Automated speech analysis to improve TMS based language mapping: Algorithm and proof of concept
Transcranial magnetic stimulation (TMS) during confrontational naming can induce anomia or speech arrest and paraphasias and those can be used to locate language areas. This method is qualitative and time consuming since a trained professional needs to analyze a long video file of the mapping session. For data analysis we adapted an existing speech analysis method [1] to cope with TMS noise, which exceeds 100 dB SPL with each discharge of the coil. Our algorithm deciphered what was said and the reaction time (RT) of each utterance with a resolution of up to 10ms, leading to a fast and quantitative analysis. To develop the noise reduction in combination with the speech analysis algorithms, semi synthetic data were created, since it is impossible for the human ear to discern speech onset in the loud TMS noises. During a confrontational naming task (without TMS) voice recordings were obtained and RTs were manually annotated. Separately, multiple realistic recordings of rTMS noise were recorded, using a figure8 coil. Semi synthetic data were then created by fusion of the rTMS noises (at the onset) and the voicerecordings with some random delay. Over 1000 recordings were created. Actual patient data were recorded from three male patients planned for neurosurgery near language areas. Setup was in accordancewith published guidelines [2] with two no TMS recordings as baseline and TMS mapping of language areas using a 5Hz 1s stimulation at an intensity of 120% of resting motor threshold. Stimulation targets were recorded (Brainsight, Rogue Research, Canada) for offline analysis. All three had direct cortical stimulation (DCS) during awake surgery which we used as gold standard. The study was approved by the local Ethics Committee of the University Hospitals Leuven. All patients gave written informed consent. The algorithm contrasted the data likelihood for the model of the expected response with the data likelihood for a generic model of words. An internal penalty parameter avoided the generic model from winning inappropriately. In this task, we adjusted the weight to recognize the correct response in order to maximize the true negatives correctly (but reducing true positives) Speech enhancement front end suppressed background noises and rTMS noise [3,4] recordings were decomposed in a weighted sum of thousands of spectro temporal exemplars of speech and noise and the noise components were suppressed. The speech recognizer yielded temporal alignments with differing offsets depending on the beginning phoneme of the response: these biases were compensated in the post processing stage. The algorithm could handle synonyms and new pictures, since the word models were built by joining phoneme models automatically. As output a
Brain Stimulation