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.

ABSTRACT: 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. On

SUBMITTER: Kang JW 

PROVIDER: S-EPMC9894458 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature