The proposed dynamic dictionary algorithm using DWT and SPIHT can compress EEG channels and positively detect seizure sections in recordings at bitrates down to 2 bits per sample.
A novel EEG compression technique using DWT and SPIHT successfully compresses data while maintaining the ability to detect seizure-like activity at low bitrates.
A novel technique for real-time electroencephalogram (EEG) compression is proposed in this paper. This technique makes use of the redundancy between the different frequency subbands present in EEG segments of one channel. It uses discrete wavelet transform (DWT) and dynamic reference lists to compute and send the decorrelated subband coefficients. Set partitioning in hierarchical trees (SPIHT) is also used as source coder. Experimental results showed that the proposed method can not only compress EEG channels in one dimension (1- D), but also detect seizure-like activity. A diagnostics-oriented performance assessment was performed to evaluate the performance of both the compression and detection capabilities of the proposed method. In this paper, we show that the algorithm can positively detect seizure sections in the recordings at bitrates down to 2 bits per sample.
Daou et al. (Wed,) conducted a other in Seizure. Dynamic Dictionary for Combined EEG Compression and Seizure Detection was evaluated on Compression and seizure detection capabilities. The proposed dynamic dictionary algorithm using DWT and SPIHT can compress EEG channels and positively detect seizure sections in recordings at bitrates down to 2 bits per sample.