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

Abstract

This paper examines the effects of compression on 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.

An evaluation of the effects of wavelet coefficient quantisation in transform based EEG compression

“…Higgins et al however, determined that a much higher PRD (30%) can be tolerated while still maintaining seizure information [40], [41]. This is further verified in [29] where an automated seizure detection algorithm is used to verify these PRD limits. These 7% and 30% PRD limits were chosen as the operating points in this research to provide a reference point for potential real-world applications such as clinical review and automated seizure detection.…”

“…Examples of these transform operations include the Fourier Transform (FT) and Wavelet Transform (WT) which exploit signal sparsity in a particular domain [28], [29]. The research presented by Cárdenas-Barrera et al in [28] is an important paper in the field of EEG compression.…”

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Unlike other bioelectric signals, such as ECG [43], no metric exists to evaluate fidelity loss in EEG signals in regards diagnostically relevant information. PRD was chosen for this research due to its widespread use to analyse quality degradation in lossy EEG compression ([26], [28], [29]). In this research, PSD analysis is added to verify the results inferred by the PRD values.

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“…PRD was chosen for this research due to its widespread use to analyse quality degradation in lossy EEG compression ([26], [28], [29]). In this research, PSD analysis is added to verify the results inferred by the PRD values.…”

Energy efficient telemonitoring of physiological signals via compressed sensing: A fast algorithm and power consumption evaluation

“…One major challenge of wireless telemonitoring is the conflict between huge amount of data collected and limited battery life of portable devices [1]- [3]. Data need to be compressed [3], [4] before transmission. Most physiological signals are redundant, which means that they can be effectively compressed [3] using transform encoders such as Discrete Wavelet Transform (DWT) based methods [5].…”