Developing of robust and high accurate ECG beat classification by combining Gaussian mixtures and wavelets features

Qazan

2020

Preprint

Self Cite

Since December 2019, the appearance of an outbreak of a novel coronavirus disease namely COVID-19 and which is previously known as 2019-nCoV. COVID-19 is a type of coronavirus that leads to the general destruction of respiratory systems and a severe respiratory symptom which are associated with highly Intensive Care Unit (ICU) admissions and death. Like any disease, the early diagnosis of coronavirus leads to limit its wide-spreading and increases the recovery rates of patients. The gold standard of COVID-19 detection is the real-time reverse transcription-polymerase chain reaction (RT-PCR) which has been used by the clinician to discover the presence or absence of this type of virus. The clinicians report that this technique has a low positive rate in the early stage of this disease. Based on this, the clinicians were forced to use another way to help in the early diagnosis of COVID-2019. So, the clinician's attention moved towards the medical imaging modalities especially the computed Tomography (CT) and X-ray chest images. Both modalities show that there is a change in the lungs in the case of COVID-19 that is different from any other type of pneumonic disease. Therefore, this research targeted toward employing different Artificial Intelligence (AI) techniques to propose a system for early detection of COVID-19 using chest X-ray images. These images are classified using different AI algorithms and a combination of them, then their performance was evaluated to recognize the best of them. These algorithms include a convolutional neural network (CNN), Softmax, support vector machine (SVM), Random Forest, and K nearest neighbor (KNN). Here CNN is into two scenarios, the first one to classify the X-ray images using a softmax classifier, and the second one to extract automated features from the images and pass these features to other classifiers (SVM, RFF, and KNN). According to the results, the performance of all classifiers is good and most of them record accuracy, sensitivity, specificity, and precision of more than 98%.

Section: Performance Evaluationmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

Automated Systems for Detection of COVID-19 Using Chest X-ray Images and Lightweight Convolutional Neural Networks

Qazan

2020

Preprint

Self Cite

“…RF classifier is basically constructed using a combination of base learners, where each base learner is an independent binary tree adopting recursive partitioning. The main advantages of RF are: it achieves higher accuracy than other classifiers, very efficient tool on large-scale data, does not overfit, and can be easily applied in multi-class inputs [19].…”

Section: Random Forest Classifiermentioning

confidence: 99%

“…For the purpose of evaluating the performance of the used classifier in classifying the finger movement using the proposed methodology, the confusion matrix was generated. This matrix provides a comparison between the classifier outputs with the corresponding original label of [19,20]. Using the generated confusion matrix, accuracy, sensitivity, specificity, and specificity are computed.…”

Section: F Classifier Performance Evaluationmentioning

confidence: 99%

Enhancing Prediction of Prosthetic Fingers Movement Based on sEMG using Mixtures of Features and Random Forest

Al-Sharu*¹,

Alqudah²

2019

IJRTE

Self Cite

As the recent development of the prosthetic control systems, it is necessary to develop the sub-components such as myoelectric control system. This sub-system is used to acquire electromyogram (EMG) signals from a person's muscles and convert it to movements to control prosthetic hands or fingers. Recently, researchers started focusing on providing feature extraction methods for both time domain and frequency domain for predicting either hand or finger movements. This paper proposes a new mixed feature set for both time and frequency domain for the classification of surface EMG (sEMG) into ten classes for controlling prosthetic finger movements. The features are based on enhanced statistical features extracted directly from a non-overlapped window with a predefined length which was selected carefully from the sEMG directly. Ten classes of individual and combined finger movements are to be recognized by using two sEMG channels from two electrodes are fixed on the human forearm to employ effective knowledge discovery and pattern recognition algorithms to enhance the recognition and classification accuracy. and the other features are statistical features extracted from Instantaneous frequency of the signal to utilize a suitable classifier helps detecting and recognizing the pattern from sEMG signals of different classes of the fingers movements either combined or single movement to enhance the classifier performances and to find the whole class of all extracted window ,a majority voting technique was applied.The method used the random forest as a classifier to build the classifier model which achieved an accuracy of 93.75% and sensitivity of 93.73% and specificity of 99.31%

“…Ever since Einthoven [1,2] discovered the possibility of obtaining an electrocardiogram (ECG) from cardiac activity, cardiologists have been able to take advantage of this effective and efficient tool for diagnosing cardiac disorders [1,3,4]. Specifically, an ECG is a particular recording of the electrical activity of the heart muscle about the potentials of the body surface [5][6][7][8]. The use of an ECG is nowadays widespread because of its non-invasiveness and easiness of performing as a test.…”

Section: Introductionmentioning

confidence: 99%

A Modified Heart Dipole Model for the Generation of Pathological ECG Signals

2020

In this paper, we introduce a new dynamic model of simulation of electrocardiograms (ECGs) affected by pathologies starting from the well-known McSharry dynamic model for the ECGs without cardiac disorders. In particular, the McSharry model has been generalized (by a linear transformation and a rotation) for simulating ECGs affected by heart diseases verifying, from one hand, the existence and uniqueness of the solution and, on the other hand, if it admits instabilities. The results, obtained numerically by a procedure based on a Four Stage Lobatto IIIa formula, show the good performances of the proposed model in producing ECGs with or without heart diseases very similar to those achieved directly on the patients. Moreover, verified that the ECGs signals are affected by uncertainty and/or imprecision through the computation of the linear index and the fuzzy entropy index (whose values obtained are close to unity), these similarities among ECGs signals (with or without heart diseases) have been quantified by a well-established fuzzy approach based on fuzzy similarity computations highlighting that the proposed model to simulate ECGs affected by pathologies can be considered as a solid starting point for the development of synthetic pathological ECGs signals.