Detection of interictal epileptiform discharges: A comparison of on-scalp MEG and conventional MEG measurements

Abstract

AbstractMagnetoencephalography (MEG) is an important part of epilepsy evaluations because of its unsurpassed ability to detect interictal epileptiform discharges (IEDs). This ability may be improved by next-generation MEG sensors, where sensors are placed directly on the scalp instead of in a fixed-size helmet, as in today’s conventional MEG systems. In order to investigate the usefulness of on-scalp MEG measurements we performed the first-ever measurements of on-scalp MEG on an epilepsy patient. The measurement was conducted as a benchmarking study, with special focus on IED detection. An on-scalp high-temperature SQUID system was utilized alongside a conventional low-temperature “in-helmet” SQUID system. EEG was co-registered during both recordings. Visual inspection of IEDs in the raw on-scalp MEG data was unfeasible why a novel machine learning-based IED-detection algorithm was developed to guide IED detection in the on-scalp MEG data. A total of 24 IEDs were identified visually from the conventional in-helmet MEG session (of these, 16 were also seen in the EEG data; eight were detected only by MEG). The on-scalp MEG data contained a total of 47 probable IEDs of which 16 IEDs were co-registered by the EEG, and 31 IEDs were on-scalp MEG-unique IEDs found by the IED detection algorithm. We present a successful benchmarking study where on-scalp MEG are compared to conventional in-helmet MEG in a temporal lobe epilepsy patient. Our results demonstrate that on-scalp MEG measurements are feasible on epilepsy patients, and indicate that on-scalp MEG might capture IEDs not seen by other non-invasive modalities.