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Long-term monitoring of resting tremor is key to assess the status of patients suffering from Parkinson’s disease (PD), which is of vital importance for reasonable medication. The detection and quantification of resting tremor in reported works rely heavily on specified movements and are not appropriate for long-term monitoring in real-life condition. The purpose of this study is to develop a detection model for long-term monitoring of resting tremor and explore an effective indicator for tremor quantification. This study included long-term acceleration data from PD patients and proposed a resting tremor detection model based on machine learning classifiers and Synthetic Minority Oversampling Technique (SMOTE). Four machine learning classifiers, K-Nearest Neighbor (KNN), Random Forest (RF), Adaptive Boosting (AdaBoost), and Support Vector Machine (SVM), were compared. Furthermore, an indicator called tremor timing ratio (TTR) was defined and calculated for tremor quantification. The detection model with RF classifier achieved the highest overall accuracy of 94.81%. The sample entropy of the acceleration signal was proved most influential in the classification by exploring the feature importance. Through the Kruskal-Wallis test and the Mann-Whitney U test, the TTR had a strong correlation with the subscore of resting tremor in Unified Parkinson Disease Rating Scale (UPDRS). Such two-step evaluation process for resting tremor can detect the tremor effectively and is expected to be applied in long-term monitoring of PD patients in daily life to realize a more comprehensive assessment of PD.

Objective Atrial fibrillation (AF) is the most common type of persistent arrhythmia. Early diagnosis and intervention of AF is essential to avert the further fatality. The technique of noninvasive electrical mapping, especially the body surface potential mapping (BSPM), has a more practical application in the study of predicting AF, when compared with the invasive electrical mapping methods such as the epicardial mapping and interventional catheter mapping. However, the prediction of AF with noninvasive signals has been inadequately studied. Thus, the aim of this paper was to analyze the properties of atrial dynamic system based on the noninvasive BSPM signals (BSPMs), using the recurrence complex network, and consequently to evaluate its role in predicting the recurrence of AF in clinical aspect. Method Twelve patients with persistent AF were included in this study. Their preoperative and postoperative BSPMs were recorded. Initially, the preoperative BSPMs were transformed into the recurrence complex network to characterize the complexity property of the atria. Subsequently, the parameters of recurrence ratio (REC), determinism (DET), entropy of the diagonal structure distribution (ENTR), and laminarity (LAM) were calculated. Furthermore, the difference in the parameters in the four regions of the body and the difference obtained from the dominant frequency (DF) method were compared. Finally, the results obtained for the atrial dynamic system complexity from a 12-lead electrocardiogram (ECG) from the BSPMs were discussed. Results Our study revealed that the patients whose REC is greater than an average threshold, and with a lower LAM presented a much higher possibility of AF recurrence, after the AF surgery. Conclusions The recurrence complex network is a useful and convenient way to evaluate the nonlinear properties of the BSPMs in patients with AF. It has good immunity to the lead position and has a potential role in the understanding of predicting the recurrence of AF.

Although radio frequency ablation is the most effective treatment for atrial fibrillation (AF), there is still a high recurrence rate. The purpose of this paper was to initially assess the probability of the recurrence of AF based on the preoperative body surface potential mapping (BSPM) signals, in other words, to predict the efficiency of ablation and assist physicians in developing more effective treatment options. At present, deep learning methods based on convolutional neural networks (CNNs) do not require complex mathematical abstractions or manual interventions; thus, higher computation efficiency can be obtained in such research. However, the use of the fully connected multi-layer perceptron (MLP) algorithms has shown low classification performance. This paper proposes an improved CNN algorithm (CNN-SVM method) for the recurrence classification in AF patients by combining with the support vector machine (SVM) architecture. The algorithm is validated on the preoperative AF signals of 14 patients for classification. All postoperative patients are followed up for one year; ten of them remain in sinus rhythm, whereas the other four turn back to AF. The ECG data for these patients are obtained through the 128-Lead BSPM system. The results show that the proposed CNN-SVM method can automatically extract the characteristic information through the CNN network. The constructed model ultimately achieved an accuracy of 96%, a sensitivity of 88%, and a specificity of 96%. It is concluded that the CNN-SVM method solves the drawbacks of MLP only for separating linear data. It improves the overall performance of AF recurrence classification, thereby providing a valuable reference for doctors to develop personalized treatment plans.

