Inter-patient ECG heartbeat classification for arrhythmia classification: a new approach of multi-layer perceptron with weight capsule and sequence-to-sequence combination

Objective: The objective of this research is to construct a method to alleviate the problem of sample imbalance in classification, especially for arrhythmia classification. This approach can improve the performance of the model without using data enhancement.Methods: In this study, we have developed a new Multi-layer Perceptron (MLP) block and have used a Weight Capsule (WCapsule) network with MLP combined with sequence-to-sequence (Seq2Seq) network to classify arrhythmias. Our work is based on the MIT-BIH arrhythmia database, the original electrocardiogram (ECG) data is classified according to the criteria recommended by the American Association for Medical Instrumentation (AAMI). Also, our method’s performance is further evaluated.Results: The proposed model is evaluated using the inter-patient paradigm. Our proposed method shows an accuracy (ACC) of 99.88% under sample imbalance. For Class N, sensitivity (SEN) is 99.79%, positive predictive value (PPV) is 99.90%, and specificity (SPEC) is 99.19%. For Class S, SEN is 97.66%, PPV is 96.14%, and SPEC is 99.85%. For Class V, SEN is 99.97%, PPV is 99.07%, and SPEC is 99.94%. For Class F, SEN is 97.94%, PPV is 98.70%, and SPEC is 99.99%. When using only half of the training sample, our method shows that the SEN of Class N and V is 0.97% and 5.27% higher than the traditional machine learning algorithm.Conclusion: The proposed method combines MLP, weight capsule network with Seq2seq network, effectively addresses the problem of sample imbalance in arrhythmia classification, and produces good performance. Our method also shows promising potential in less samples

Classification of epileptic EEG signals based on simple random sampling and sequential feature selection

Electroencephalogram (EEG) signals are used broadly in the medical fields. The main applications of EEG signals are the diagnosis and treatment of diseases such as epilepsy, Alzheimer, sleep problems and so on. This paper presents a new method which extracts and selects features from multi-channel EEG signals. This research focuses on three main points. Firstly, simple random sampling (SRS) technique is used to extract features from the time domain of EEG signals. Secondly, the sequential feature selection (SFS) algorithm is applied to select the key features and to reduce the dimensionality of the data. Finally, the selected features are forwarded to a least square support vector machine (LS_SVM) classifier to classify the EEG signals. The LS_SVM classifier classified the features which are extracted and selected from the SRS and the SFS. The experimental results show that the method achieves 99.90, 99.80 and 100 % for classification accuracy, sensitivity and specificity, respectively

Source identification and pattern study of closed coal mines water inflow in Songzao Mining Area, Chongqing City

Accurate identification of the source of water gushing in closed coal mines and correct division of water gushing modes are of great significance for scientific disposal of water resources waste and water environment pollution caused by closed coal mine drainage. A comprehensive method for water inflow characterization, source identification, and model research for closed coal mines by multivariate analysis of “water quantity–hydrochemistry–microorganism–hydrogeological conditions” is proposed. The method is based on the dynamic monitoring data of water inflow and the water chemical and microbial indexes of several closed coal mines in the Songzao mining area of Chongqing in a hydrological year. Water quality analysis methods, such as flow dynamic analysis of water inflow and flow–rainfall hydro-logical series correlation function, descriptive statistics of water chemical indexes, and the Pearson correla-tion function of water chemical indexes between mine water samples are also used as bases. The method is further coupled with the hydrogeological conditions of the mining area. Results show that there are three types of fluctuations in the response of water inflow from closed coal mines to rainfall: sudden rise and slow drop, slow rise and slow drop, and stable. The difference in water inflow source and water diversion medium is the main reason for the dynamic change in mine water inflow and the temporal and spatial differences in its response to rainfall. It also causes the characteristics of large variability in TDS, pH, chemical correlation degree, and microbial content of mine water. Based on water source identification, four types, rainfall infiltration type, aquifer release type, old empty water overflow type, and compound type, of water gushing modes of closed coal mines in mining areas are proposed. The multivariate comprehensive analysis method identifies the source of water inrush from closed coal mines in karst mining areas effectively, deepens the understanding of the characteristics of water inrush from closed coal mines, and provides theoretical support for the scientific prevention and control of closed coal mine water inrush in Songzao mining area and the coordinated development of environment and resources

