressing the EEG data indexes with one representative value for each group. The compressed files will be transmitted to the cloud platform, and the original EEG data will be reconstructed without losing any information. The proposed LEDaC method has been tested, and the results show that, in terms of compression ratio, the proposed LEDaC method outperforms the other methods.