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A Tele Encephalopathy Diagnosis Based on EEG Signal Compression and Encryption

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Advances in Cyber Security (ACeS 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1347))

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Abstract

A Telemedicine network that uses connectivity and information technology to transmit medical signals such as neurological signals Electroencephalography (EEG) has become a reality for medical services of long distances. In the monitoring of mobile healthcare, these signals need to be compressed for the efficient utilization of bandwidth and the confidentiality of these signals, where compression is a critical tool to solve storage and transmission problems and can then retrieve the original signal (OS) from the compressed signal. The objective of this manuscript is to achieve higher compression gains at a low bit rate while maintaining the integrity of clinical details and also encrypting the signal to keep it private, except for specialists. Thresholding techniques are utilized in the compression stage, the Discrete Wavelet Transform (DWT). Instead, Huffman Encoding (HuFE) is utilized for compression and EEG signal encryption with chaos. This manuscript addresses the encoding of EEG signals with consistency for Telemedicine applications. To test the proposed method, overall compression and reconstruction (ComRec) time (T) was measured, the root mean square (RMSE), and the compression ratio (CR). Findings from the simulation show that the addition of HuFE after the DWT algorithm gives the best CR and complexity efficiency. The findings show that the consistency of the reconstructed signal (Rs) is maintained at a low PRD while yielding better findings in compression. Utilizing the DWT as a loss compression algorithm followed by the HuFE as a lossy compression algorithm, CR = 92.9% at RMS = 0.16 and PRD = 5. 4131%.

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Author information

Authors and Affiliations

  1. College of Computer Science and Information Technology, University of Anbar, Ramadi, Iraq

    Azmi Shawkat Abdulbaqi

  2. College of Science, Department of Physics, University of Misan, Amarah, Iraq

    Salwa Mohammed Nejrs

  3. College of Engineering, University of Tikreet, Tikrit, Iraq

    Sawsan D. Mahmood

  4. Faculty of Arts, Computing and Creative Industry, Sultan Idris Education University, Tanjong Malim, Malaysia

    Ismail Yusuf Panessai

Authors
  1. Azmi Shawkat Abdulbaqi
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  2. Salwa Mohammed Nejrs
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  3. Sawsan D. Mahmood
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  4. Ismail Yusuf Panessai
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Corresponding author

Correspondence to Azmi Shawkat Abdulbaqi .

Editor information

Editors and Affiliations

  1. National Advanced IPv6 Centre, Universiti Sains Malaysia, Penang, Malaysia

    Mohammed Anbar

  2. Hodeidah University, Hodeidah, Yemen

    Nibras Abdullah

  3. National Advanced IPv6 Centre, Universiti Sains Malaysia, Penang, Malaysia

    Selvakumar Manickam

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Abdulbaqi, A.S., Nejrs, S.M., Mahmood, S.D., Panessai, I.Y. (2021). A Tele Encephalopathy Diagnosis Based on EEG Signal Compression and Encryption. In: Anbar, M., Abdullah, N., Manickam, S. (eds) Advances in Cyber Security. ACeS 2020. Communications in Computer and Information Science, vol 1347. Springer, Singapore. https://doi.org/10.1007/978-981-33-6835-4_10

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  • DOI: https://doi.org/10.1007/978-981-33-6835-4_10

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