Recurrent neural networks with composite features for detection of electrocardiographic changes in partial epileptic patients

Automatic Classification System of Arrhythmias Using 12-Lead ECGs with a Deep Neural Network Based on an Attention Mechanism

Nowadays, a series of social problems caused by cardiovascular diseases are becoming increasingly serious. Accurate and efficient classification of arrhythmias according to an electrocardiogram is of positive significance for improving the health status of people all over the world. In this paper, a new neural network structure based on the most common 12-lead electrocardiograms was proposed to realize the classification of nine arrhythmias, which consists of Inception and GRU (Gated Recurrent Units) primarily. Moreover, a new attention mechanism is added to the model, which makes sense for data symmetry. The average F1 score obtained from three different test sets was over 0.886 and the highest was 0.919. The accuracy, sensitivity, and specificity obtained from the PhysioNet public database were 0.928, 0.901, and 0.984, respectively. As a whole, this deep neural network performed well in the multi-label classification of 12-lead ECG signals and showed better stability than other methods in the case of more test samples.

The Role of Machine Learning in Knowledge-Based Response-Adapted Radiotherapy

With the continuous increase in radiotherapy patient-specific data from multimodality imaging and biotechnology molecular sources, knowledge-based response-adapted radiotherapy (KBR-ART) is emerging as a vital area for radiation oncology personalized treatment. In KBR-ART, planned dose distributions can be modified based on observed cues in patients' clinical, geometric, and physiological parameters. In this paper, we present current developments in the field of adaptive radiotherapy (ART), the progression toward KBR-ART, and examine several applications of static and dynamic machine learning approaches for realizing the KBR-ART framework potentials in maximizing tumor control and minimizing side effects with respect to individual radiotherapy patients. Specifically, three questions required for the realization of KBR-ART are addressed: (1) what knowledge is needed; (2) how to estimate RT outcomes accurately; and (3) how to adapt optimally. Different machine learning algorithms for KBR-ART application shall be discussed and contrasted. Representative examples of different KBR-ART stages are also visited.

Electrocardiogram analysis using a combination of statistical, geometric, and nonlinear heart rate variability features

OBJECTIVE

The paper addresses a common and recurring problem of electrocardiogram (ECG) classification based on heart rate variability (HRV) analysis. Current understanding of the limits of HRV analysis in diagnosing different cardiac conditions is not complete. Existing research suggests that a combination of carefully selected linear and nonlinear HRV features should significantly improve the accuracy for both binary and multiclass classification problems. The primary goal of this work is to evaluate a proposed combination of HRV features. Other explored objectives are the comparison of different machine learning algorithms in the HRV analysis and the inspection of the most suitable period T between two consecutively analyzed R-R intervals for nonlinear features.

METHODS AND MATERIAL

We extracted 11 features from 5min of R-R interval recordings: SDNN, RMSSD, pNN20, HRV triangular index (HTI), spatial filling index (SFI), correlation dimension, central tendency measure (CTM), and four approximate entropy features (ApEn1-ApEn4). Analyzed heart conditions included normal heart rhythm, arrhythmia (any), supraventricular arrhythmia, and congestive heart failure. One hundred patient records from six online databases were analyzed, 25 for each condition. Feature vectors were extracted by a platform designed for this purpose, named ECG Chaos Extractor. The vectors were then analyzed by seven clustering and classification algorithms in the Weka system: K-means, expectation maximization (EM), C4.5 decision tree, Bayesian network, artificial neural network (ANN), support vector machines (SVM) and random forest (RF). Four-class and two-class (normal vs. abnormal) classification was performed. Relevance of particular features was evaluated using 1-Rule and C4.5 decision tree in the cases of individual features classification and classification with features' pairs.

RESULTS

Average total classification accuracy obtained for top three classification methods in the two classes' case was: RF 99.7%, ANN 99.1%, SVM 98.9%. In the four classes' case the best results were: RF 99.6%, Bayesian network 99.4%, SVM 98.4%. The best overall method was RF. C4.5 decision tree was successful in the construction of useful classification rules for the two classes' case. EM and K-means showed comparable clustering results: around 50% for the four classes' case and around 75% for the two classes' case. HTI, pNN20, RMSSD, ApEn3, ApEn4 and SFI were shown to be the most relevant features. HTI in particular appears in most of the top-ranked pairs of features and is the best analyzed feature. The choice of the period T for nonlinear features was shown to be arbitrary. However, a combination of five different periods significantly improved classification accuracy, from 70% for a single period up to 99% for five periods.

CONCLUSIONS

Analysis shows that the proposed combination of 11 linear and nonlinear HRV features gives high classification accuracy when nonlinear features are extracted for five periods. The features' combination was thoroughly analyzed using several machine learning algorithms. In particular, RF algorithm proved to be highly efficient and accurate in both binary and multiclass classification of HRV records. Interpretable and useful rules were obtained with C4.5 decision tree. Further work in this area should elucidate which features should be extracted for the best classification results for specific types of cardiac disorders.

Recurrent neural networks for diagnosis of carpal tunnel syndrome using electrophysiologic findings

This paper presents the use of recurrent neural networks (RNNs) for diagnosis of carpal tunnel syndrome (CTS) (normal, right CTS, left CTS, bilateral CTS). The RNN is trained with the Levenberg-Marquardt algorithm. The RNN is trained on the features of CTS (right median motor latency, left median motor latency, right median sensory latency, left median sensory latency). The multilayer perceptron neural network (MLPNN) is also implemented for comparison the performance of the classifiers on the same diagnosis problem. The total classification accuracy of the RNN is significantly high (94.80%). The obtained results confirmed the validity of the RNNs to help in clinical decision-making.