A novel electrocardiogram feature extraction approach for cardiac arrhythmia classification

The use of energy sources boosts technological development over time, but the continuous and unrelenting usage of fossil fuel energy has resulted in harmful impacts to the ecosystem. Then came an alert in the scientific community, generating discourses and research in the field of renewable energy, for example, wind, solar and biofuels. Among various fields of study in wind power, this work is concerned in the study of electric generators, specifically in the detection of faults, using magnetic axial flux acquired from a squirrel cage induction generator (SCIG). We compare the performance of classifiers Bayes, Support Vector Machines, Optimum Path Forest and K- Nearest Neighbor in three different feature extraction methods, High Order Statistics (HOS), Fourier Transform and Structural Co-occurrence Matrix (SCM). We were able to correctly identify 99.4% of the Normal conditions using a Bayesian classifier alongside with as well identify incipient short-circuits with 95.6% of hit rate. SVM had a lowers false positive, 0.6% and 2% in HOS and SCM, respectively, while identifying 98% of the Normal conditions, so is more suitable for binary classification between fault and non-fault short-circuits.

An efficient ECG arrhythmia classification method based on Manta ray foraging optimization

Location is an elemental problem for mobile robotics due the importance of determining a position of the robot in space. This knowledge along with the environment map are basic information for robot mobility. In this paper, a new approach for navigation and location of mobile robots on topological maps using classification with reject option in attributes obtained from a Structural Co-occurrence Matrix (SCM) is proposed. Furthermore, we compare our approach with others state-of-the-art extractors, such as Statistical Moments, Gray-Level Co-occurrence Matrix (GLCM) and Local Binary Patterns (LBP). Structural Co-Occurrence Matrix was evaluated with the Average, Gaussian, Laplacian and Sobel filters. Regarding to classifiers, Bayesian classifier, Multilayer Perceptron (MLP) and Support Vector Machines (SVM) were analyzed. The descriptors Scale Invariant Feature Transform (SIFT) and Speed Up Robust Features (SURF) were also used. According to results, SCM was the fastest feature extractor with 0.117 s and accuracy of 100% in navigation test, showing the relevance of our approach in the mobile robot localization.

Induction motors are reported as the horse power in industries. Due to its importance, researchers studied how to predict its faults in order to improve reliability. Condition health monitoring plays an important role in this field, since it is possible to predict failures by analyzing its operational data. This paper proposes the usage of vibration signals, combined with Higher-Order Statistics (HOS) and machine learning methods to detect broken bars in a squirrel-cage three-phase induction motor. The Support Vector Machines (SVM), Multi-Layer Perceptron (MLP), Optimum-Path Forest and Naive-Bayes were used and have achieved promising results: high classification rate with SVM, high sensitivity rate with MLP and fast training convergence with OPF.

This paper presents a novel maximum margin clustering method with immune evolution (IEMMC) for automatic diagnosis of electrocardiogram (ECG) arrhythmias. This diagnostic system consists of signal processing, feature extraction, and the IEMMC algorithm for clustering of ECG arrhythmias. First, raw ECG signal is processed by an adaptive ECG filter based on wavelet transforms, and waveform of the ECG signal is detected; then, features are extracted from ECG signal to cluster different types of arrhythmias by the IEMMC algorithm. Three types of performance evaluation indicators are used to assess the effect of the IEMMC method for ECG arrhythmias, such as sensitivity, specificity, and accuracy. Compared with K-means and iterSVR algorithms, the IEMMC algorithm reflects better performance not only in clustering result but also in terms of global search ability and convergence ability, which proves its effectiveness for the detection of ECG arrhythmias.

Electrocardiogram (ECG) signal shows the different electrical activities of the heart. Since manual analysis is tedious and time-consuming, an offline automated ECG analysis and classification scheme is proposed in this study. Using Discrete Wavelet Transform (DWT) as feature extraction, this comparative study focused on ECG classification between Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM). It aims to classify normal and abnormal heartbeats with the addition of non-ECG signals. Abnormal heartbeats include ECG signals with atrial fibrillation and ventricular tachycardia. The ECG signals that were used as basis in comparing the results of the two pairings came from MIT-BIH Arrhythmia Database that is found on PhysioNet while the non-ECG signals came from another database which is related to stress recognition. ECG signal analysis in this study comprises three stages: the acquisition of signal from database, the feature extraction and the classification for determining the signal. In this study, SVM using the kernel function Radial Basis Function (RBF) paired with the mother wavelet Daubechies proved to be better than ANFIS paired with Haar mother wavelet.

Deep convolutional neural network optimized with hybrid marine predator’s and nomadic people optimization for cardiac arrhythmia classification using ECG signals

The 3D Discrete Wavelet Transform (DWT) and Support Vector Machine (SVM) are used in this study to analyze and characterize Electrocardiogram (ECG) signals. This technique consists of three stages: ECG signal preprocessing, feature extraction, and ECG signal order. The 3D wavelet transform is a signal preprocessing technique, de-noising, along with wavelet coefficient extraction.•SVM is used to categorize the ECG through each of the nine heartbeat types recognized by the various classifiers. For this work, around 6400 ECG beats were looked at over the China Physiological Signal Challenge (CPSC) 2018 arrhythmia dataset.•The best degree of exactness was acquired when level 4 rough constants with Symlet-8 (Sym8) channel were utilized for arrangement. Utilizing the ECG signals from CPSC 2018 data set, the SVM classifier has a normal precision of 99.02%, which is much better than complex support vector machine (CSVM) 98.5%, and weighted support vector machine (WSVM) 99%.•The suggested approach is far superior to others in terms of accuracy, and classification of several diseases of arrhythmia.

