FRACTAL ANALYSIS OF THE ELECTROCARDIOGRAM SIGNAL

2014 ◽  
Vol 14 (04) ◽  
pp. 1450055 ◽  
Author(s):  
IBTICEME SEDJELMACI ◽  
F. BEREKSI-REGUIG
Keyword(s):  
Premature Ventricular Contraction ◽  
Multifractal Spectrum ◽  
Bundle Branch Block ◽  
Ecg Signals ◽  
Linear Approach ◽  
Atrial Premature Beat ◽  
The Subject ◽  
Electrocardiogram Signal ◽  
Electrocardiogram Ecg

In this paper, the analysis of the electrocardiogram (ECG) signal is carried out according a non-linear approach. This concerns the eventual fractal behavior of such signal and the correlation of such behavior with normal and pathological ECG signals. The analysis is carried out on different ECG signals taken from the MIT-BIH arrhythmia database. In fact these signals are those of six subjects with different ages and presenting both normal and abnormal arrhythmias situations. The abnormal situations are atrial premature beat (APB), premature ventricular contraction (PVC), right bundle branch block (RBBB) and left bundle branch block (LBBB). The fractal behavior of these signals is analyzed according to the determination of the multifractal spectrum and the fractal dimension variations and looking for eventually a fractal signature of each heart disease and age of the subject. The obtained results show a fractal signature according to the age and the pathologies for the studied cases. However further investigations are required on larger databases to confirm such results.

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

2020 ◽  
Vol 12 (10) ◽  
pp. 1685 ◽  
Author(s):  
Amin Ullah ◽  
Syed Muhammad Anwar ◽  
Muhammad Bilal ◽  
Raja Majid Mehmood
Keyword(s):  
Premature Ventricular Contraction ◽  
Careful Study ◽  
Spectral Image ◽  
Bundle Branch Block ◽  
Ecg Signals ◽  
The One ◽  
Short Time ◽  
Electrocardiogram Ecg ◽  
Better Than

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.

scholarly journals Automatic ECG Diagnosis Using Convolutional Neural Network

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 951 ◽  
Author(s):  
Roberta Avanzato ◽  
Francesco Beritelli
Keyword(s):  
Confusion Matrix ◽  
Premature Ventricular Contraction ◽  
Disease Diagnosis ◽  
Ecg Signals ◽  
Testing Dataset ◽  
Life Threatening ◽  
Causes Of Mortality ◽  
Atrial Premature Beat ◽  
Heart Disease Diagnosis ◽  
Electrocardiogram Ecg

Cardiovascular disease (CVD) is the most common class of chronic and life-threatening diseases and, therefore, considered to be one of the main causes of mortality. The proposed new neural architecture based on the recent popularity of convolutional neural networks (CNN) was a solution for the development of automatic heart disease diagnosis systems using electrocardiogram (ECG) signals. More specifically, ECG signals were passed directly to a properly trained CNN network. The database consisted of more than 4000 ECG signal instances extracted from outpatient ECG examinations obtained from 47 subjects: 25 males and 22 females. The confusion matrix derived from the testing dataset indicated 99% accuracy for the “normal” class. For the “atrial premature beat” class, ECG segments were correctly classified 100% of the time. Finally, for the “premature ventricular contraction” class, ECG segments were correctly classified 96% of the time. In total, there was an average classification accuracy of 98.33%. The sensitivity (SNS) and the specificity (SPC) were, respectively, 98.33% and 98.35%. The new approach based on deep learning and, in particular, on a CNN network guaranteed excellent performance in automatic recognition and, therefore, prevention of cardiovascular diseases.

scholarly journals Deep Learning based Arrhythmia Classification with an ECG Acquisition System

2019 ◽  
Vol 9 (2) ◽  
pp. 3849-3852
Keyword(s):  
Deep Learning ◽  
Premature Ventricular Contraction ◽  
Detection Algorithm ◽  
Raspberry Pi ◽  
Data Set ◽  
Bundle Branch Block ◽  
Ecg Signals ◽  
Peak Detection Algorithm ◽  
Different Types ◽  
Testing Accuracy

One of the issues that the human body faces is arrhythmia, a condition where the human heartbeat is either irregular, too slow or too fast. One of the ways to diagnose arrhythmia is by using ECG signals, the best diagnostic tool for detection of arrhythmia. This paper describes a deep learning approach to check whether signs of arrhythmia, in a given input signal, are present or not. A batch normalized CNN is used to classify the ECG signals based on the different types of arrhythmia. The model has achieved 96.39% training accuracy and 97% testing accuracy. The ECG signals are classified into five classes namely: Normal beats, Premature Ventricular Contraction (PVC) beats, Right Bundle Branch Block (RBBB) beats, Left Bundle Branch Block (LBBB) beats and Paced beats. A peak detection algorithm with six simple steps is designed to detect R-peaks from the ECG signals. A hardware device is built using Raspberry Pi to acquire ECG signals, which are then sent to the trained CNN for classification. The data-set for training is obtained from the MIT-BIH repository. Keras and Tensorflow libraries are used to design and develop the CNN and an application is designed using ’MEAN’ stack and ’Flask’ based servers.

