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The Cluster Tool : designing a clustering algorithm and graphical user interface for efficient clinical interpretation of Interictal Epileptiform Discharges

Spijkerboer, Feline (2019) The Cluster Tool : designing a clustering algorithm and graphical user interface for efficient clinical interpretation of Interictal Epileptiform Discharges.

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Abstract:Objective: Manual detection of interictal epileptiform activity in long-term EEG recordings is time-consuming and highly susceptible to individual interpretation. Automatic detection algorithms offer a faster, reproducible and more objective, and therefore more efficient method for EEG evaluation. These algorithms are able to reach human-like sensitivities. Nevertheless, they are rejected in the clinical practice due to their high false-positive rate and mostly impractical manner of displaying their results. Therefore, the objective of this research is to design a clustering algorithm and graphical user interface, that presents the automatically detected Interictal Epileptiform Discharges according to their morphology and localisation. Methods: The clustering algorithm was based on the events found by the Persyst P13 spike detector. We divided single lead EEG segments into groups according to their localisation. The events within these groups were then clustered with the K-means algorithm. The Squared Euclidean distance and Dynamic Time Warping distance were considered as distance measures for the clustering. The combination of clustering algorithm and graphical user interface is referred to as the Cluster Tool. The Cluster Tool was evaluated with usability and clinical performance tests. A total of 23 EEGs was used. The usability tests were performed by five EEG experts, through moderated testing. The clinical performance assessments were done by two test participants. Mutually agreed on clinical conclusions were compared to the clinical conclusion that was described in the EEG report, which was considered the gold standard. Results: The usage of the tool resulted in remarkably similar clinical diagnoses in comparison to the EEG report. However, the clusters derived by the algorithm did not consistently meet the expectations of the neurologists. This decreased their trust in the performance of the tool and caused them to spend time on manually checking detections within clusters. The use of the Cluster Tool did not speed up the EEG evaluation. The Dynamic Time Warping distance showed a slightly better separation of the cluster results than the SE distance. Discussion: The Cluster Tool shows the potential of a comprehensive visualisation of interictal epileptiform discharges for improving EEG evaluation. However, the clustering algorithm was too inaccurate to impact the clinical workflow positively. After the implementation of a sufficiently accurate clustering algorithm, the designed prototype promises a faster, reproducible and more objective method for EEG evaluation. The post-processing of the output of automatic spike detection algorithms is a step which has been underestimated for too long. Focusing on the visualisation of these detections is an important step toward the clinical implementation of such algorithms.
Item Type:Essay (Master)
Faculty:TNW: Science and Technology
Subject:44 medicine, 50 technical science in general
Programme:Technical Medicine MSc (60033)
Link to this item:http://purl.utwente.nl/essays/79964
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