Objective To summarize the classification, diagnosis, and treatment of iatrogenic bile duct injury. Method The clinical data of 27 cases of iatrogenic bile duct injuries who treated in Central Hospital of Huzhou City from 2008–2013 were retrospectively analyzed. Results The classification of 27 cases: 5 cases of type Ⅰ, 18 cases of type Ⅱ, 2 cases of type Ⅲ, 2 cases of type Ⅳ. Diagnosis: 11 cases were immediately discovered at the time of the initial operation, include 1 case of type Ⅰ, 8 cases of type Ⅱ, 1 case of type Ⅲ, 1 case of type Ⅳ; 10 cases were detected in early stage after the initial operation, include 2 cases of type Ⅰ, 7 cases of type Ⅱ, 1 case of type Ⅲ; 6 cases were detected in delayed stage after the initial operation, include 2 cases of type Ⅰ, 3 cases of type Ⅱ, 1 case of type Ⅳ. Treatment effect: 17 cases for excellent, 5 cases for good, 4 cases for bad, the well recover rate was 84.6% (22/26). One case died after operation. A total of 26 cases were followed up, 1 case was lost to follow up. During the follow-up period, bile leakage occurred in 3 cases, infection of incision occurred in 2 cases, cholangitis occurred in 3 cases, and bile duct stricture occurred in 2 cases. Conclusions The best time of repairing for the iatrogenic bile duct injuries is at the time of the initial operation or early stage. According to the type of injury and the time of the injury was diagnosed, timely and effective treatment by intervention and (or) surgery is the key.
Human motion control system has a high degree of nonlinear characteristics. Through quantitative evaluation of the nonlinear coupling strength between surface electromyogram (sEMG) signals, we can get the functional state of the muscles related to the movement, and then explore the mechanism of human motion control. In this paper, wavelet packet decomposition and n:m coherence analysis are combined to construct an intermuscular cross-frequency coupling analysis model based on wavelet packet-n:m coherence. In the elbow flexion and extension state with 30% maximum voluntary contraction force (MVC), sEMG signals of 20 healthy adults were collected. Firstly, the subband components were obtained based on wavelet packet decomposition, and then the n:m coherence of subband signals was calculated to analyze the coupling characteristics between muscles. The results show that the linear coupling strength (frequency ratio 1:1) of the cooperative and antagonistic pairs is higher than that of the nonlinear coupling (frequency ratio 1:2, 2:1 and 1:3, 3:1) under the elbow flexion motion of 30% MVC; the coupling strength decreases with the increase of frequency ratio for the intermuscular nonlinear coupling, and there is no significant difference between the frequency ratio n:m and m:n. The intermuscular coupling in beta and gamma bands is mainly reflected in the linear coupling (1:1), nonlinear coupling of low frequency ratio (1:2, 2:1) between synergetic pair and the linear coupling between antagonistic pairs. The results show that the wavelet packet-n:m coherence method can qualitatively describe the nonlinear coupling strength between muscles, which provides a theoretical reference for further revealing the mechanism of human motion control and the rehabilitation evaluation of patients with motor dysfunction.
Synchronization analysis of electroencephalogram (EEG) and electromyogram (EMG) could reveal the functional corticomuscular coupling (FCMC) during the motor task in human. A novel method combining Gabor wavelet and transfer entropy (Gabor-TE) is proposed to quantitatively analyze the nonlinearly synchronous corticomuscular function coupling and direction characteristics under different steady-state force. Firstly, the Gabor wavelet transform method was used to acquire the local frequency-band signals of the EEG and EMG signals recorded from nine healthy controls simultaneously during performing grip task with four different steady-state forces. Secondly, the TE of local frequency-band was calculated and the unit area index of the transfer (ATE) was defined to quantitatively analyze the synchronous corticomuscular function coupling and direction characteristics under steady-state force. Lastly, the effect of EEG and EMG signal power spectrum on Gabor-TE analysis was explored. The results showed that the coupling strength in the beta band was stronger in EEG→EMG direction than in EMG→EEG direction, and the ATE values in the beta band in EEG→EMG direction decreased with the force increasing. It is also shown that the difference in TE values of gamma band present a varying regularity as the increase of force in both directions. In addition, EMG power spectrum was significantly correlated with the result of Gabor-TE inspecific frequency band. The results of our study confirmed that Gabor-TE can quantitatively describe the nonlinearly synchronous corticomuscular function coupling in both local frequency band and information transmission. The analysis of FCMC provides basic information for exploring the motor control and the evaluation of clinical rehabilitation.
