The Effects of Lossy Compression on Diagnostically Relevant Seizure Information in EEG Signals

This paper examines the effects of compression on electroencephalogram (EEG) signals, in the context of automated detection of epileptic seizures. Specifically, it examines the use of lossy compression on EEG signals in order to reduce the amount of data which has to be transmitted or stored, while having as little impact as possible on the information in the signal relevant to diagnosing epileptic seizures. Two popular compression methods, JPEG2000 and SPIHT, were used. A range of compression levels was selected for both algorithms in order to compress the signals with varying degrees of loss. This compression was applied to the database of epileptiform data provided by the University of Freiburg, Germany. The real-time EEG analysis for event detection automated seizure detection system was used in place of a trained clinician for scoring the reconstructed data. Results demonstrate that compression by a factor of up to 120:1 can be achieved, with minimal loss in seizure detection performance as measured by the area under the receiver operating characteristic curve of the seizure detection system.

I. Introduction

Multichannel Electroencephalogram (EEG) is a tool for measuring the electrical activity of the brain, and the use of EEG to diagnose a variety of neurological conditions such as epilepsy has long been established [1]. Recent years have seen an increased interest in the use of ambulatory EEG monitoring, where at-home monitoring give advantages over in-patient monitoring in diagnosing neurological conditions [2]. A wireless and mobile ambulatory EEG device would allow the patient to remain at home in their normal environment during periods of observation. Furthermore, automated seizure detection would also reduce the workload of a trained clinician monitoring EEG recordings.