TY - GEN
T1 - Lossless EEG data source coding for seizure prone activity
AU - Mc Sweeney, Richard
AU - Popovici, Emanuel
PY - 2010
Y1 - 2010
N2 - Autonomous sensor networks that provide patient monitoring are growing in popularity due to the prospects of lower cost, and the ease of supervision by the physician. Physiological signals monitoring could result in large volume of data being either transmitted or stored which can then be directly related with the energy consumption for the system. This paper presents an EEG compression scheme that is aimed at real-time patient monitoring. It is lossless and incorporates well-known techniques that are computationally easy. A segmentation process that takes advantage of the 50 Hz mains signal is introduced in this work to reduce the entropy of the data stream. High compression gains of 60-66% for both seizure and non-seizure activity are obtained, and a comparison with other high performance lossless EEG compression strategies are presented. The results show that the proposed method performs 2-6% better than a method which directly applies Huffman coding to a DPCM EEG signal.
AB - Autonomous sensor networks that provide patient monitoring are growing in popularity due to the prospects of lower cost, and the ease of supervision by the physician. Physiological signals monitoring could result in large volume of data being either transmitted or stored which can then be directly related with the energy consumption for the system. This paper presents an EEG compression scheme that is aimed at real-time patient monitoring. It is lossless and incorporates well-known techniques that are computationally easy. A segmentation process that takes advantage of the 50 Hz mains signal is introduced in this work to reduce the entropy of the data stream. High compression gains of 60-66% for both seizure and non-seizure activity are obtained, and a comparison with other high performance lossless EEG compression strategies are presented. The results show that the proposed method performs 2-6% better than a method which directly applies Huffman coding to a DPCM EEG signal.
UR - https://www.scopus.com/pages/publications/79951609929
U2 - 10.1109/ITAB.2010.5687613
DO - 10.1109/ITAB.2010.5687613
M3 - Conference proceeding
AN - SCOPUS:79951609929
SN - 9781424465606
T3 - Proceedings of the IEEE/EMBS Region 8 International Conference on Information Technology Applications in Biomedicine, ITAB
BT - ITAB 2010 - 10th International Conference on Information Technology and Applications in Biomedicine
T2 - 10th International Conference on Information Technology and Applications in Biomedicine: Emerging Technologies for Patient Specific Healthcare, ITAB 2010
Y2 - 2 November 2010 through 5 November 2010
ER -