Atrial fibrillation (AF) is a common clinical arrhythmia with a lifetime risk and has become an attractive topic of clinical research and health management. Although the 12-lead classical ECG configuration is adequate for the detection of AF, its configuration is suboptimal for constructing surface potential mapping or extracting other diagnostic features during AF. Body surface potential mapping (BSPM) has a broad application prospect in AF study, but the electrode wiring is complicated and the hardware cost is high. This paper proposes a method to improve the performance of leads selection algorithm from a 128-lead BSPM system with equidistantly spaced electrodes. 128-lead BSPM data obtained from 8 AF patients were used in the study. Leads selection was performed by a sequential selection based on an adapted Botteron’s approach and multiple linear regression. The result showed that the proposed method has significant improvement on the performance of the accurate reconstruction of dominant frequency on the patient-specific dataset. The proposed method may not only improve the performance on the spectrum characteristic but also effectively improve the performance of time domain criteria when the number of selected leads is above 12.

Background: Tic disorders (TD) are common neuropsychiatric disorders among children and adolescents. It is controversial that trace elements may participate in the pathogenesis of TD. Our study aimed to investigate the trace elements status of zinc (Zn), copper (Cu), iron (Fe), and magnesium (Mg) in children with TD, in comparison to healthy controls.Methods: The medical records of eligible TD children and normal healthy children from January 1 to December 31, 2018 in the outpatient clinic were retrospectively reviewed. The clinical information of all subjects were collected including age, gender, diagnosis, previous health records, and serum trace elements level (Cu, Zn, Fe, Mg) at the time of diagnosis before initiating treatment.Results: In total, 1204 TD children (7.63 ± 2.45 years) and 1,220 healthy children (7.27 ± 3.15 years) who were divided into two gender and three age groups (2–4years, 5–9years, ≥10 years) were reviewed in our study. Our study showed that TD children generally had lower whole blood levels of Zn, Cu, Fe than the normal controls (P < 0.01). No significant difference was observed in whole blood levels of Mg. After adjusting for gender, the trends still remained. Further analysis was performed according to age, the trends still remained in Zn and Fe in all age groups (P < 0.05). However, we observed an almost significantly (P = 0.055) lower level of Cu in TD of 2–4 years group while significant differences in other two groups (P < 0.01). Further multiple linear regression and point biserial correlation showed that the lower blood levels of Zn, Cu, and Fe were correlated with the incidence of TD.Conclusion: The present results indicated that lower blood levels of zinc, iron, copper were associated with TD. Trace elements may be used as an auxiliary treatment for TD and need to be further explored.

Liver regeneration (LR) happens after various types of injuries. Unlike the well-studied LR caused by partial hepatectomy (PHx), there is accumulating evidence suggesting that LR during other injuries may result from unknown mechanisms. In this study, we found that insulin-like growth factor 2 (IGF-2) was drastically induced following the liver injuries caused by tyrosinemia or long-term treatments of CCl4. However, this was not observed during the early phase of acute liver injuries after PHx or single treatment of CCl4. Remarkably, most IGF-2-expressing hepatocytes were located at the histological area around the central vein of the liver lobule after the liver injuries caused either in fumarylacetoacetate hydrolase–deficient mice or in CCl4 chronically treated mice. Hepatocyte proliferation in vivo was significantly promoted by induced IGF-2 overexpression, which could be inhibited by adeno-associated virus–delivered IGF-2 short hairpin RNAs or linsitinib, an inhibitor of IGF-2 signaling. Proliferating hepatocytes in vivo responded to IGF-2 through both insulin receptor and IGF-1 receptor. IGF-2 also significantly promoted DNA synthesis of primary hepatocytes in vitro. More interestingly, the significantly induced IGF-2 was also found to colocalize with glutamine synthetase in the region enriched with proliferating hepatocytes for the liver samples from patients with liver fibrosis. Conclusion: IGF-2 is produced by pericentral hepatocytes to promote hepatocyte proliferation and repair tissue damage in the setting of chronic liver injury, which is distinct from the signaling that occurs post-PHx. (Hepatology 2017;66:2002–2015)

