LAUSR

The need to improve smart health systems to monitor the health situation of patients has grown as a result of the spread of epidemic diseases, the ageing of the population, the increase in the number of patients, and the lack of facilities to treat them. This led to an increased demand for remote healthcare systems using biosensors. These biosensors produce a large volume of sensed data that will be received by the edge of the Internet of Medical Things (IoMT) to be forwarded to the data centers of the cloud for further treatment. An edge-fog computing-enabled lossless electroencephalogram (EEG) data compression with epileptic seizure detection in IoMT networks is proposed in this article. The proposed approach achieves three functionalities. First, it reduces the amount of sent data from the edge to the fog gateway using lossless EEG data compression based on a hybrid approach of $k$ -means clustering and Huffman encoding (KCHE) at the edge gateway. Second, it decides the epileptic seizure situation of the patient at the fog gateway based on the epileptic seizure detector-based Naive Bayes (ESDNB) algorithm. Third, it reduces the size of IoMT EEG data delivered to the cloud using the same lossless compression algorithm in the first step. Various measures implemented to show the effectiveness of the suggested approach and the comparison results confirm that the KCHE reduces the amount of EEG data transmitted to the fog and cloud platform and produces a suitable detection of an epileptic seizure. The average of compression power of the proposed KCHE is four times the average of compression power of other methods for all EEG records ( $Z, F, N, O$ , and $S$ ). Furthermore, the proposed ESDNB outperforms the other methods in terms of accuracy, where it provides accuracy from 99.53 % up to 99.99 % using the data set of Bonn University.