Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation

The electrocardiogram (ECG) is one of the most extensively employed signals used in the diagnosis and prediction of cardiovascular diseases (CVDs). The ECG signals can capture the heart's rhythmic irregularities, commonly known as arrhythmias. A careful study of ECG signals is crucial for precise diagnoses of patients' acute and chronic heart conditions. In this study, we propose a two-dimensional (2-D) convolutional neural network (CNN) model for the classification of ECG signals into eight classes; namely, normal beat, premature ventricular contraction beat, paced beat, right bundle branch block beat, left bundle branch block beat, atrial premature contraction beat, ventricular flutter wave beat, and ventricular escape beat. The one-dimensional ECG time series signals are transformed into 2-D spectrograms through short-time Fourier transform. The 2-D CNN model consisting of four convolutional layers and four pooling layers is designed for extracting robust features from the input spectrograms. Our proposed methodology is evaluated on a publicly available MIT-BIH arrhythmia dataset. We achieved a state-of-the-art average classification accuracy of 99.11\%, which is better than those of recently reported results in classifying similar types of arrhythmias. The performance is significant in other indices as well, including sensitivity and specificity, which indicates the success of the proposed method.Comment: 14 pages, 5 figures, accepted for future publication in Remote Sensing MDPI Journa

Kurdish Dialect Recognition using 1D CNN

Dialect recognition is one of the most attentive topics in the speech analysis area. Machine learning algorithms have been widely used to identify dialects. In this paper, a model that based on three different 1D Convolutional Neural Network (CNN) structures is developed for Kurdish dialect recognition. This model is evaluated, and CNN structures are compared to each other. The result shows that the proposed model has outperformed the state of the art. The model is evaluated on the experimental data that have been collected by the staff of department of computer science at the University of Halabja. Three dialects are involved in the dataset as the Kurdish language consists of three major dialects, namely Northern Kurdish (Badini variant), Central Kurdish (Sorani variant), and Hawrami. The advantage of the CNN model is not required to concern handcraft as the CNN model is featureless. According to the results, the 1 D CNN method can make predictions with an average accuracy of 95.53% on the Kurdish dialect classification. In this study, a new method is proposed to interpret the closeness of the Kurdish dialects by using a confusion matrix and a non-metric multi-dimensional visualization technique. The outcome demonstrates that it is straightforward to cluster given Kurdish dialects and linearly isolated from the neighboring dialects

Detection of EEG Signal Post-Stroke Using FFT and Convolutional Neural Network

Stroke is a condition that occurs when the blood supply to the brain is disrupted or reduced. It may be caused by a blockage (ischemic stroke) or rupture of a blood vessel (hemorrhagic stroke) so that it can cause disability. Therefore patients need to undergo rehabilitation. One of the procedures of monitoring of the recovery of stroke patients using the National Institutes of Health Stroke Scale (NIHSS) method, but sometimes subjectively. Electroencephalogram (EEG) is an instrument that can measure electrical activity in the brain, including abnormalities caused by stroke. This study investigates EEG signal detection in post-stroke patients using Fast Fourier Transform (FFT) and 1D Convolutional Neural Network (1D CNN). Fast Fourier Transform (FFT) extraction can increase accuracy from 60% to 80.3% from the use of Adam's optimization model. Meanwhile, the AdaDelta model gave 20% accuracy without FFT. And its condition increased to 79.9% with FFT extraction. Therefore, Adam's stability has the advantage of remembering to use hyper-parameter. On the other hand, FFT is beneficial for directing information used for the use of 1D CNN, thus increasing accuracy. The results showed that using of Fast Fourier Transform (FFT) in identification could increase accuracy by 45-80% compared to identification using only 1D CNN. Meanwhile, the results of the study show that the relative weight correction model using Adaptive Moment Estimation (Adam) provided higher accuracy compared to the Adaptive learning rate (AdaDelta)

Anomaly Detection And Classification In Time Series With Kervolutional Neural Networks

Recently, with the development of deep learning, end-to-end neural network architectures have been increasingly applied to condition monitoring signals. They have demonstrated superior performance for fault detection and classification, in particular using convolutional neural networks. Even more recently, an extension of the concept of convolution to the concept of kervolution has been proposed with some promising results in image classification tasks. In this paper, we explore the potential of kervolutional neural networks applied to time series data. We demonstrate that using a mixture of convolutional and kervolutional layers improves the model performance. The mixed model is first applied to a classification task in time series, as a benchmark dataset. Subsequently, the proposed mixed architecture is used to detect anomalies in time series data recorded by accelerometers on helicopters. We propose a residual-based anomaly detection approach using a temporal auto-encoder. We demonstrate that mixing kervolutional with convolutional layers in the encoder is more sensitive to variations in the input data and is able to detect anomalous time series in a better way.Comment: 9 pages, 1 figure, 4 table

Diagnosis electromechanical system by means CNN and SAE: an interpretable-learning study

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Cyber-physical systems are the response to the adaptability, scalability and accurate demands of the new era of manufacturing called Industry 4.0. They will become the core technology of control and monitoring in smart manufacturing processes. In this regard, the complexity of industrial systems implies a challenge for the implementation of monitoring and diagnosis schemes. Moreover, the challenges that is presented in technological aspects regarding connectivity, data management and computing are being resolved through different IT-OT (information technology and operational technology) convergence proposals. These solutions are making it possible to have large computing capacities and low response latency. However, regarding the logical part of information processing and analysis, this still requires additional studies to identify the options with a better complexity-performance trade-off. The emergence of techniques based on artificial intelligence, especially those based on deep-learning, has provided monitoring schemes with the capacity for characterization and recognition in front of complex electromechanical systems. However, most deep learning-based schemes suffer from critical lack of interpretability lying to low generalization capabilities and overfitted responses. This paper proposes a study of two of the main deep learning-based techniques applied to fault diagnosis in electromechanical systems. An analysis of the interpretability of the learning processes is carried out, and the approaches are evaluated under common performance metrics.Peer ReviewedPostprint (published version