Development of Artificial Intelligence to Support Needle Electromyography Diagnostic Analysis / 대한의료정보학회지
Healthcare Informatics Research
;
: 131-138, 2019.
Article
Dans Anglais
| WPRIM
| ID: wpr-740231
ABSTRACT
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.
Texte intégral:
Disponible
Indice:
WPRIM (Pacifique occidental)
Sujet Principal:
Intelligence artificielle
/
Classification
/
Boidae
/
Électromyographie
/
Codage clinique
/
Potentiels de membrane
/
Méthodes
/
Aiguilles
Type d'étude:
Etude diagnostique
langue:
Anglais
Texte intégral:
Healthcare Informatics Research
Année:
2019
Type:
Article
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