Machine learning based classification of intraoperative EMG signals recorded during brain tumor surgeries: a pooled data approach for large-scale analysis and real-time applications

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Çil A., Aydın E. H., Aydemir Ö.

COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING, sa.7, ss.1-12, 2026 (SCI-Expanded, Scopus)

• Yayın Türü: Makale / Tam Makale
• Basım Tarihi: 2026
• Doi Numarası: 10.1080/10255842.2026.2670517
• Dergi Adı: COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING
• Derginin Tarandığı İndeksler: Scopus, Science Citation Index Expanded (SCI-EXPANDED), BIOSIS, Compendex, EMBASE, INSPEC, MEDLINE
• Sayfa Sayıları: ss.1-12
• Karadeniz Teknik Üniversitesi Adresli: Evet

Özet

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 pre processing, the 80.42% accuracy achieved by Random Forest on original data with a 250ms win dow was increased to 97.13% using Bagged Trees on processed data. The study identified 150ms as the optimal window size for 94.72% accuracy and rapid response. These findings con tribute to the literature by establishing the critical balance between speed and accuracy for intraoperative nerve protection.