Efficient lossless multi-channel EEG compression based on channel clustering

With the growth of telemedicine systems, transferring a large number of medical signals such as for an EEG is a critical challenge. Intelligent analyzing systems, responsible for analyzing medical signals, are a very important part of any telemedicine system. These systems need data with high quality in order to detect abnormal events and diseases. Lossless compression methods play an important role when coding medical signals for telemedicine systems since the data remains unchanged. Multi-channel EEG signals for medical applications are usually acquired by a number of electrodes placed on different parts of the scalp. According to electrode placements, it is necessary to take into account their multi-channel structure to propose efficient compression methods. This paper uses inter-channel and intra-channel correlations to propose an efficient and simple lossless compression method. In the first stage, a differential pulse code modulation technique is used as a preprocessing step for extracting intra-channel correlation. Subsequently, channels are grouped in different clusters, and the centroid of each cluster is calculated and coded by arithmetic coding. In the second stage, the difference between the centroid and the data of channels in each cluster is calculated and compressed by arithmetic coding. The proposed method is capable of lossless EEG signal compression with a higher compression rate than existing methods.