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Session: Neurophysiology: Clinical Neurophysiology
Session starts: Thursday 24 January, 13:30
Presentation starts: 13:45
Room: Lamoraalzaal

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Debby Klooster ()
Bert Kleine ()
Martien van Bussel ()
Miriam Lammers ()
Machiel Zwarts ()

Abstract:
Aim: In this project we investigate whether EMG recordings have added value in the diagnosis and classification of epilepsy with motor seizures. Motor seizures can be either myoclonias, tonic seizures, clonic seizures or tonic-clonic seizures. One of the goals is to differentiate these seizures from non-epileptic attacks and movement disorders. Methods: EMG was recorded in 34 patients referred to our epilepsy monitoring unit (EMU) for the differential diagnosis of their spells. In the EMU, EEG and video are continuously recorded for a period of one up to five days. Stellate equipment is used in combination with a 36 channel Lamont amplifier. Depending on the number of channels that were not in use for EEG, the EMG is recorded from the M. deltoideus bilaterally or from the M. deltoideus, the M. biceps brachii and the M. triceps brachii. Following the routine procedure, the patient or nurse can use a pushbutton to mark seizures or other events. After the recording, the EEG is read together with the video of potential seizures by an experienced EEG technician and then by a neurologist and clinical neurophysiologist. This way we created a database containing seizures types, and their EMG correlates in each patient. Results: Eight out of 34 patients had motor seizures during the recording. Multiple seizure types occurred in two patients. One patient showed multiple clonic seizures. One patient showed two clear tonic-clonic seizures. Tonic seizures were found in three patients and myoclonias in five patients. All major seizures were also detected by video. However, EMG improved the characterization of the onset. Additionally, epileptic myoclonus was frequently difficult to distinguish by video alone, and EMG was required to define negative epileptic myoclonus. Conclusion: The EMG can have added value in differentiating epileptic motor seizures from non-epileptic motor seizures. Furthermore, the EMG might provide additional information about the timing and the exact location of motor seizures. Future perspectives: We will develop methods for automatic seizure detection, based on EMG signals. An algorithm was created within Kempenhaeghe [1] to detect tonic seizures based on EMG signals in combination with accelerometry data. Also algorithms based on EMG alone were proposed by Conradsen et al. [2]. We plan to investigate which parameter(s) can distinguish epileptic motor activity from non-epileptic movements. Furthermore, we plan to find parameters that can help to automatically detect motor seizures. REFERENCES [1] R.A.P.P. Adriaans, Strategies for a real-time detection system of the tonic phase of nocturnal generalized tonic and tonic-clonic epileptic seizures. Master thesis report, Technical University of Eindhoven, 2012. [2] I. Conradsen, S. Beniczky, K. Hoppe, P. Wolf, H.B.D. Sorendsen, Automated algorithm for generalized tonic-clonic epileptic seizure onset detection based on sEMG zero-crossing rate. IEEE Biomedical Engineering, VOL. 59, NO 2, February 2012