Biomedical subjects

Wenyan Jia

Publications and source records attributed to Wenyan Jia.

3 recordsLinked to original sources

PubMed2005

An epileptic seizure prediction algorithm based on second-order complexity measure.

The quality of life of many epilepsy patients may be improved significantly if the occurrence of epileptic seizures can be successfully forecasted and clinical intervention, such as electrical stimulation or drug delivery, can then be used to suppress their emergence, or warn the patient of the forthcoming events. In this paper, a prediction algorithm based on the second-order complexity measure was proposed to predict the impending seizures. Through the analysis of long-term intracranial EEG recordings from two frontal lobe epilepsy patients, the results indicated that the sensitivity of prediction was 77.8% (14/18) and 66.7% (4/6) and the number of false warnings was 3 and 2 for the two patients, respectively. Because only the information of past seizures was utilized to predict the current seizure and the computation load was low, the prediction algorithm could possibly be applied to clinical practice.

Adolescent↗

PubMed2004

Mu rhythm-based cursor control: an offline analysis.

OBJECTIVE: To classify the EEG data recorded in mu rhythm-based cursor control experiments with 4 possible choices. METHODS: The algorithm included preprocessing, feature extraction, and classification. Two spatial filters, common average reference and common spatial subspace decomposition, were used in preprocessing to improve the signal-to-noise ratio, and then two features were extracted based on the power spectrum and the time course of the mu rhythm respectively. A Fisher ratio was defined to select channels in feature extraction. A 2-dimensional linear classifier was trained for final classification. RESULTS: Two types of classifiers were trained for the training dataset. The uniform classifier gave a classification accuracy of 76.4%, and the classifier trained by leave-one-out method gave a classification accuracy of 74.4%, both higher than the online accuracy 69.5%. The uniform classifier was applied to the test dataset and the classification accuracy was 65.9%, lower than the online accuracy 73.2%. CONCLUSIONS: Spatial filtering can give a notable improvement in classification accuracy. The time course of the mu rhythm, as well as the power of the mu rhythm, shows difference between the 4 targets, and can contribute to the classification. SIGNIFICANCE: The spatial filtering, feature extraction and channel selection methods in the algorithm will provide some practical suggestions for further study on the mu rhythm-based brain-computer interface.

Algorithms↗

PubMed2004

[The progress in epileptic seizure prediction].

It is estimated that epilepsy, a chronic disorder of the nervous system, affects about 0.5%-2% of the population and about 10%-50% do not respond well to current antiepileptic medications and may not be candidates for surgery. For these patients, the unpredictability of seizure onset is a major cause of disability and mortality. Therefore, anticipation of an imminent seizure would be beneficial to patients because it could provide time for the application of preventive measures to keep the risk of seizure to a minimum. This paper reviews the feasibilities, the progress, existing problems and possible applications in the field of epileptic seizure prediction.

Algorithms↗