Title :
Seizure detection on prolonged-EEG videos
Author :
Shen, Yu Ting ; Chung, Pau Choo ; Thonnet, Monnique ; Chauvel, Patrick
Author_Institution :
Dept. of Electr. Eng., Nat. Cheng Kung Univ., Tainan
Abstract :
This paper develops the fusion of audio and video features by Dempster-Shafer theory for seizure detection. In audio analysis, Mel frequency cepstral coefficient (MFCC) and zero-crossing rate (ZCR) are applied to hidden Markov model (HMM) for audio type classification and probability computation. The results are transferred to belief of evidence and combined with the results from videos. Results have been tested by data obtained from several seizure patients and showed promising results.
Keywords :
audio signal processing; electroencephalography; hidden Markov models; medical image processing; medical signal detection; video signal processing; Dempster-Shafer theory; HMM; Mel frequency cepstral coefficient; audio type classification; hidden Markov model; prolonged-EEG videos; seizure detection; zero-crossing rate; Cepstral analysis; Cepstrum; Electroencephalography; Epilepsy; Feature extraction; Hidden Markov models; Mel frequency cepstral coefficient; Storms; Testing; Videos; Dempster-Shafer fusion; Hidden Markov Model (HMM); multimodal fusion; seizure detection;
Conference_Titel :
Circuits and Systems, 2008. ISCAS 2008. IEEE International Symposium on
Conference_Location :
Seattle, WA
Print_ISBN :
978-1-4244-1683-7
Electronic_ISBN :
978-1-4244-1684-4
DOI :
10.1109/ISCAS.2008.4541846
