Stephen Faul

Energy-Efficient Low Duty Cycle MAC Protocol for Wireless Body Area Networks

This paper presents an energy-efficient medium access control protocol suitable for communication in a wireless body area network for remote monitoring of physiological signals such as EEG and ECG. The protocol takes advantage of the static nature of the body area network to implement the effective time-division multiple access (TDMA) strategy with very little amount of overhead and almost no idle listening (by static, we refer to the fixed topology of the network investigated). The main goal is to develop energy-efficient and reliable communication protocol to support streaming of large amount of data. TDMA synchronization problems are discussed and solutions are presented. Equations for duty cycle calculation are also derived for power consumption and battery life predictions. The power consumption model was also validated through measurements. Our results show that the protocol is energy efficient for streaming communication as well as sending short bursts of data, and thus can be used for different types of physiological signals with different sample rates. The protocol is implemented on the analog devices ADF7020 RF transceivers.

A comparison of quantitative EEG features for neonatal seizure detection

OBJECTIVE This study was undertaken to identify the best performing quantitative EEG features for neonatal seizures detection from a test set of 21. METHODS Each feature was evaluated on 1-min, artefact-free segments of seizure and non-seizure neonatal EEG recordings. The potential utility of each feature for neonatal seizure detection was determined using receiver operating characteristic analysis and repeated measures t-tests. A performance estimate of the feature set was obtained using a cross-fold validation and combining all features together into a linear discriminant classifier model. RESULTS Significant differences between seizure and non-seizure segments were found in 19 features for 17 patients. The best performing features for this application were the RMS amplitude, the line length and the number of local maxima and minima. An estimate of the patient independent classifier performance yielded a sensitivity of 81.08% and specificity of 82.23%. CONCLUSIONS The individual performances of 21 quantitative EEG features in detecting electrographic seizure in the neonate were compared and numerically quantified. Combining all features together into a classifier model led to superior performance than that provided by any individual feature taken alone. SIGNIFICANCE The results documented in this study may provide a reference for the optimum quantitative EEG features to use in developing and enhancing neonatal seizure detection algorithms.

An evaluation of automated neonatal seizure detection methods

OBJECTIVE To evaluate 3 published automated algorithms for detecting seizures in neonatal EEG. METHODS One-minute, artifact-free EEG segments consisting of either EEG seizure activity or non-seizure EEG activity were extracted from EEG recordings of 13 neonates. Three published neonatal seizure detection algorithms were tested on each EEG recording. In an attempt to obtain improved detection rates, threshold values in each algorithm were manipulated and the actual algorithms were altered. RESULTS We tested 43 data files containing seizure activity and 34 data files free from seizure activity. The best results for Gotman, Liu and Celka, respectively, were as follows: sensitivities of 62.5, 42.9 and 66.1% along with specificities of 64.0, 90.2 and 56.0%. CONCLUSIONS The levels of performance achieved by the seizure detection algorithms are not high enough for use in a clinical environment. The algorithm performance figures for our data set are considerably worse than those quoted in the original algorithm source papers. The overlap of frequency characteristics of seizure and non-seizure EEG, artifacts and natural variances in the neonatal EEG cause a great problem to the seizure detection algorithms. SIGNIFICANCE This study shows the difficulties involved in detecting seizures in neonates and the lack of a reliable detection scheme for clinical use. It is clear from this study that while each algorithm does produce some meaningful information, the information would only be usable in a reliable neonatal seizure detection process when accompanied by more complex analysis, and more advanced classifiers.

EEG compression using JPEG2000: How much loss is too much?

