NDLI: Classification of patient's reaction in language assessment during awake craniotomy

Content Provider	IEEE Xplore Digital Library
Author	Nishimura, T. Nagao, T. Iseki, H. Muragaki, Y. Tamura, M. Minami, S.
Copyright Year	2014
Description	Author affiliation: Fac. of Adv. Technol. & Surg., Tokyo Women's Med. Univ., Tokyo, Japan (Iseki, H.; Muragaki, Y.; Tamura, M.) \|\| Grad. Sch. of Environ. & Inf. Sci., Yokohama Nat. Univ., Yokohama, Japan (Nishimura, T.; Nagao, T.) \|\| SOLIZE Corp., Tokyo, Japan (Minami, S.)
Abstract	Surgical video recording is widely used in operation rooms in order to analyze such as surgical procedures and intraoperative incident detection. Therefore, a number of useful operation video records are stored in the hospitals. It is considered that these video records contain significant information, so it is needed to utilize these video data. In awake craniotomy, which is one of the advanced neurological surgery, surgeon performs direct electrical stimulation to patient's brain area during linguistic tasks(such as, naming objects or generating verbs) in order to detect brain functional areas. The electrical stimulation of the cortical speech area causes temporary speech arrest. Hence, video segments which speech arrest is caused are significant in terms of surgical video analysis. The electrical stimulation timings are obtained from sound information, however that segments are not tagged speech arrest or not. In this paper, we report on the performance of a classification method for classifying patient's response for linguistic tasks just after electrical stimulation. In order to extract patient's speech features, we used melfrequency cepstrum coefficient(MFCC) and its delta parameters which are often used in speech recognition. We used Relevance Vector Machine(RVM) and Support Vector Machine(SVM) for classification and compared their results. We applied RVM and SVM for extracted patient's speech features and evaluated in F-measure. The classifier achieves in classification rates about 80[%] in 10-fold cross validation. The result shows that speech features are effective for classifying patient's responses.
Starting Page	207
Ending Page	212
File Size	1659360
Page Count	6
File Format	PDF
ISBN	9781479947713
ISSN	18833977
e-ISBN	9781479947706
DOI	10.1109/IWCIA.2014.6988107
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-11-07
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Surgery Awake Craniotomy Speech Feature extraction Mel frequency cepstral coefficient Kernel MFCC Surgical Records Pragmatics Relevance Vector Machine
Content Type	Text
Resource Type	Article

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