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Huffman quantization approach for optimized EEG signal compression with transformation technique

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Abstract

The significance of the electroencephalography (EEG) signal is used to read the brain activity in the form of electrical patterns. EEG signals help to diagnose anomalies in the brain at the time of head injuries, epilepsy, seizures, brain tumor, dizziness and sleep deprivation. So such types of crucial signals should be transported in a secure method to avoid any data loss or to prevent noise interruptions which can lead to the misdetection of diseases. As the EEG signals are in higher-dimensional size, it should be compressed for effective transportation. In this research, a lossless compression method named as Huffman-based discrete cosine transform is implemented to transmit the EEG data efficiently. The discrete cosine transform and inverse discrete cosine transform are proposed here to increase the privacy of the data and reduce the complexity of the data. This paper mainly focuses on to get a high accuracy ratio in reconstructing the original data after compression and transportation without any losses in minimum computational time. The preprocessing and sampling are made at the initial stages to remove the noises and transmit the original data. The Huffman quantization method based on discrete cosine transform achieves high-performance metrics in terms of peak signal-to-noise ratio, quality score and compression ratio when compared with existing methods in various transformations of data.

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Authors and Affiliations

  1. Department of Information Technology, SRM Institute of Science and Technology, Chennai, Tamil Nadu, India

    P. Rajasekar

  2. Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai, Tamil Nadu, India

    M. Pushpalatha

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  1. P. Rajasekar
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  2. M. Pushpalatha
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Correspondence to P. Rajasekar.

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Communicated by V. Loia.

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Rajasekar, P., Pushpalatha, M. Huffman quantization approach for optimized EEG signal compression with transformation technique. Soft Comput 24, 14545–14559 (2020). https://doi.org/10.1007/s00500-020-04804-z

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