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A high-performance lossless compression scheme for EEG signals using wavelet transform and neural network predictors.

Sriraam N - Int J Telemed Appl (2012)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Center for Biomedical Informatics and Signal Processing and Department of Biomedical Engineering, SSN College of Engineering, SSN Nagar, Kalavakkam, Chennai 603 110, India.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus

Proposed lossless compression scheme.
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fig1: Proposed lossless compression scheme.

Mentions: In [18] Wongsawat et al. applied the Karhunen-Loeve transform (KLT) for lossless EEG compression. The effect of uniform quantization on near-lossless compression of EEG signals has been reported by the author [19]. Gopikrishna and Makur discussed a near-lossless compression scheme using wavelets and ARX model [20]. Lossy compression based on genetic algorithm, wavelet-packets, and neural network and linear predictors have been reported [21–23]. Recent works reported based on pursuit approach with wavelet dictionaries, wavelet-SPIHT, and finite rate of innovation technique exploiting sampling theory have shown some improvement in the compression performance [24–26]. It has been observed from the existing literature that even though several compression techniques have been reported, the search for new methods continues to achieve higher compression efficiency, while preserving the point-to-point diagnostic information in the reconstructed signal. This paper highlights a high performance lossless EEG compression using wavelet transform and neural network predictors. Even though the combinations of wavelet and neural network have been reported for compression problems [27–29], it has not been extensively applied for 1D biomedical signals. Figure 1 shows the proposed lossless EEG compression scheme.


A high-performance lossless compression scheme for EEG signals using wavelet transform and neural network predictors.

Sriraam N - Int J Telemed Appl (2012)

Proposed lossless compression scheme.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3303683&req=5

fig1: Proposed lossless compression scheme.
Mentions: In [18] Wongsawat et al. applied the Karhunen-Loeve transform (KLT) for lossless EEG compression. The effect of uniform quantization on near-lossless compression of EEG signals has been reported by the author [19]. Gopikrishna and Makur discussed a near-lossless compression scheme using wavelets and ARX model [20]. Lossy compression based on genetic algorithm, wavelet-packets, and neural network and linear predictors have been reported [21–23]. Recent works reported based on pursuit approach with wavelet dictionaries, wavelet-SPIHT, and finite rate of innovation technique exploiting sampling theory have shown some improvement in the compression performance [24–26]. It has been observed from the existing literature that even though several compression techniques have been reported, the search for new methods continues to achieve higher compression efficiency, while preserving the point-to-point diagnostic information in the reconstructed signal. This paper highlights a high performance lossless EEG compression using wavelet transform and neural network predictors. Even though the combinations of wavelet and neural network have been reported for compression problems [27–29], it has not been extensively applied for 1D biomedical signals. Figure 1 shows the proposed lossless EEG compression scheme.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Center for Biomedical Informatics and Signal Processing and Department of Biomedical Engineering, SSN College of Engineering, SSN Nagar, Kalavakkam, Chennai 603 110, India.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus