A Self-Adapting System for the Automated Detection of Inter-Ictal Epileptiform Discharges

Abstract

PurposeScalp EEG remains the standard clinical procedure for the diagnosis of epilepsy. Manual detection of inter-ictal epileptiform discharges (IEDs) is slow and cumbersome, and few automated methods are used to assist in practice. This is mostly due to low sensitivities, high false positive rates, or a lack of trust in the automated method. In this study we aim to find a solution that will make computer assisted detection more efficient than conventional methods, while preserving the detection certainty of a manual search.MethodsOur solution consists of two phases. First, a detection phase finds all events similar to epileptiform activity by using a large database of template waveforms. Individual template detections are combined to form “IED nominations”, each with a corresponding certainty value based on the reliability of their contributing templates. The second phase uses the ten nominations with highest certainty and presents them to the reviewer one by one for confirmation. Confirmations are used to update certainty values of the remaining nominations, and another iteration is performed where ten nominations with the highest certainty are presented. This continues until the reviewer is satisfied with what has been seen. Reviewer feedback is also used to update template accuracies globally and improve future detections.Key FindingsUsing the described method and fifteen evaluation EEGs (241 IEDs), one third of all inter-ictal events were shown after one iteration, half after two iterations, and 74%, 90%, and 95% after 5, 10 and 15 iterations respectively. Reviewing fifteen iterations for the 20–30 min recordings 1took approximately 5 min.SignificanceThe proposed method shows a practical approach for combining automated detection with visual searching for inter-ictal epileptiform activity. Further evaluation is needed to verify its clinical feasibility and measure the added value it presents.

Automated epileptiform spike detection via affinity propagation-based template matching

“…Similar studies have been presented in [17], [21]. Lodder et al implemented a spike detector based on a set of smart spike templates [21]. Here the system is only validated on a set of 241 IEDs, even though it is reported to perform with a sensitivity of 92%.…”

“…Also, the precision is in the acceptable range (.668), which is mainly attributed to the unbalanced dataset. Similar studies have been presented in [17], [21]. Lodder et al implemented a spike detector based on a set of smart spike templates [21].…”

Rapid annotation of interictal epileptiform discharges via template matching under Dynamic Time Warping

“…Annotation speed is improved drastically as a result. A related system (hereafter referred to collectively simply as the “ Self-Adapting System ”) has been proposed in (Lodder and van Putten, 2014), which also integrates the concept of template matching together with user assessment to iteratively refine the detection results while populating the database of tem plates. The Self-Adapting System is not dependent on user-selected templates in the actual application, while for NeuroBrowser , the user does need to define a specific template for the EEG recording at hand.…”

Detection of Interictal Discharges With Convolutional Neural Networks Using Discrete Ordered Multichannel Intracranial EEG

“…Detection of IEDs has attracted interest from the machine learning and biomedical communities and a variety of algorithms have been developed [1]. These algorithms are based on methods such as template matching [2]- [5], classification [6]- [9], dictionary learning [10], differential operator [11], spike rate [12] and other methods common in the well-established field of spike detection [14], [15]. The common characteristic of all these methods is that a description of an IED/spike signal is obtained either through modeling or similarity measurement with features of interest.…”