Up to now, SCN9A mutations encoding Nav1.7 have been limited to inherited pain syndromes. A few of pathogenic SCN9A mutations with or without SCN1A mutations have been identified in epileptic patients. Here, we report two heterozygous SCN9A mutations with no SCN1A mutations, which are associated with variable epilepsy phenotypes and explored the possibility of SCN9A contributing to a multifactorial etiology for epilepsy. Our findings suggest that the two SCN9A mutations (c.980G>A chr2:167149868 p.G327E; c.5702_5706del chr2:167055410 p.I1901fs) should be regarded as pathogenic mutations. Two heterozygous mutations of SCN9A are associated with a wide clinical spectrum of seizure phenotypes including simple febrile seizures, afebrile seizures, generalized tonic-clonic seizure, myoclonic or tonic seizures, and focal clonic seizures. Patients with deletion mutations tend to be associated with more severe seizure type than missense mutations.

Atrial fibrillation (AF) is one of the most common arrhythmias. The automatic AF detection is of great clinical significance but at the same time it remains a big problem to researchers. In this study, a novel deep learning method to detect AF was proposed. For a 10 s length single lead electrocardiogram (ECG) signal, the continuous wavelet transform (CWT) was used to obtain the wavelet coefficient matrix, and then a convolutional neural network (CNN) with a specific architecture was trained to discriminate the rhythm of the signal. The ECG data in multiple databases were divided into 4 classes according to the rhythm annotation: normal sinus rhythm (NSR), atrial fibrillation (AF), other types of arrhythmia except AF (OTHER), and noise signal (NOISE). The method was evaluated using three different wavelet bases. The experiment showed promising results when using a Morlet wavelet, with an overall accuracy of 97.56%, an average sensitivity of 97.56%, an average specificity of 99.19%. Besides, the area under curve (AUC) value is 0.9983, which showed that the proposed method was effective for detecting AF.

Objective Atrial fibrillation inducibility has recently been shown to be associated with the increased clinical recurrence after ablation. Previous studies have identified unstable sinus rhythm before the occurrence of paroxysmal AF, yet earlier subtle changes, related to AF inducibility, are less be investigated. The purposed of this study is to predict AF inducibility, here we established a controllable canine model and prepared various sinus rhythms with different AF inducibility to study atrial electrophysiological changes. Methods The data were derived from acetylcholine induced acute canine AF models (n = 5, data samples = 90) through epicardial mapping system using 64 electrodes attached to the left and right atrial appendages. Signal preprocessing consists of three steps: noise reduction, removal of ventricular artifact and extraction of activation time (AT). Two classifiers based on activating rule and spatiotemporal features, visualized by network mapping, were established by binary logical regression analysis. Results The sensitivity, specificity and accuracy of activation analysis are 83.30%, 91.70% and 87.80% respectively, while those of spatiotemporal analysis are apparently increased to 97.60%, 93.80% and 95.56%. We observed that the atria (in sinus rhythm) with more disordered electrophysiological activity was more vulnerable to AF. Conclusion The high accuracies indicate the feasibility of AF inducibility prediction using sinus rhythm electrocardiogram, especially with the aid of superior spatiotemporal analysis. Significance To our knowledge, this is the first study that demonstrates the possibility of predicting AF inducibility using network mapping. With this study, accurate AF inducibility forecasting may help to evaluate the recurrence of AF after ablation.

Atrial fibrillation (AF) is an abnormal heart rhythm. The goal of radiofrequency ablation for AF is to regain a normal heart rhythm. Presently, the commonly used algorithms for prediction of AF recurrence face constraints such as no flexible feature selection and optimization. Deep learning algorithm, such as convolutional neural networks (CNN), can overcome these drawbacks by calculating features automatically. This study presents a noninvasive ECG-based approach with deep learning algorithm to predict AF recurrence according to preoperative AF signals of 14 patients. The features of cardiac activity can be extracted from the convolution neural network and then fed to multilayer perceptron (MLP) for classification. We have evaluated the result by measuring accuracy (ACC), sensitivity (SE) and specificity (SP). In the meantime, the optimal CNN combination of parameters are showed in a table. Our proposed method has shown good merits with ACC of 93.14%, SE of 83.5% and SP of 95.99%. It can be concluded that BSPM would contain more potential information because of its wide range of spatial coverage compared with traditional signal acquisition system, and the deep learning algorithm of CNN has more advantages in feature extraction for prediction of AF recurrence.

