Classification of deep vein thrombosis stages using convolutional neural network of electromyogram with vibrotactile stimulation toward developing an early diagnostic tool: A preliminary study on a pig model

• Published in: PloS one Vol. 18; no. 2; p. e0281219
• Main Authors: Kang, Jong Woo, Kim, Keun-Tae, Park, Jong Woong, Lee, Song Joo
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 02.02.2023
• Subjects: Algorithms; Analysis; Animals; Artificial neural networks; Biology and Life Sciences; Catheters; Classification; Compression therapy; Diagnosis; Disease prevention; Electromyography; Evaluation; Experiments; Hogs; Leg; Mechanical properties; Medical diagnosis; Medical research; Medicine and Health Sciences; Modulus of elasticity; Muscle, Skeletal; Neural networks; Neural Networks, Computer; Physical Sciences; Principal components analysis; Research and Analysis Methods; Signal processing; Stimuli; Surgery; Swine; Tactile stimuli; Thromboembolism; Thrombosis; Ultrasonic imaging; Veins & arteries; Venous thrombosis; Venous Thrombosis - diagnosis; Vibration; Vibrations; South Korea; United Kingdom > UK
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0281219

Deep vein thrombosis (DVT) can lead to life-threatening disorders; however, it can only be recognized after its symptom appear. This study proposed a novel method that can detect the early stage of DVT using electromyography (EMG) signals with vibration stimuli using the convolutional neural networks (CNN) algorithm. The feasibility of the method was tested with eight legs before and after the surgical induction of DVT at nine-time points. Furthermore, perfusion pressure (PP), intracompartmental pressure (IP), and shear elastic modulus (SEM) of the tibialis anterior were also collected. In the proposed method, principal component analysis (PCA) and CNN were used to analyze the EMG data and classify it before and after the DVT stages. The cross-validation was performed in two strategies. One is for each leg and the other is the leave-one-leg-out (LOLO), test without any predicted information, for considering the practical diagnostic tool. The results showed that PCA-CNN can classify before and after DVT stages with an average accuracy of 100% (each leg) and 68.4±20.5% (LOLO). Moreover, all-time points (before induction of DVT and eight-time points after DVT) were classified with an average accuracy of 72.0±11.9% which is substantially higher accuracy than the chance levels (11% for 9-class classification). Based on the experimental results in the pig model, the proposed CNN-based method can classify the before- and after-DVT stages with high accuracy. The experimental results can provide a basis for further developing an early diagnostic tool for DVT using only EMG signals with vibration stimuli.