Neural Anomalies Monitoring: Applications to Epileptic Seizure Detection and Prediction

There have been numerous efforts in the field of electronics with the aim of merging the areas of healthcare and technology in the form of low power, more efficient hardware. However one area of development that can aid in the bridge of healthcare and emerging technology is in Information and Communication Technology (ICT). Here, databasing and analysis systems can help bridge the wealth of information available (blood tests, genetic information, neural data) into a common framework of analysis. Also, ICT systems can integrate real-time processing from emerging technological solutions, such as developed low-power electronics. This work is based on this idea, merging technological solutions in the form of ICT with the need in healthcare to identify normality in a patients’ health profile. In this work we develop this idea and explain the concept more thoroughly. We then go on to explore two applications under development. The first is a system designed around monitoring neural activity and identifying, through a processing algorithm, what is normal activity, such that we can identify anomalies, or abnormalities in the signal. We explore Epilespy with seizure detection and prediction as an application case study to show the potential of this method. The motivation being that current methods of prediction have proven to be unsuccessful. We show that using our algorithm we can achieve significant success in seizure prediction and detection, above and beyond current methods. The second application explores the link between genetic information and standard tests (blood, urine etc...) and how they link in together to define a personalised benchmark. We show how this could work and the steps that have been made towards developing such a database

Comparison of prediction sensitivity versus FPR for the same variations on pattern methods depicted in Fig. 12.

<p>Comparison of prediction sensitivity versus FPR for the same variations on pattern methods depicted in Fig. 12.</p

The pattern counts generated for Ngrams of 2 to 16 for various re-quantizations from the original 16 bit to 4, 8 and 12.

<p>This is done over an example seizure period to identify the most effective quantization resolution.</p

The (a) sensitivity and (b) False prediction rate for the optimal channel with the best and 1st seizure used for training, optimised across all ITs.

<p>The (a) sensitivity and (b) False prediction rate for the optimal channel with the best and 1st seizure used for training, optimised across all ITs.</p

Comparison of methods for assessing sequence similarity in the multiresolution N-gram process, with pattern sizes of 12 (top) and 4 (bottom) over 5 second windows (method <i>(3)</i>).

<p>Comparison of methods for assessing sequence similarity in the multiresolution N-gram process, with pattern sizes of 12 (top) and 4 (bottom) over 5 second windows (method <i>(3)</i>).</p

The sensitivity, FPR and number of patients that exceed the lower and upper critical sensitivity (and FPR less than 0.15) for different brain focal onset regions for an SOP of 10 and 20 minutes.

<p>The sensitivity, FPR and number of patients that exceed the lower and upper critical sensitivity (and FPR less than 0.15) for different brain focal onset regions for an SOP of 10 and 20 minutes.</p

Sensitivity and FPR for an IT of 30, 20 and 10 minutes and for the best and 1st seizure training case for an SOP of 10 minutes.

<p>Also displayed are the optimal statistics when minimising FPR and maximizing sensitivity.</p>1<p>S: Sensitivity.</p>2<p>FPR: False Prediction Rate.</p

Summary of patient data used in this study, including number of seizures, seizure origin, electrode type and interictal hours used.

1<p>Origin  = {F: Frontal, T: Temporal, O: Occipital, P: Parietal}.</p>2<p>Electrode  = {g: grid, s: strip, d: depth}.</p

Traditional quantization (6 bit sampled at 256 Hz) on part of the sample EEG signal shown.

<p>Also shown are the equivalent hexadecimal symbol representation of this data.</p

Patterns and counts for method (d) using 2 pattern sizes ().

<p>Patterns and counts for method (d) using 2 pattern sizes ().</p