The purpose of this study is to predict atrial fibrillation (AF) from epicardial signals by investigating the recurrence property of atrial activity dynamic system before AF. A novel scheme is proposed to predict AF by using multi-threshold spectra of the recurrence complex network. Firstly, epicardial signals are transformed into the recurrence complex network to quantify structural properties of the recurrence in the phase space. Spectral parameters with multi-threshold are used to characterize the global structure of the network. Then the feature sequential forward searching algorithm and mutual information based Maximum Relevance Minimum Redundancy criterion are used to find the optimal feature set. Finally, a support vector machine is used to predict the occurrence of AF. This method is assessed on the pre-AF epicardial signals of canine which includes the normal group A (no further AF will happen), the mild group B (the following AF time is less than 180s) and the severe group C (the following AF time is more than 180s). 25 optimal features are selected out of 180 features from each sample. With these features, sensitivity, specificity and accuracy are 99.40%, 99.70% and 99.60%, respectively, which are the best among the recurrence based methods. The results suggest that the proposed method can predict AF accurately and thus can be prospectively used in the postoperative evaluation.

Atrial fibrillation (AF) can result in life-threatening arrhythmia, and a clinically convenient means for detecting vulnerability remains elusive. We investigated atrial vulnerability by analyzing the sinus electrogram (EG) from AF animal models using a phase synchronization method. Using acetylcholine (ACh)-induced acute canine AF models (n = 4), a total of 128 electrical leads were attached to the surface of the anterior and posterior atria, and the pulmonary veins to form an electrocardiological mapping system. ACh was injected at varying concentrations with ladder-type adjustments. Sinus EGs and induced AF EGs that pertain to specific ACh concentrations were recorded. We hypothesize that the atrial vulnerability may be correlated with the Shannon entropy (SE) of the phase difference matrix that is extracted from the sinus EG. Our research suggests that the combination of SE with the synchronization method enables the sinus node EG to be analyzed and used to estimate atrial vulnerability.

Atrial Fibrillation (AF), a chaotic rhythm classically considered with random electrical activity, is now demonstrated to show a certain degree of organization and synchronization. Rather than those traditional indices which always focus on the pairwise properties of adjacent signals, a new synchronization index-S estimator-is introduced in this paper to quantify the synchronization level for all the signals in a selected area. By evaluating a complement of the entropy of the normalized eigenvalues of the corresponding correlation matrix, S estimator is designed to be proportional to the amount of synchronization. 400 episodes of 64-channel epicardial signals acquired from four living mongrels were studied under normal sinus rhythm (SN) and AF. The results showed that there were significant decreases of S estimator for both anterior left atrium and anterior right atrium with the rhythm changing from SN to AF. After dividing the research area into eight subparts, S estimator is also capable to demonstrate the different synchronization level for each subpart and revealed the electrophysiology individual difference among four experimental subjects. In conclusion, S estimator succeeds in estimating the synchronization degree for multi-channel signals in a selected area, with no limits to the number of the signals to be analyzed. It can help us to distinguish the region with a high synchronization level during AF, which would be helpful to the clinical AF treatment and enhance our understanding of underlying mechanisms of AF.

Complex network spectra features are proposed to be used by the classifier to classify atrial fibrillation (AF) and normal sinus rhythm (NSR). This novel complex network construction method utilizes the fuzzy symbolic dynamics (FSD) and recurrence complex network to analyze the synchronization of cardiac electrical activity. Firstly, the multi-lead epicardial signals recorded from dogs are transformed into the FSD plot to construct a complex network by using the recurrence complex network theory. Then, network spectra features are extracted. Finally, epicardium signals are classified into AF and NSR patterns by using the fuzzy c-means algorithm (FCM). The method is validated with three sample sets from dogs A, B and C. The samples are NSR and AF segments which are all from dog experiments. The sensitivity (SE), specificity (SP) and accuracy (AC) of the identification for set A are 98.2%, 98.9% and 98.6% respectively, 96.1%, 98.6%, 97.5% for set B, and 97.7%, 98.7%, 98.4% for set C. The experiments indicate that this approach is effective in AF detection.

