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Updated: May 14, 2026
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Published on: April 2, 2018
Machine learning based classification of intraoperative EMG signals recorded during brain tumor surgeries: a pooled
Ahmet Çil1, Enes Halit Aydın1, Önder Aydemir1
1Department of Electrical and Electronics Engineering, Karadeniz Technical University, Trabzon, Turkey.
Abstract:
This study analyzes a publicly available, 7-class iEMG dataset from West China Hospital to prevent nerve damage during brain tumor surgery. Trees, SVM, KNN, Neural Networks, Random Forest, Naive Bayes and 1D-CNN, LSTM, CNN-LSTM models were evaluated. Through data preprocessing, the 80.42% accuracy achieved by Random Forest on original data with a 250 ms window was increased to 97.13% using Bagged Trees on processed data. The study identified 150 ms as the optimal window size for 94.72% accuracy and rapid response. These findings contribute to the literature by establishing the critical balance between speed and accuracy for intraoperative nerve protection.