Accelerometer-based automatic voice onset detection in speech mapping with navigated repetitive transcranial magnetic stimulation

被引:17
|
作者
Vitikainen, Anne-Mari [1 ,2 ,3 ]
Makela, Elina [1 ,2 ]
Lioumis, Pantelis [1 ,2 ,4 ]
Jousmaki, Veikko [5 ,6 ]
Makela, Jyrki P. [1 ,2 ]
机构
[1] Univ Helsinki, BioMag Lab, HUS Med Imaging Ctr, FI-00029 Helsinki, Finland
[2] Helsinki Univ Hosp, FI-00029 Helsinki, Finland
[3] Univ Helsinki, Dept Phys, FI-00014 Helsinki, Finland
[4] Univ Helsinki, Neurosci Ctr, FI-00014 Helsinki, Finland
[5] Aalto Univ, Sch Sci, Dept Neurosci & Biomed Engn, FI-00076 Espoo, Finland
[6] Aalto Univ, Sch Sci, Aalto NeuroImaging, FI-00076 Espoo, Finland
关键词
Presurgical planning; Navigated TMS (nTMS); Object naming; Speech mapping; DIRECT CORTICAL STIMULATION; BRAIN-STIMULATION; SURGERY; NTMS;
D O I
10.1016/j.jneumeth.2015.05.015
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Background: The use of navigated repetitive transcranial magnetic stimulation (rTMS) in mapping of speech-related brain areas has recently shown to be useful in preoperative workflow of epilepsy and tumor patients. However, substantial inter- and intraobserver variability and non-optimal replicability of the rTMS results have been reported, and a need for additional development of the methodology is recognized. In TMS motor cortex mappings the evoked responses can be quantitatively monitored by electromyographic recordings; however, no such easily available setup exists for speech mappings. New method: We present an accelerometer-based setup for detection of vocalization-related larynx vibrations combined with an automatic routine for voice onset detection for rTMS speech mapping applying naming. Comparison with existing method(s): The results produced by the automatic routine were compared with the manually reviewed video-recordings. Results: The new method was applied in the routine navigated rTMS speech mapping for 12 consecutive patients during preoperative workup for epilepsy or tumor surgery. The automatic routine correctly detected 96% of the voice onsets, resulting in 96% sensitivity and 71% specificity. Majority (63%) of the misdetections were related to visible throat movements, extra voices before the response, or delayed naming of the previous stimuli. The no-response errors were correctly detected in 88% of events. Conclusion: The proposed setup for automatic detection of voice onsets provides quantitative additional data for analysis of the rTMS-induced speech response modifications. The objectively defined speech response latencies increase the repeatability, reliability and stratification of the rTMS results. (C) 2015 Elsevier B.V. All rights reserved.
引用
收藏
页码:70 / 77
页数:8
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