The methods of dominant frequency and Lorenz plot are used in this study to evaluate the activation rate and the activation rate variability of cardiac signals during atrial fibrillation. An epicardial mapping system was applied to acquire the atrial electrogram of mongrel dogs. The dominant frequency and Lorenz plot of each signal from various myocardial regions of the atria were analyzed. Our results show that both a frequency gradient and a variability gradient exist in the atria and the roots of pulmonary veins. The dominant frequencies of the anterior atria are higher than the posterior ones and the activation variability of both atria was higher than those of the pulmonary veins. A combination of these two methods may provide a more comprehensive understanding of the electrophysiology mechanism associated with atrial fibrillation.

In order to get a better understanding of atrial fibrillation, we introduced a method based on Graph Theory to interpret the relations of different parts of the atria. Atrial electrograms under sinus rhythm and atrial fibrillation were collected from eight living mongrel dogs with cholinergic AF model. These epicardial signals were acquired from 95 unipolar electrodes attached to the surface of the atria and four pulmonary veins. Then, we analyzed the electrode correlations using Graph Theory. The topology, the connectivity and the parameters of graphs during different rhythms were studied. Our results showed that the connectivity of graphs varied from sinus rhythm to atrial fibrillation and there were parameter gradients in various parts of the atria. The results provide spatial insight into the interaction between different parts of the atria and the method may have its potential for studying atrial fibrillation.

The atrial electricity activity is a complex dynamical system and shares the recurrence property of a fundamental characteristic as many other dynamical systems. We transform multi-electrode epicardium signals into the recurrence complex network to quantify the structure properties of recurrence in the phase space. The analysis results suggest that the proportions of the motifs occurring in the networks will vary significantly with the change of the atrial dynamical structure. By using four-node motifs parameters, we can predict atrial fibrillation successfully with the total accuracy of 90.76%, which has a significant meaning for the postoperative evaluation.

In order to have an intuitive grasp of atrial fibrillation model using ECG mapping technique, we started from animals' CT images, extracted contours of torso surface and epicardium with Snake model, reconstructed three-dimensional models of torso surface and epicardium with triangular patches. Using interpolation based on Delaunay triangulation, signals (acquired from animal experiments) of normal sinus rhythm (NSR) and atrial fibrillation (AF) were displayed in 3D perspective. And the important process in cardiac electrical activity, such as the start and ending parts of spontaneous AF, could be clearly observed.

The mechanisms of sustained atrial fibrillation (AF) are not well understood. And predicting the development of AF is a problem of great clinical interest. This paper proposed an AF analysis method by evaluating, based on Shannon entropy, the complexity of atrial activation in various AF stages. The first step was to preprocess and characterize electrograms. Then, Shannon entropy analysis and statistical analysis were applied to find the significance of interval entropy in sustained AF. Study results proved that interval entropy presented a degressive tendency in the process of sustained AF and some sites with high activation frequency but low entropy was possibly related to ectopic driver of AF.

Low energy defibrillation can not only save the power of the device (especially the ICD) but also lighten the damage to the patients. This paper investigates the method to decrease the defibrillation energy by optimizing the pulse duration of electrical shock. The defibrillation threshold is one of the main parameter to evaluate the defibrillation dosage. In this paper, the defibrillation threshold corresponding to the pulse duration is analysed and studied through 7 animal trails by an appropriative defibrillator designed by ourselves.The defibrillation thresholds are changing along with the changing of the pulse duration and existing optimal pulse duration that corresponding to the lowest defibrillation threshold. Although this optimal pulse duration is not so steady under different conditions in our animal trails but it showed a nice possibility to decrease the defibrillation energy by optimizing the pulse duration.