Minimum node degree of k-connected vehicular ad hoc networks in highway scenarios

A vehicular ad hoc network (VANET) is a specific type of mobile ad hoc networks (MANETs); it can provide direct or multi-hop vehicle-to-vehicle (V2V), vehicle-to-roadside (V2R), vehicle-to-pedestrian (V2P), and vehicle-to-internet (V2I)communications based on the pre-existing road layouts. The emerging and promising VANET technologies have drawn tremendous attention from the government, academics, and industry over the past few years and have been increasingly available for a large number of cutting edge applications that can be classified into road safety, traffic efficiency, and infotainment categories. Due to the unique characteristics of VANETs, such as high mobility with an organized but constrained pattern, and diverse radio propagation conditions, the conventional researches dedicated for general MANETs cannot be directly applied to VANETs. This paper presents an analytical framework to investigate the minimum node degree of k-connected VANETs, with a homogeneous range assignment in highway scenarios. We simulate the mobility patterns with realistic vehicular traces, model the network topology as a two-path fading geometric random graph, and conduct extensive experiments on the derived analytical results. Through a combination of mathematical modeling and simulations, we derive a probabilistic bound for the minimum node degree of a homogeneous vehicular ad hoc network in highway scenarios. The analytical framework is useful in the study of connectivity and estimation of performance in one-dimensional vehicular ad hoc networks

A novel dilated contextual attention module for breast cancer mitosis cell detection

Background and object: Mitotic count (MC) is a critical histological parameter for accurately assessing the degree of invasiveness in breast cancer, holding significant clinical value for cancer treatment and prognosis. However, accurately identifying mitotic cells poses a challenge due to their morphological and size diversity.Objective: We propose a novel end-to-end deep-learning method for identifying mitotic cells in breast cancer pathological images, with the aim of enhancing the performance of recognizing mitotic cells.Methods: We introduced the Dilated Cascading Network (DilCasNet) composed of detection and classification stages. To enhance the model’s ability to capture distant feature dependencies in mitotic cells, we devised a novel Dilated Contextual Attention Module (DiCoA) that utilizes sparse global attention during the detection. For reclassifying mitotic cell areas localized in the detection stage, we integrate the EfficientNet-B7 and VGG16 pre-trained models (InPreMo) in the classification step.Results: Based on the canine mammary carcinoma (CMC) mitosis dataset, DilCasNet demonstrates superior overall performance compared to the benchmark model. The specific metrics of the model’s performance are as follows: F1 score of 82.9%, Precision of 82.6%, and Recall of 83.2%. With the incorporation of the DiCoA attention module, the model exhibited an improvement of over 3.5% in the F1 during the detection stage.Conclusion: The DilCasNet achieved a favorable detection performance of mitotic cells in breast cancer and provides a solution for detecting mitotic cells in pathological images of other cancers

Traveling wave solutions of a nonlocal dispersal predator–prey model with spatiotemporal delay

In this paper, we study the existence and nonexistence of traveling wave solution for the nonlocal dispersal predator–prey model with spatiotemporal delay. This model incorporates the Leslie–Gower functional response into the Lotka–Volterra-type system, and both species obey the logistic growth. We explore the existence of traveling wave solution for c≥c⋆ by using the upper-lower solutions and the Schauder’s fixed point theorem. Furthermore, the nonexistence of traveling wave solution for

A New Method of Tractor Engine State Identification Based on Vibration Characteristics

Based on signal decomposition, a tractor engine state recognition method is proposed to explore the degree of information recognition of vibration signals at measurement points at different distances from the engine and the degree of correlation in different directions. The accuracy of engine operating state information recognition was obtained by analyzing the vibration signals of the tractor at different measurement points. The main contents are as follows: Based on variational mode decomposition (VMD), the modal component, which includes the state information, was obtained by measuring the vibration signal of the tractor at each measurement point under different driving conditions, and the exogenous excitation of the tractor under different road conditions was simulated by changing the tire pressure. Then, the state characteristics of the modal component were quantified based on permutation entropy (PE), and the correlation coefficient was used as the evaluation index to select the entropy of the optimal modal component as the feature vector. Finally, a support vector machine and random forest classification models were trained with 4800 feature vectors under 25 working conditions, and the remaining 900 feature vectors were used to verify the classification results. Compared with the results of empirical mode decomposition (EMD), the superiority of this method was proved. A comparative study with backpropagation demonstrated the superiority of the support vector machine and random forest identification method using a small sample size. The results indicate the following: (1) the accuracy of engine condition recognition, which was measured by longitudinal vibration signals, was better than that of vertical vibration signals at different measurement points; and (2) the closer the vibration transmission distance between the measurement point and the engine, the higher the recognition accuracy of the measured signals. This study provides a reference for the condition identification of agricultural machinery in complex working environments and lays a foundation for the fault diagnosis of agricultural machinery under working conditions

