Authors:
Rushda Basir Ahmad
and
Nadeem Ahmad Khan
Affiliation:
Department of Electrical Engineering, Lahore University of Management Sciences, Lahore, Pakistan
Keyword(s):
Compression, Binary Encoding, Epilepsy, EEG.
Abstract:
Ambulatory electroencephalogram (EEG), allows collection of patients data over extended periods of time. However, as a small recording requires large memory for storage, and this makes EEG data storage an arduous task. Moreover, classification of EEG for extraction of relevant information is relatively challenging, and selective data retrieval depends on task at hand. Consequently, EEG data storage and classification need to be computationally efficient. This paper presents a combined scheme, for the simultaneous compression and classification of EEG data, which not only decreases the overall computational effort, but also allows selective archiving and retrieval of data. Huffman and Arithmetic coding techniques are employed on CHB-MIT scalp EEG database and the results are presented in form of compression ratio (CR) and percentage root mean square distortion (PDR). For classification, Intelligent Neurologist Support System (INSS), has been used. The classifier output apart from bein
g stored as data, is also used for intelligent data reduction, when only specific information is required, resulting in increased CR and decreased PDR, which is desired. Hence, the results show intelligent compression and reduction of data results in efficient management of EEG data. The signal undergoes state-of-the-art compression such that on reconstruction it almost maintains the same classification accuracy as the original one.
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