G Hellmann1. 1. Centre Epilepsy Erlangen, Department of Neurology, and Institute of Physiology and Experimental Pathophysiology, Friedrich-Alexander University, Erlangen-Nuremberg, Erlangen, Germany. hellmann@ipb.uni-erlangen.de
Abstract
OBJECTIVE: A 3-layer detection procedure was designed including preselection applying TEMPLAS software, feature extraction and artificial neural networks to determine a fast, precise and highly selective spike algorithm. METHODS: Ten intraoperative ECoG recordings of patients with temporal lobe epilepsy were computer-assisted and evaluated by 3 experts upon preselected events. For each event, 19 features were extracted, normalized and fed into a two-layer and 3-layer feedforward, back-propagate network. The weights of the 5 best individual two-layer networks of patients were averaged separately to derive a mean network, where weights were pruned, rounded off and the configuration approximated by a linear equation. RESULTS: In addition. when investigating latency histograms, a method for multi-channel artefact detection and elimination of too close intra-channel events could be found. Out of several training trails only the mean network and the linear equation were able to generalize. In comparison with the results of 19 publications, the developed solution and the estimated overall detection rates (spikes: 81%; non-spikes: 99.3%) were found to be of high quality. The processing time is short, and therefore, the method can be used to initiate other measurements. CONCLUSION: The developed solution is a fast, precise and highly selective spike detection method.
OBJECTIVE: A 3-layer detection procedure was designed including preselection applying TEMPLAS software, feature extraction and artificial neural networks to determine a fast, precise and highly selective spike algorithm. METHODS: Ten intraoperative ECoG recordings of patients with temporal lobe epilepsy were computer-assisted and evaluated by 3 experts upon preselected events. For each event, 19 features were extracted, normalized and fed into a two-layer and 3-layer feedforward, back-propagate network. The weights of the 5 best individual two-layer networks of patients were averaged separately to derive a mean network, where weights were pruned, rounded off and the configuration approximated by a linear equation. RESULTS: In addition. when investigating latency histograms, a method for multi-channel artefact detection and elimination of too close intra-channel events could be found. Out of several training trails only the mean network and the linear equation were able to generalize. In comparison with the results of 19 publications, the developed solution and the estimated overall detection rates (spikes: 81%; non-spikes: 99.3%) were found to be of high quality. The processing time is short, and therefore, the method can be used to initiate other measurements. CONCLUSION: The developed solution is a fast, precise and highly selective spike detection method.
Authors: Nykan Mirchi; Nebras M Warsi; Frederick Zhang; Simeon M Wong; Hrishikesh Suresh; Karim Mithani; Lauren Erdman; George M Ibrahim Journal: Front Hum Neurosci Date: 2022-06-27 Impact factor: 3.473