To better analyze the problem of abnormal neuromuscular coupling related to motor dysfunction for stroke patients, the functional coupling of the multichannel electromyography (EMG) were studied and the difference between stroke patients and healthy subjects were further analyzed to explore the pathological mechanism of motor dysfunction after stroke. Firstly, the cross-frequency coherence (CFC) analysis and non-negative matrix factorization (NMF) were combined to construct a CFC-NMF model to study the linear coupling relationship in bands and the nonlinear coupling characteristics in different frequency ratios during elbow flexion and extension movement. Furthermore, the significant coherent area and sum of cross-frequency coherence were respectively calculated to quantitatively describe the intermuscular linear and nonlinear coupling characteristics. The results showed that the linear coupling relationship between multichannel muscles was different in frequency bands and the overall coupling was stronger in low frequency band. The linear coupling strength of the stroke patients was lower than that of the healthy subjects in different frequency bands especially in beta and gamma bands. For the nonlinear coupling, the intermuscular coupling strength of stroke patients in different frequency ratios was significantly lower than that of the healthy subjects, and the coupling strength in the frequency ratio 1∶2 was higher than that in the frequency ratio 1∶3. This method can provide a theoretical basis for exploring the intermuscular coupling mechanism of patients with motor dysfunction.
In order to fully explore the neural oscillatory coupling characteristics of patients with mild cognitive impairment (MCI), this paper analyzed and compared the strength of the coupling characteristics for 28 MCI patients and 21 normal subjects under six different-frequency combinations. The results showed that the difference in the global phase synchronization index of cross-frequency coupling under δ-θ rhythm combination was statistically significant in the MCI group compared with the normal control group (P = 0.025, d = 0.398). To further validate this coupling feature, this paper proposed an optimized convolutional neural network model that incorporated a time-frequency data enhancement module and batch normalization layers to prevent overfitting while enhancing the robustness of the model. Based on this optimized model, with the phase locking value matrix of δ-θ rhythm combination as the single input feature, the diagnostic accuracy of MCI patients was (95.49 ± 4.15)%, sensitivity and specificity were (93.71 ± 7.21)% and (97.50 ± 5.34)%, respectively. The results showed that the characteristics of the phase locking value matrix under the combination of δ-θ rhythms can adequately reflect the cognitive status of MCI patients, which is helpful to assist the diagnosis of MCI.
Stroke is an acute cerebrovascular disease in which sudden interruption of blood supply to the brain or rupture of cerebral blood vessels cause damage to brain cells and consequently impair the patient's motor and cognitive abilities. A novel rehabilitation training model integrating brain-computer interface (BCI) and virtual reality (VR) not only promotes the functional activation of brain networks, but also provides immersive and interesting contextual feedback for patients. In this paper, we designed a hand rehabilitation training system integrating multi-sensory stimulation feedback, BCI and VR, which guides patients' motor imaginations through the tasks of the virtual scene, acquires patients' motor intentions, and then carries out human-computer interactions under the virtual scene. At the same time, haptic feedback is incorporated to further increase the patients' proprioceptive sensations, so as to realize the hand function rehabilitation training based on the multi-sensory stimulation feedback of vision, hearing, and haptic senses. In this study, we compared and analyzed the differences in power spectral density of different frequency bands within the EEG signal data before and after the incorporation of haptic feedback, and found that the motor brain area was significantly activated after the incorporation of haptic feedback, and the power spectral density of the motor brain area was significantly increased in the high gamma frequency band. The results of this study indicate that the rehabilitation training of patients with the VR-BCI hand function enhancement rehabilitation system incorporating multi-sensory stimulation can accelerate the two-way facilitation of sensory and motor conduction pathways, thus accelerating the rehabilitation process.
Brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP) have attracted much attention in the field of intelligent robotics. Traditional SSVEP-based BCI systems mostly use synchronized triggers without identifying whether the user is in the control or non-control state, resulting in a system that lacks autonomous control capability. Therefore, this paper proposed a SSVEP asynchronous state recognition method, which constructs an asynchronous state recognition model by fusing multiple time-frequency domain features of electroencephalographic (EEG) signals and combining with a linear discriminant analysis (LDA) to improve the accuracy of SSVEP asynchronous state recognition. Furthermore, addressing the control needs of disabled individuals in multitasking scenarios, a brain-machine fusion system based on SSVEP-BCI asynchronous cooperative control was developed. This system enabled the collaborative control of wearable manipulator and robotic arm, where the robotic arm acts as a “third hand”, offering significant advantages in complex environments. The experimental results showed that using the SSVEP asynchronous control algorithm and brain-computer fusion system proposed in this paper could assist users to complete multitasking cooperative operations. The average accuracy of user intent recognition in online control experiments was 93.0%, which provides a theoretical and practical basis for the practical application of the asynchronous SSVEP-BCI system.
The electroencephalogram (EEG) signal is a general reflection of the neurophysiological activity of the brain, which has the advantages of being safe, efficient, real-time and dynamic. With the development and advancement of machine learning research, automatic diagnosis of Alzheimer’s diseases based on deep learning is becoming a research hotspot. Started from feedforward neural networks, this paper compared and analysed the structural properties of neural network models such as recurrent neural networks, convolutional neural networks and deep belief networks and their performance in the diagnosis of Alzheimer’s disease. It also discussed the possible challenges and research trends of this research in the future, expecting to provide a valuable reference for the clinical application of neural networks in the EEG diagnosis of Alzheimer’s disease.