A High-Performance Lossless Compression Scheme for EEG Signals Using Wavelet Transform and Neural Network Predictors
Developments of new classes of efficient compression algorithms, software systems, and hardware for data intensive applications in today's digital health care systems provide timely and meaningful solutions in response to exponentially growing patient information data complexity and associated...
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| Format: | Article |
| Language: | English |
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Wiley
2012-01-01
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| Series: | International Journal of Telemedicine and Applications |
| Online Access: | http://dx.doi.org/10.1155/2012/302581 |
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| author | N. Sriraam |
| author_facet | N. Sriraam |
| author_sort | N. Sriraam |
| collection | DOAJ |
| description | Developments of new classes of efficient compression algorithms, software systems, and hardware for data intensive applications in today's digital health care systems provide timely and meaningful solutions in response to exponentially growing patient information data complexity and associated analysis requirements. Of the different 1D medical signals, electroencephalography (EEG) data is of great importance to the neurologist for detecting brain-related disorders. The volume of digitized EEG data generated and preserved for future reference exceeds the capacity of recent developments in digital storage and communication media and hence there is a need for an efficient compression system. This paper presents a new and efficient high performance lossless EEG compression using wavelet transform and neural network predictors. The coefficients generated from the EEG signal by integer wavelet transform are used to train the neural network predictors. The error residues are further encoded using a combinational entropy encoder, Lempel-Ziv-arithmetic encoder. Also a new context-based error modeling is also investigated to improve the compression efficiency. A compression ratio of 2.99 (with compression efficiency of 67%) is achieved with the proposed scheme with less encoding time thereby providing diagnostic reliability for lossless transmission as well as recovery of EEG signals for telemedicine applications. |
| format | Article |
| id | doaj-art-981fb34bda2e485a84b6b63068864607 |
| institution | Kabale University |
| issn | 1687-6415 1687-6423 |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Telemedicine and Applications |
| spelling | doaj-art-981fb34bda2e485a84b6b630688646072025-02-03T05:54:18ZengWileyInternational Journal of Telemedicine and Applications1687-64151687-64232012-01-01201210.1155/2012/302581302581A High-Performance Lossless Compression Scheme for EEG Signals Using Wavelet Transform and Neural Network PredictorsN. Sriraam0Center for Biomedical Informatics and Signal Processing and Department of Biomedical Engineering, SSN College of Engineering, SSN Nagar, Kalavakkam, Chennai 603 110, IndiaDevelopments of new classes of efficient compression algorithms, software systems, and hardware for data intensive applications in today's digital health care systems provide timely and meaningful solutions in response to exponentially growing patient information data complexity and associated analysis requirements. Of the different 1D medical signals, electroencephalography (EEG) data is of great importance to the neurologist for detecting brain-related disorders. The volume of digitized EEG data generated and preserved for future reference exceeds the capacity of recent developments in digital storage and communication media and hence there is a need for an efficient compression system. This paper presents a new and efficient high performance lossless EEG compression using wavelet transform and neural network predictors. The coefficients generated from the EEG signal by integer wavelet transform are used to train the neural network predictors. The error residues are further encoded using a combinational entropy encoder, Lempel-Ziv-arithmetic encoder. Also a new context-based error modeling is also investigated to improve the compression efficiency. A compression ratio of 2.99 (with compression efficiency of 67%) is achieved with the proposed scheme with less encoding time thereby providing diagnostic reliability for lossless transmission as well as recovery of EEG signals for telemedicine applications.http://dx.doi.org/10.1155/2012/302581 |
| spellingShingle | N. Sriraam A High-Performance Lossless Compression Scheme for EEG Signals Using Wavelet Transform and Neural Network Predictors International Journal of Telemedicine and Applications |
| title | A High-Performance Lossless Compression Scheme for EEG Signals Using Wavelet Transform and Neural Network Predictors |
| title_full | A High-Performance Lossless Compression Scheme for EEG Signals Using Wavelet Transform and Neural Network Predictors |
| title_fullStr | A High-Performance Lossless Compression Scheme for EEG Signals Using Wavelet Transform and Neural Network Predictors |
| title_full_unstemmed | A High-Performance Lossless Compression Scheme for EEG Signals Using Wavelet Transform and Neural Network Predictors |
| title_short | A High-Performance Lossless Compression Scheme for EEG Signals Using Wavelet Transform and Neural Network Predictors |
| title_sort | high performance lossless compression scheme for eeg signals using wavelet transform and neural network predictors |
| url | http://dx.doi.org/10.1155/2012/302581 |
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