Hybrid multichannel EEG compression scheme for tele-health monitoring

Telemonitoring of Electroencephalogram (EEG) through wireless and information technology is very crucial as EEG is a primary clinical diagnostic tool for many neurological disorders. EEG signals are continuously recorded through many channels over prolonged time periods at extremely high resolution and thus possess enormous data size. This may lead to large storage space and more transmission bandwidth requirement for remote EEG signal analysis. An attempt has been made to compress multichannel EEG by exploiting neighboring channel and sample redundancy, present along with the information content. This paper presents a high performance hybrid multichannel EEG compression algorithm based on frequency transformation and parameter extraction techniques. It compares discrete cosine transform (DCT) with Fast Fourier transform (FFT) to compress the multichannel EEG. This compressed EEG is embedded with corresponding EEG sub band power information to obtain a high quality signal reconstruction for accurate brain state analysis. The performance of proposed hybrid compression scheme is quantified in terms of compression level achieved and reconstruction error introduced. A high compression ratio and considerably low percent root-mean-square difference (PRD) error is found thereby preserving diagnostic information while transmission/ recovery of real time multichannel EEG for efficient telehealth monitoring applications with reduced computational complexity. This research has also resulted in repeatability and thus reliability testing of an efficient hybrid compression technique developed earlier for ECG signals.