Lossless Compression and Implementation for Medical Signals Using Verilog

Rasheed, M. Kamran; Padma, T. V.; Kumari, Ch. Usha; Rao, N. Madhusudan

doi:10.1109/iciccs51141.2021.9432328

Cited by 3 publications

(1 citation statement)

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“…To increase the privacy of the data and reduce the complexity of data, the authors used inverse discrete cosine transform and discrete cosine transform, where the EEG data have sent eciently. In [32], the author introduced a lossless Log2 sub band compression method to calculate the dierence between two 24-bit samples.…”

Section: Literature Reviewmentioning

confidence: 99%

An Edge-Fog Computing-Enabled Lossless EEG Data Compression With Epileptic Seizure Detection in IoMT Networks

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et al. 2022

IEEE Internet Things J.

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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 EEG data compression with Epileptic Seizure Detection in IoMT networks is proposed in this paper. 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 Human 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 rst step. Various measures implemented to show the eectiveness of the suggested approach and the comparison results conrm that the KCHE reduces the amount of EEG

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Section: Literature Reviewmentioning

confidence: 99%