Classification of Patient's Reaction in Language Assessment during Awake Craniotomy
Classification of Patient's Reaction in Language Assessment during Awake Craniotomy
复制标题
清醒开颅手术中患者语言评估反应的分类
DOI：
10.1109/iwcia.2014.6988107
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
Manabu Tamura and Shinji Minami
中科院分区：
文献类型：
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作者：
Toshihiko Nishimura;Tomoharu Nagao;Hiroshi Iseki;Yoshihiro Muragaki;Manabu Tamura and Shinji Minami
手术视频记录广泛用于手术室，以便分析诸如手术过程和术中事件检测。因此，医院中存储了大量有用的手术视频记录。认为这些视频记录包含重要信息，因此需要利用这些视频数据。清醒开颅手术是一种先进的神经外科手术，外科医生在语言任务（如命名物体或产生动词）期间对患者的脑区进行直接电刺激，以检测脑功能区。对皮层语言区的电刺激会导致暂时的语言停止。因此，在手术视频分析方面，引起言语停滞的视频片段是重要的。从声音信息获得电刺激定时，然而，该片段未被标记为言语停止或未被标记为言语停止。在本文中，我们报告的分类方法的性能进行分类病人的语言任务的反应后，电刺激。为了提取患者的语音特征，我们使用了语音识别中常用的梅尔频率倒谱系数（MFCC）及其增量参数。我们使用相关向量机（RVM）和支持向量机（SVM）进行分类，并比较它们的结果。我们应用RVM和SVM提取患者的语音特征，并在F-测度进行评估。该分类器在10倍交叉验证中的分类率达到80%左右。结果表明，语音特征是有效的分类病人的反应。
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.