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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

Sample recordings of DS1–DS5.
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Related In: Results  -  Collection


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fig3: Sample recordings of DS1–DS5.

Mentions: For experimental study, recordings of EEG grabbed from extracranial and intracranial electrodes obtained from the host site of Epileptology Department, University of Bonn are used [44]. Data sets 1 and 2 (DS1 and DS2) are obtained from healthy volunteers in, an awaken state with eyes open (DS1) and eyes closed (DS2), respectively which are recorded using surface electrodes [23, 33]. Data set (DS3) is extracted from hippocampal formation of the opposite hemisphere of the brain and Data set 4 (DS4) is recorded from within the epileptic zone [44]. DS3 and DS4 contained activity measured during seizure-free intervals. Data set 5 (DS5) contains recordings exhibiting ictal seizure activity. DS3–DS5 is recorded using intracranial electrodes [44]. A total of 15-minute recordings of EEG are considered. DS1–DS5 is represented with 12 bit accuracy with a sampling rate of 173.61 Hz [44]. Figure 3 shows the sample recordings of EEGs with 180 s samples.


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

Sriraam N - Int J Telemed Appl (2012)

Sample recordings of DS1–DS5.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig3: Sample recordings of DS1–DS5.
Mentions: For experimental study, recordings of EEG grabbed from extracranial and intracranial electrodes obtained from the host site of Epileptology Department, University of Bonn are used [44]. Data sets 1 and 2 (DS1 and DS2) are obtained from healthy volunteers in, an awaken state with eyes open (DS1) and eyes closed (DS2), respectively which are recorded using surface electrodes [23, 33]. Data set (DS3) is extracted from hippocampal formation of the opposite hemisphere of the brain and Data set 4 (DS4) is recorded from within the epileptic zone [44]. DS3 and DS4 contained activity measured during seizure-free intervals. Data set 5 (DS5) contains recordings exhibiting ictal seizure activity. DS3–DS5 is recorded using intracranial electrodes [44]. A total of 15-minute recordings of EEG are considered. DS1–DS5 is represented with 12 bit accuracy with a sampling rate of 173.61 Hz [44]. Figure 3 shows the sample recordings of EEGs with 180 s samples.

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