scholarly journals Arrhythmia Classification Using One Dimensional Conventional Neural Network

2021 ◽  
Vol 13 (3) ◽  
pp. 43-58
Author(s):  
Sarah kamil ◽  
Lamia Muhammed
Keyword(s):  
Neural Network ◽  
Cardiovascular Disease ◽  
Deep Learning ◽  
Disease Classification ◽  
Visual Interpretation ◽  
Deep Convolutional Neural Networks ◽  
Bundle Branch Block ◽  
Ecg Signals ◽  
Short Period ◽  
Electrocardiogram Ecg

Arrhythmia is a heart condition that occurs due to abnormalities in the heartbeat, which means that the heart's electrical signals do not work properly, resulting in an irregular heartbeat or rhythm and thus defeating the pumping of blood. Some cases of arrhythmia are not considered serious, while others are very dangerous, life-threatening, and cause death in a short period of time. In the clinical routine, cardiac arrhythmia detection is performed by electrocardiogram (ECG) signals. The ECG is a significant diagnosis tool that is used to record the electrical activity of the heart, and its signals can reveal abnormal heart activity. However, because of their small amplitude and duration, visual interpretation of ECG signals is difficult. As a result, we present a significant approach for identifying arrhythmias using ECG signals. In this study, we proposed an approach based on Deep Learning (DL) technology that is a framework of nine-layer one-dimension Conventional Neural Network (1D CNN) for classifying automatically ECG signals into four cardiac conditions named: normal (N), Atrial Premature Beat (APB), Left Bundle Branch Block (LBBB), and Right Bundle Branch Block (RBBB). The practical test of this work was executed with the benchmark MIT-BIH database. We achieved an average accuracy of 99%, precision of 98%, recall of 96.5%, specificity of 99.08%, and an F1-score of 95.75%. The obtained results were compared with some relevant models, and they showed that the proposed framework outperformed those models in some measures. The new approach’s performance indicates its success. Also, it has been shown that deep convolutional neural networks can be used efficiently in automated detection and, therefore, cardiovascular disease protection as well as help cardiologists in medical practice by saving time and effort. Keywords: 1-D CNN, Arrhythmia, Cardiovascular Disease, Classification, Deep learning, Electrocardiogram(ECG), MIT-BIH arrhythmia database.

scholarly journals Electrocardiogram Signal Denoising by Hilbert Transform and Synchronous Detection

2020 ◽  
Vol 24 (4) ◽  
pp. 323-336
Author(s):  
Mohammed Assam Ouali ◽  
◽  
Asma Tinouna ◽  
Mouna Ghanai ◽  
Kheireddine Chafaa
Keyword(s):  
Hilbert Transform ◽  
High Energy ◽  
Signal Denoising ◽  
Ecg Signal ◽  
Synchronous Detection ◽  
Ecg Signals ◽  
Dominant Component ◽  
Electrocardiogram Signal ◽  
Electrocardiogram Ecg ◽  
Ecg Database

An efficient method for Electrocardiogram (ECG) signal denoising based on synchronous detection and Hilbert transform techniques is presented. The goal of the method is to decompose a noisy ECG signal into two components classified according to their energy: (1) component with high energy representing the dominant component which is the clean ECG signal, and (2) component with low energy representing the sub-dominant component which is the contaminant noise. The investigated approach is validated through out some experimentations on MIT-BIH ECG database. Experimental results show that random noises can be effectively suppressed from ECG signals.

scholarly journals Cardiac Health Prediction using Electrocardiography

2019 ◽  
Vol 8 (2) ◽  
pp. 5703-5711
Keyword(s):  
Premature Ventricular Contraction ◽  
Diagnostic Information ◽  
Physiological Signals ◽  
Bundle Branch Block ◽  
Cardiac Health ◽  
The Past ◽  
The Future ◽  
Hrv Analysis ◽  
Network Pattern ◽  
Electrocardiogram Ecg

Amongst various physiological signals, that can be collected from the human body, Electrocardiogram (ECG) is one widely used signal that gives an overview of individual’s health non-invasively. Some prognostic tools, based on ECG, have already been introduced in the past. However, the diagnostic information contained in ECG is still under used. In the present study, we propose an algorithm that predicts the cardiac health (both present and future) by analyzing subject’s ECG. The prediction is based on diagnostic information like Blood Pressure (BP), Arrhythmia and Heart Rate Variability (HRV), where BP and Arrhythmia are used to predict the present cardiac health, and Arrhythmia and HRV are used to predict the future cardiac health associated with an individual. To verify the algorithm, we use: (1) Linear Regression Model to extract BP based on parameters extracted from ECG; (2) Neural Network Pattern Recognition Application to detect Arrhythmia- Right and Left bundle branch block beat, Atrial premature contraction beat, Premature ventricular contraction beat and Premature or ectopic supraventricular beats, in any ECG signal; (3) SelfOrganized Maps for HRV analysis using ECG. These models are used on ECG of 30 subjects chosen from an existing database. Based on the outputs of these models our algorithm predicts the present as well as the future cardiac health of 30 subjects under study. Our predictions are compared with the present and future cardiac health of these subjects already documented in the database. The prediction accuracy showed that present and future cardiac health risk of an individual can be satisfactorily determined using the proposed algorithm, which, in future, can be easily incorporated in any health monitoring device which can record ECG.

