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. 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.

Attention-assisted hybrid CNN-BILSTM-BiGRU model with SMOTE–Tomek method to detect cardiac arrhythmia based on 12-lead electrocardiogram signals

“…To address the data imbalance, they employed the SMOTE–Tomek technique and achieved 92.86% accuracy. 71 Rai et al introduced a combined CNN-LSTM model to detect six types of cardiac arrhythmias based on samples from the MIT-BIH and PTB Diagnostic datasets. The data imbalance issue was addressed using the SMOTE–Tomek technique with an accuracy of 99.2%.…”

Venous congestion affects neuromuscular changes in pigs in terms of muscle electrical activity and muscle stiffness

“…The vibrating stimulus was just used to characterize muscle stiffness, not for therapeutic purposes. In our previous study [13], we investigated to determine how deep the vibration can be transmitted from a vibration motor attached to the thigh skin of pigs. The acceleration data was acquired at four randomized points in the thigh muscles to examine the PLOS ONE impact of vibration stimulation on vibration transmission depending on the depth of the soft tissue.…”

“…The depth was measured using an ultrasound machine. On average, the vibrations were detected at a depth of 3.06±0.08 cm beneath the skin, and were not detectable at a depth of 3.89±0.08 cm beneath the skin [ 40 ]. When considering the application of vibration to the human calf, which is the most susceptible area for human DVT, a transmission depth of 3~4 cm is sufficient to reach the calf muscles and is shallower than the deep veins [ 41 ].…”

“…To validate our hypothesis, we performed experiments on anesthetized pigs’ legs. The pig leg model was chosen due to its close resemblance to the condition of completely obstructive symptomatic proximal DVT in a human limb [ 13 ]. Additionally, we induced VC in three stages to observe the progressive neuromechanical changes that occur during the progression of acute VC.…”