Automatic detection and classification of electrocardiogram (ECG) signals is of great importance for diagnosis of cardiac abnormalities. A method is proposed here to classify different cardiac abnormalities like Cardiomyopathy, Myocardial infarction, Dysrhythmia, Myocardial hypertrophy and Valvular heart disease. Support Vector Machine (SVM) has been used to classify the patterns inherent in the features extracted through Continuous Wavelet Transform (CWT) of different ECG signals. CWT allows a time domain signal to be transformed into time-frequency domain such that frequency characteristics and the location of particular features in a time series may be highlighted simultaneously. Thus it allows accurate extraction of feature from non-stationary signals like ECG. SVM transforms the multi-dimensional feature space into a linearly separable feature space with the help of Kernel function. In the present work, SVM in regression mode has been successfully applied for the classification of cardiac abnormalities with good diagnostic accuracy.

Automatic histologically-closer classification of skin lesions

Purpose:There has been burgeoning interest in applying machine learning methods for predicting radiotherapy outcomes. However, the imbalanced ratio of a large number of variables to a limited sample size in radiation oncology constitutes a major challenge. Therefore, dimensionality reduction methods can be a key to success. The study investigates and contrasts the application of traditional machine learning methods and deep learning approaches for outcome modeling in radiotherapy. In particular, new joint architectures based on variational autoencoder (VAE) for dimensionality reduction are presented and their application is demonstrated for the prediction of lung radiation pneumonitis (RP) from a large-scale heterogeneous dataset.Methods:A large-scale heterogeneous dataset containing a pool of 230 variables including clinical factors (e.g., dose, KPS, stage) and biomarkers (e.g., single nucleotide polymorphisms (SNPs), cytokines and micro-RNAs) in a population of 106 non-small cell lung cancer (NSCLC) patients who received radiotherapy was used for modeling RP. 22 patients had grade 2 or higher RP. Four methods were investigated, including feature selection (case A) and feature extraction (case B) with traditional machine learning methods, a VAE-MLP joint architecture (case C) with deep learning and lastly, the combination of feature selection and joint-architecture (case D). For feature selection, Random forest (RF), Support Vector Machine (SVM) and multi-layer perceptron (MLP) were implemented to select relevant features. Specifically, each method was run for multiple times to rank features within several cross-validated (CV) resampled sets. A collection of ranking lists were then aggregated by top 5% and Kemeny graph methods to identify the final ranking for prediction. A synthetic minority oversampling technique (SMOTE) was applied to correct for class imbalance during this process. For deep learning, a VAE-MLP joint architecture where a VAE aimed for dimensionality reduction and an MLP aimed for classification was developed. In this architecture, reconstruction loss and prediction loss were combined into a single loss function to realize simultaneous training and weights were assigned to different classes to mitigate class imbalance. To evaluate the prediction performance and conduct comparisons, the area under receiver operating characteristic (ROC) curves (AUCs) were performed for nested CVs for both handcrafted feature selections and the deep learning approach. The significance of differences in AUCs was assessed using the DeLong test of U-statistics.Results:An MLP-based method using weight pruning (WP) feature selection yielded the best performance among the different hand-crafted feature selection methods (case A), reaching an AUC of 0.804 (95% CI: 0.761–0.823) with 29 top features. A VAE-MLP joint architecture (case C) achieved a comparable but slightly lower AUC of 0.781 (95% CI: 0.737–0.808) with the size of latent dimension being 2. The combination of handcrafted features (case A) and latent representation (case D) achieved a significant AUC improvement of 0.831 (95% CI: 0.805–0.863) with 22 features (p-value = 0.000642 compared with handcrafted features only (Case A) and p-value=0.000453 compared to VAE alone (Case C)) with an MLP classifier.Conclusion:The potential for combination of traditional machine learning methods and deep learning VAE techniques has been demonstrated for dealing with limited datasets in modeling radiotherapy toxicities. Specifically, latent variables from a VAE-MLP joint architecture are able to complement handcrafted features for the prediction of RP and improve prediction over either method alone.

International audience

Knowledge discovery using neural approach for SME’s credit risk analysis problem in Turkey

Cutting analysis is a important and crucial task task to detect and prevent problems during the petroleum well drilling process. Several studies have been developed for drilling inspection, but none of them takes care about analysing the generated cutting at the vibrating shale shakers. Here we proposed a system to analyse the cutting's concentration at the vibrating shale shakers, which can indicate problems during the petroleum well drilling process, such that the collapse of the well borehole walls. Cutting's images are acquired and sent to the data analysis module, which has as the main goal to extract features and to classify frames according to one of three previously classes of cutting's volume. A collection of supervised classifiers were applied in order to allow comparisons about their accuracy and efficiency. We used the Optimum-Path Forest (OPF), Artificial Neural Network using Multilayer Perceptrons (ANN-MLP), Support Vector Machines (SVM) and a Bayesian Classifier (BC) for this task. The first one outperformed all the remaining classifiers. Recall that we are also the first to introduce the OPF classifier in this field of knowledge. Very good results show the robustness of the proposed system, which can be also integrated with other commonly system (Mud-Logging) in order to improve the last one's efficiency.

Effective compression and classification of ECG arrhythmia by singular value decomposition