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A preliminary work of the nonconvulsive seizure detection system is presented here. The system aims at detecting nonconvulsive seizures for epilepsy patients, targeting a 24/7 monitoring based on continuous electroencephalography (EEG) signals. It has been observed that the interesting seizure-related brain activities in some of the multi-channel EEG signals were weak, often with a noisy background or artifacts, and this might also be patient-dependent. Therefore, using the 'best' channels with a good signal quality is expected to enhance the seizure detection performance. This paper describes a method to select the 'best' EEG channels adaptively from the data of nonconvulsive seizure patients. A signal quality index (SQI) was proposed, where a higher SQI of a channel (signal) indicates a stronger brain activity associated with the ictals of nonconvulsive seizures and less artifacts. The validity of the SQI for adaptive channel selection is demonstrated in this paper. Advantages and limitations of our proposed method were discussed.
|Title of host publication||2018 IEEE 23rd International Conference on Digital Signal Processing, DSP 2018|
|Place of Publication||Piscataway|
|Publisher||Institute of Electrical and Electronics Engineers|
|Number of pages||5|
|Publication status||Published - 31 Jan 2019|
|Event||23rd IEEE International Conference on Digital Signal Processing, DSP 2018 - Shanghai, China|
Duration: 19 Nov 2018 → 21 Nov 2018
|Conference||23rd IEEE International Conference on Digital Signal Processing, DSP 2018|
|Period||19/11/18 → 21/11/18|
- Channel selection
- nonconvulsive seizure
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