عنوان انگلیسی مقاله:
Deep learning for waveform identification of resting needle electromyography signals
ترجمه فارسی عنوان مقاله:
یادگیری عمیق برای شناسایی شکل موج سیگنالهای الکترومیوگرافی سوزن ساکن
Sciencedirect - Elsevier - Clinical Neurophysiology, 130 (2019) 617-623: doi:10:1016/j:clinph:2019:01:024
Hiroyuki Nodera ⇑, Yusuke Osaki, Hiroki Yamazaki, Atsuko Mori, Yuishin Izumi, Ryuji Kaji
Objective: Given the recent advent in machine learning and artificial intelligence on medical data analysis,
we hypothesized that the deep learning algorithm can classify resting needle electromyography (n-
Methods: Six clinically observed resting n-EMG signals were used as a dataset. The data were converted
to Mel-spectrogram. Data augmentation was then applied to the training data. Deep learning algorithms
were applied to assess the accuracies of correct classification, with or without the use of pre-trained
weights for deep-learning networks.
Results: While the original data yielded the accuracy up to 0.86 on the test dataset, data-augmentation up
to 200,000 training images showed significant increase in the accuracy to 1.0. The use of pre-trained
weights (fine tuning) showed greater accuracy than ‘‘training from scratch”.
Conclusions: Resting n-EMG signals were successfully classified by deep-learning algorithm, especially
with the use of data augmentation and transfer learning techniques.
Significance: Computer-aided signal identification of clinical n-EMG testing might be possible by deeplearning
Keywords: Needle electromyography | Deep learning | Artificial neural network | Data augmentation | Resting discharge