scholarly journals A Hybrid Deep CNN Model for Abnormal Arrhythmia Detection Based on Cardiac ECG Signal

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 951
Author(s):  
Amin Ullah ◽  
Sadaqat ur Rehman ◽  
Shanshan Tu ◽  
Raja Majid Mehmood ◽  
Fawad ◽  
...  
Keyword(s):  
Classification Accuracy ◽  
Vital Role ◽  
Massachusetts Institute Of Technology ◽  
One Dimensional ◽  
Ecg Signals ◽  
Electrocardiogram Signal ◽  
Deep Cnn ◽  
Institute Of Technology ◽  
Fully Connected ◽  
Electrocardiogram Ecg

Electrocardiogram (ECG) signals play a vital role in diagnosing and monitoring patients suffering from various cardiovascular diseases (CVDs). This research aims to develop a robust algorithm that can accurately classify the electrocardiogram signal even in the presence of environmental noise. A one-dimensional convolutional neural network (CNN) with two convolutional layers, two down-sampling layers, and a fully connected layer is proposed in this work. The same 1D data was transformed into two-dimensional (2D) images to improve the model’s classification accuracy. Then, we applied the 2D CNN model consisting of input and output layers, three 2D-convolutional layers, three down-sampling layers, and a fully connected layer. The classification accuracy of 97.38% and 99.02% is achieved with the proposed 1D and 2D model when tested on the publicly available Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database. Both proposed 1D and 2D CNN models outperformed the corresponding state-of-the-art classification algorithms for the same data, which validates the proposed models’ effectiveness.

Arrhythmia ECG Beats Classification Using Wavelet-Based Features and Support Vector Machine Classifier

2019 ◽  
pp. 74-88 ◽  
Author(s):  
Chandan Kumar Jha ◽  
Maheshkumar H. Kolekar
Keyword(s):  
Support Vector Machine ◽  
Premature Ventricular Contraction ◽  
Abnormal Behavior ◽  
Support Vector ◽  
Bundle Branch Block ◽  
Learning Classifier ◽  
Beat Classification ◽  
Multi Resolution Analysis ◽  
Resolution Analysis ◽  
Electrocardiogram Ecg

Abnormal behavior of heart muscles generates irregular heartbeats which are collectively known as arrhythmia. Classification of arrhythmia beats plays a prominent role in electrocardiogram (ECG) analysis. It is widely used in online and long-term patient monitoring systems. This chapter reports a classification technique to recognize normal (N) and five arrhythmia beats (i.e., left bundle branch block [LBBB], right bundle branch block [RBBB], premature ventricular contraction [V], paced [P], and atrial premature contraction [A]). The technique utilizes features of heartbeats extracted by the wavelet multi-resolution analysis. The feature vectors are used to train and test the classifier based on the support vector machine which has been emerged as a benchmark in machine learning classifier. It accomplishes the beat classification very efficiently. ECG records of the MIT-BIH arrhythmia database are utilized to acquire the different types of heartbeats. Performance of the proposed classifier outperforms the contemporary arrhythmia beats classification techniques.

From fabric to smart T-shirt: fine tuning an improved robust system to detect arrhythmia

2021 ◽  
pp. 004051752110608
Author(s):  
Abdel Salam Malek ◽  
Ashraf Elnahrawy ◽  
Hamed Anwar ◽  
Mohamed Naeem
Keyword(s):  
Research Work ◽  
Outer Part ◽  
Premature Ventricular Contraction ◽  
Fine Tuning ◽  
Quality Data ◽  
High Quality Data ◽  
Ecg Signals ◽  
Weft Knitted Fabric ◽  
Smart Garments ◽  
Electrocardiogram Ecg

Wearable electrocardiogram (ECG) systems should be comfortable, non-stigmatizing, and capable of producing high-quality data. Many different designs of wearable textile ECG systems have recently emerged. Some of them are not considered to be smart garments, whereas most of the others present only the electronic side of the system. Our research work introduces a comprehensive study for an improved single-lead ECG smart shirt to identify automatically premature ventricular contraction as a common form of arrhythmia. For artifact-free results, Marvelous Designer is implemented to design our optimized relaxed slim fit shirt. In addition, a weft-knitted fabric of 80% nylon–20% spandex is used to manufacture the outer part of the shirt. Moreover, lightweight and small size electronic components are integrated to the outer part via low-resistance dry textile electrodes and 100% cotton fabric as an inner layer for easy transmission of weak ECG signals.

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