Epicardial mapping system is an important tool for studying electrophysiological characteristics of atrial fibrillation (AF). AF is the most common arrhythmia and is becoming more prevalent each year. Most in vivo experimental research on AF is performed by simultaneous epicardial mapping technique. An epicardial mapping system is developed to record electrical activities from 128 sites at the same time, with a spacing of 3.5-5.0 mm. The initial purpose of this research is to display the electrical signals on the whole-atrial epicardial surface and store these data for subsequent analysis. 128 unipolar electrodes arranged in eight flexible patches were adopted to detect signal on the surface of whole atrium. Each electrode patch was designed to adaptable to the corresponding epicardial surface of canine atrium. The system contained an amplifier with 128 isolated channels and a data acquisition card based on USB communication. Random acquisition duration of interested data during sinus or AF rhythm could be recorded. Animal tests were operated on four mongrel dogs, in which two were models of chronic AF. Result shows that this system can perform successfully in a surgical setting. This system may be a special and portable tool for whole-atrium epicardial mapping and therefore useful for studying AF.

Dynamic epicardial mapping system is a useful instrument for studying electrophysiological characteristics of atrial fibrillation (AF). It is important to develop an optimal therapy for AF. The purpose of this research is to show the electrical activity on the epicardial surface of atrium and therefore find the appropriate technique or ablation procedures to prevent AF. It emphasizes on dynamic threedimensional (3D) holograph based on whole-atrial epicardial mapping. A system was designed to display successively either sampling waveform or the pattern of depolarization distribution. 64 unipolar electrodes arranged in eight flexible patches were adopted to detect signals. They can be located respectively on the surface of posterior and anterior LA or RA, and pulmonary veins (4 patches). Each electrode patch was designed to adaptable to the corresponding epicardial surface of canine atrium. Animal tests were operated on ten dogs in which chronic AF had been induced. Each 20-second sampling data during sinus or AF rhythm were recorded and stored for analysis. Multilevel B-splines were applied to compute the whole atria surface through a set of irregularly spaced point. Thus the 3D isopotential map was drawn to reflect the conduction of depolarization wave dynamically during AF. The propagation of electrical activity was different during sinus and AF rhythm. The results demonstrate that the system could find the pathway of AF through 3D dynamic exhibition. Nevertheless, 64 sites are not enough to detect the electrical activity thoroughly. A multi-site epicardial mapping system more than 128 channels is being developed for more exact mapping.

When heart is in ventricular fibrillation or atrial fibrillation, its electrical activities are very complex not only on spatial distribution but also on temporal variation. It is very difficult to find out its rules by traditional points by points mapping method. The 64/176-channel epicardial mapping system developed by Fudan University can be used to study the complex arrhythmias and has acquired some interesting results from the animal trials.

Epicardial mapping system is an important tool in the study and treatment of cardiac arrhythmias. Surgical therapy has been applied to eliminate atrial fibrillation (AF) for almost two decades, but there is still little effect on the treatment of AF in respect that the mechanism of AF is still unknown. Further investigation into the electrophysiological properties in AF is required to develop an appropriate treatment, though radiofrequency ablation has opened the new era of therapies for AF. Nevertheless, the sequential epicardial mapping for whole-atrium will increase the benefit to understand electrical mechanism during AF. The purpose of our research is to detect the electrical activity on the atrial surface and therefore find the optimal technique or ablation procedures to prevent AF. Animal tests were operated on ten dogs in which chronic AF had been induced. In experiment whole-atrial electrodes were located on the atrial surface after the heart had been exposed. Each 20-second sampling data during sinus or atrial fibrillation rhythm were recorded and stored for analysis. Three-dimensional dynamic maps are presented clearly and the activity of sinus or AF rhythm can be seen quite differently. The active isopotential map can display the dynamic electrical conduction of the atria as a movie. This study demonstrates the flexibility of the system in AF research.

To treat sudden arrhythmias often happened in cardiosurgical operations, e.g., atrial fibrillation, ventricular fibrillation and ventricular tachycardia, it was necessary to develop special low-energy cardioverter-defibrillator with precise energy measurement and impedance compensation, working in both epicardial manner and endocardial manner. The system consisted of 4 main parts: cardioverter-defibrillator module, R synchronization module, impedance detector and power supply module. High-voltage circuit was isolated from low-voltage circuit to insure the security. The System could discharge with biphasic truncated exponential waveform rapidly; precision of R wave identification was high; impedance classification compensation could be fulfilled; discharge energy measurement was precise. So the system has the ability for cardioversion and defibrillation in both epicardial and endocardial manner in cardiosurgical operations.