Compression of biosignals is an important means of conserving power in wireless body area networks and ambulatory monitoring systems. In contrast to lossless compression techniques, lossy compression algorithms can achieve higher compression ratios and hence, higher power savings, at the expense of some degradation of the reconstructed signal. In this paper, a variant of the lossy JPEG2000 algorithm is applied to Electroencephalogram (EEG) data from the Freiburg epilepsy database. By varying compression parameters, a range of reconstructions of varying signal fidelity is produced. Although lossy compression has been applied to EEG data in previous studies, it is unclear what level of signal degradation, if any, would be acceptable to a clinician before diagnostically significant information is lost. In this paper, the reconstructed EEG signals are applied to REACT, a state-of-the-art seizure detection algorithm, in order to determine the effect of lossy compression on its seizure detection ability. By using REACT in place of a clinician, many hundreds of hours of reconstructed EEG data are efficiently analysed, thereby allowing an analysis of the amount of EEG signal distortion that can be tolerated. The corresponding compression ratios that can be achieved are also presented.

Gaussian Process Modeling of EEG for the Detection of Neonatal Seizures

Gaussian process (GP) probabilistic models have attractive advantages over parametric and neural network modeling approaches. They have a small number of tuneable parameters, can be trained on relatively small training sets, and provide a measure of prediction certainty. In this paper, these properties are exploited to develop two methods of highlighting the presence of neonatal seizures from electroencephalograph (EEG) signals. In the first method, the certainty of the GP model prediction is used to indicate the presence of seizures. In the second approach, the hyperparameters of the GP model are used. Tests are carried out with a feature set of ten EEG measures developed from various signal processing techniques. Features are evaluated using a neural network classifier on 51 h of real neonatal EEG. The GP measures, in particular, the prediction certainty approach, produce a high level of performance compared to other modeling methods and methods currently in clinical use for EEG analysis, indicating that they are an important and useful tool for the real-time detection of neonatal seizures.

Automatic detection of EEG artefacts arising from head movements using EEG and gyroscope signals

Contamination of EEG signals by artefacts arising from head movements has been a serious obstacle in the deployment of automatic neurological event detection systems in ambulatory EEG. In this paper, we present work on categorizing these head-movement artefacts as one distinct class and on using support vector machines to automatically detect their presence. The use of additional physical signals in detecting head-movement artefacts is also investigated by means of support vector machines classifiers implemented with gyroscope waveforms. Finally, the combination of features extracted from EEG and gyroscope signals is explored in order to design an algorithm which incorporates both physical and physiological signals in accurately detecting artefacts arising from head-movements.

Automatic detection of EEG artefacts arising from head movements

The need for reliable detection of artefacts in raw and processed EEG is widely acknowledged. In this paper, we present the results of an investigation into appropriate features for artefact detection in the REACT ambulatory EEG system. The study focuses on EEG artefacts arising from head movement. The use of one generalised movement artefact class to detect movement artefacts is proposed. Temporal, frequency, and entropy-based features are evaluated using Kolmogorov-Smirnov and Wilcoxon rank-sum non-parametric tests, Mutual Information Evaluation Function and Linear Discriminant Analysis. Results indicate good separation between normal EEG and artefacts arising from head movement, providing a strong argument for treating these head movement artefacts as one generalised class rather than treating their component signals individually.

Age-independent seizure detection

This paper examines whether an appropriate algorithm, developed for use with neonatal data, could also be used, without alteration, for the detection of seizures in adults with epilepsy. The performance of a feature extraction and SVM classifier system is evaluated on databases of 17 neonatal patients and 15 adult patients. Mean ROC curve areas of 0.96 and 0.94 for neonatal and adult databases respectively show that high accuracy can be achieved independent of age. It is also shown that features contribute differently for neonatal and adult data.

Chaos theory analysis of the newborn EEG - is it worth the wait?

In this study neonatal EEG has been analysed with information theory, complexity, SVD-based and nonlinear dynamic systems theory, or chaos theory, approaches. The analysis has been carried out to determine, given the amount of extra time needed to generate the chaos theory results, if they are considerably better than their information theory, complexity and SVD-based counterparts. The results show that while the KY dimension gives comparable performance to the information theory approaches, its computation time is more than 1000 times greater. The effects of preprocessing are also analysed

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.