A New Method of Tractor Engine State Identification Based on Vibration Characteristics

Based on signal decomposition, a tractor engine state recognition method is proposed to explore the degree of information recognition of vibration signals at measurement points at different distances from the engine and the degree of correlation in different directions. The accuracy of engine operating state information recognition was obtained by analyzing the vibration signals of the tractor at different measurement points. The main contents are as follows: Based on variational mode decomposition (VMD), the modal component, which includes the state information, was obtained by measuring the vibration signal of the tractor at each measurement point under different driving conditions, and the exogenous excitation of the tractor under different road conditions was simulated by changing the tire pressure. Then, the state characteristics of the modal component were quantified based on permutation entropy (PE), and the correlation coefficient was used as the evaluation index to select the entropy of the optimal modal component as the feature vector. Finally, a support vector machine and random forest classification models were trained with 4800 feature vectors under 25 working conditions, and the remaining 900 feature vectors were used to verify the classification results. Compared with the results of empirical mode decomposition (EMD), the superiority of this method was proved. A comparative study with backpropagation demonstrated the superiority of the support vector machine and random forest identification method using a small sample size. The results indicate the following: (1) the accuracy of engine condition recognition, which was measured by longitudinal vibration signals, was better than that of vertical vibration signals at different measurement points; and (2) the closer the vibration transmission distance between the measurement point and the engine, the higher the recognition accuracy of the measured signals. This study provides a reference for the condition identification of agricultural machinery in complex working environments and lays a foundation for the fault diagnosis of agricultural machinery under working conditions

Multiscale entropy algorithms and their applications in cardiac disease discrimination

In recent years, the number of cardiac disease patients has been increasing. Modern medical research has shown that the complexity of electrocardiogram (ECG) signals is related to cardiovascular diseases. This paper investigates the difference in complexity of ECG data from the people with different cardiovascular diseases, such as atrial fibrillation (AF), ventricular arrhythmia (VA) and congestive heart failure (CHF). The empirical mode decomposition (EMD) and multiscale entropy method are used to analyze the ECG data, and a mathematical model established by a support vector machine is used to identify different diseases. The accuracy recognition rate of the AF recognition is 96.25%, and that of the CHF and VA reach 90.26% and 92.20%, respectively. The experimental results show that the recognition method proposed in this paper is successful

Crustal Apparent Density Variations in the Middle Segment of the North Tianshan Mountains and Their Tectonic Significance

In this study, we collected mobile gravity observations in the middle segment of the North Tianshan Mountains from August 2016 to July 2022 and carried out classical adjustment calculations under the constraint of the absolute gravity datum to obtain the spatiotemporal variation pattern of the local gravity field. We used equivalent source inversion to obtain the spatiotemporal variation characteristics of crustal apparent density. We also extracted the coseismic deformation field from SAR data, using the 2016 Hutubi earthquake as an example, and constructed a model of the seismogenic fault. The gravity monitoring network in the study area performed well in resolving the earthquake source parameters. Both the time-varying gravity field and equivalent apparent density variation pattern show prominent zoning characteristics with a smoothly evolving spatial distribution over time. The variation trends of the gravity field and equivalent apparent density are in line with the orientation of tectonic structures, and their anomalous signals can be detected before and after an earthquake. The constructed seismogenic structure of the 2016 Hutubi earthquake indicates a typical thrust earthquake, probably occurring on a north-dipping blind fault beneath a region with intense crustal deformation. The subsurface tectonic system reflected by this earthquake can be informatively extended to the entire middle segment of the North Tianshan Mountains by subsurface configuration. Our findings can serve as a reference for analyzing the source characteristics of the time-varying gravity field and interpreting anomalous pre-seismic signals, and aid in understanding earthquake preparation zones and the mode of crustal tectonic movements