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KMID : 1100520190250020131
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Healthcare Informatics Research
2019 Volume.25 No. 2 p.131 ~ p.138
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Development of Artificial Intelligence to Support Needle Electromyography Diagnostic Analysis
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Nam Sang-Woo
Sohn Min-Kyun
Kim Hyun-Ah
Kong Hyoun-Joong
Jung Il-Young
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Abstract
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Objectives: This study proposes a method for classifying three types of resting membrane potential signals obtained as images through diagnostic needle electromyography (EMG) using TensorFlow-Slim and Python to implement an artificial-intelligence-based image recognition scheme.
Methods: Waveform images of an abnormal resting membrane potential generated by diagnostic needle EMG were classified into three types?positive sharp waves (PSW), fibrillations (Fibs), and Others?using the TensorFlow-Slim image classification model library. A total of 4,015 raw waveform data instances were reviewed, with 8,576 waveform images subsequently collected for training. Images were learned repeatedly through a convolutional neural network. Each selected waveform image was classified into one of the aforementioned categories according to the learned results.
Results: The classification model, Inception v4, was used to divide waveform images into three categories (accuracy = 93.8%, precision = 99.5%, recall = 90.8%). This was done by applying the pretrained Inception v4 model to a fine-tuning method. The image recognition model was created for training using various types of image-based medical data.
Conclusions: The TensorFlow-Slim library can be used to train and recognize image data, such as EMG waveforms, through simple coding rather than by applying TensorFlow. It is expected that a convolutional neural network can be applied to image data such as the waveforms of electrophysiological signals in a body based on this study.
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KEYWORD
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Artificial Intelligence, Deep Learning, Electromyography, Convolutional Neural Network, Classification
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