Lojini Logesparan

A Novel Phase Congruency Based Algorithm for Online Data Reduction in Ambulatory EEG Systems

Real signals are often corrupted by noise with a power spectrum variable over time. In applications involving these signals, it is expected that dynamically estimating and correcting for this noise would increase the amount of useful information extracted from the signal. One such application is scalp EEG monitoring in epilepsy, where electrical activity generated by cranio-facial muscles obscure the measured brainwaves. This paper presents a data-selection algorithm based on phase congruency to identify interictal spikes from background EEG; together with a novel statistical method that allows a more comprehensive trade-off based quantitative comparison of two algorithms which have been tested at a fixed threshold in the same database. Here, traditional phase congruency has been modified to incorporate a dynamic estimate of muscle activity present in the input scalp EEG signal. The proposed algorithm achieves 50% data reduction whilst detecting more than 80% of interictal spikes. This represents a significant improvement over the state-of-the-art denoising method for phase congruency.

Assessing the impact of signal normalization: Preliminary results on epileptic seizure detection

Signal normalization is an essential part of patient independent algorithms, for example to correct for variations in signal amplitude from different parts of the body, prior to applying a fixed threshold for event detection. Multiple methods for providing the required normalization are available. This paper presents a systematic investigation into the effects of five different methods using epileptic seizure detection from the EEG as an illustration case. It is found that, whilst normalization is essential, four of the considered methods actually decrease the ability to detect seizures, counteracting the algorithm aim. For optimal detection performance the effects of the signal normalization illustrated here should be incorporated into future algorithm designs.

A Low Power Linear Phase Programmable Long Delay Circuit

A novel linear phase programmable delay is being proposed and implemented in a 0.35 μm CMOS process. The delay line consists of N cascaded cells, each of which delays the input signal by Td/N, where Td is the total line delay. The delay generated by each cell is programmable by changing a clock frequency and is also fully independent of the frequency of the input signal. The total delay hence depends only on the chosen clock frequency and the total number of cascaded cells. The minimum clock frequency is limited by the maximum time a voltage signal can effectively be held by an individual cell. The maximum number of cascaded cells will be limited by the effects of accumulated offset due to transistor mismatch, which eventually will affect the operating mode of the individual transistors in a cell. This latter limitation has however been dealt with in the topology by having an offset compensation mechanism that makes possible having a large number of cascaded cells and hence a long resulting delay. The delay line has been designed for scalp-based neural activity analysis that is predominantly in the sub-100 Hz frequency range. For these signals, the delay generated by a 31-cell cascade has been demonstrated to be programmable from 30 ms to 3 s. Measurement results demonstrate a 31 stage, 50 Hz bandwidth, 0.3 s delay that operates from a 1.1 V supply with power consumption of 270 nW.

Discriminating between best performing features for seizure detection and data selection

Seizure detection algorithms have been developed to solve specific problems, such as seizure onset detection, occurrence detection, termination detection and data selection. It is thus inherent that each type of seizure detection algorithm would detect a different EEG characteristic (feature). However most feature comparison studies do not specify the seizure detection problem for which their respective features have been evaluated. This paper shows that the best features/algorithm bases are not the same for all types of algorithms but depend on the type of seizure detection algorithm wanted. To demonstrate this, 65 features previously evaluated for online seizure data selection are re-evaluated here for seizure occurrence detection, using performance metrics pertinent to each seizure detection type whilst keeping the testing methodology the same. The results show that the best performing features/algorithm bases for data selection and occurrence detection algorithms are different and that it is more challenging to achieve high detection accuracy for the former seizure detection type. This paper also provides a comprehensive evaluation of the performance of 65 features for seizure occurrence detection to aid future researchers in choosing the best performing feature(s) to improve seizure detection accuracy.

Improving seizure detection performance reporting: Analysing the duration needed for a detection

Improving seizure detection performance relies not only on novel signal processing approaches but also on new accurate, reliable and comparable performance reporting to give researchers better and fairer tools for understanding the true algorithm operation. This paper investigates the sensitivity of current performance metrics to the duration of data that must be marked as candidate seizure activity before a seizure detection is made. The results demonstrate that not all metrics are insensitive to this high level choice in the algorithm design, and provide new approaches for comparing between reported algorithm performances in a robust and reliable manner.

An introduction to future truly wearable medical devices—From application to ASIC

This talk will provide an introduction to the “Towards future truly wearable medical devices: from application to ASIC” mini-symposium. For user comfort and acceptance long term physiological sensors must be discrete, comfortable and easy to use. These requirements place stringent limits on all aspects of the system design: from the overall application aim, to power generation issues, to low power electronic design techniques. For successful devices design issues in all of these areas must be solved simultaneously. The work here presents an overview and introduction to these topics.

Improving phase congruency for EEG data reduction

Real signals are often corrupted by noise. In applications where the noise power spectrum is variable with time, dynamic noise estimation and compensation can potentially improve the performance of signal processing algorithms. One such application is scalp EEG monitoring in epilepsy, where the electrical activity generated by cranio-facial muscle contraction and expansion, often obscures the measured brainwave signals. This work presents a data reduction algorithm which is based on differentiating interictal from normal background activity, in epileptic scalp EEG signals, using a modified phase congruency technique. The modification is based on dynamically estimating muscle activity from the signal and incorporating this estimation in phase congruency computations. The proposed algorithm identifies 90%of interictal spikes whilst transmitting only 45% of EEG data. This is in the order of 15% improvement in data reduction when compared to the performance obtained with the state-of-the-art denoised phase congruency—which calculates a constant noise threshold—applied to the same dataset.

Performance-Power Consumption Tradeoff in Wearable Epilepsy Monitoring Systems

Automated seizure detection methods can be used to reduce time and costs associated with analyzing large volumes of ambulatory EEG recordings. These methods however have to rely on very complex, power hungry algorithms, implemented on the system backend, in order to achieve acceptable levels of accuracy. In size, and therefore power-constrained EEG systems, an alternative approach to the problem of data reduction is online data selection, in which simpler algorithms select potential epileptiform activity for discontinuous recording but accurate analysis is still left to a medical practitioner. Such a diagnostic decision support system would still provide doctors with information relevant for diagnosis while reducing the time taken to analyze the EEG. For wearable systems with limited power budgets, data selection algorithm must be of sufficiently low complexity in order to reduce the amount of data transmitted and the overall power consumption. In this paper, we present a low-power hardware implementation of an online epileptic seizure data selection algorithm with encryption and data transmission and demonstrate the tradeoffs between its accuracy and the overall system power consumption. We demonstrate that overall power savings by data selection can be achieved by transmitting less than 40% of the data. We also show a 29% power reduction when selecting and transmitting 94% of all seizure events and only 10% of background EEG.

Erratum to: Optimal features for online seizure detection

Electronic supplementary material (ESM) that was supposed to accompany this article was inadvertently omitted. Now, it is included here.