Adapting an Automatic Speech Recognition System to Event Classification of Electroencephalograms1
| Autor: | Joseph Picone, R. Anstotz, Iyad Obeid, Vinit Shah |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
medicine.diagnostic_test
Event (computing) Computer science business.industry Speech recognition Electroencephalography Task (project management) 03 medical and health sciences Identification (information) ComputingMethodologies_PATTERNRECOGNITION 0302 clinical medicine Software medicine 030212 general & internal medicine Noise (video) Adaptation (computer science) Hidden Markov model business 030217 neurology & neurosurgery |
| Zdroj: | 2018 IEEE Signal Processing in Medicine and Biology Symposium (SPMB). |
| DOI: | 10.1109/spmb.2018.8615625 |
| Popis: | Identification of clinically significant events in electroencephalograms (EEGs) is a time-consuming task for neurologists [1]. EEG signals contain a variety of morphologies which relate to a combination of brain signals and noise/artifacts. Automated classification of such events has the potential to speed up the interpretation process and provide valuable input to other types of EEG decision-making software. Because of the similarities between EEGs and speech signals, both of which contain temporal/sequential information, one of our long-term goals has been to apply well-developed concepts from speech recognition to EEG processing. We have previously approached this by applying hidden Markov Models (HMMs) [2] [3] using a toolkit known as HTK [4]. In this poster, we discuss the application of a new high-performance speech recognition system known as Kaldi [5] to this task. Adaptation of this technology to the EEG problem has not been as straightforward as previously thought. |
| Databáze: | OpenAIRE |
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