Cardiac Arrhythmia Detection using Dual Tree Wavelet Transform and Convolutional Neural Network

2021 ◽  
Author(s):  
K Reddy Madhavi ◽  
Padmavathi kora ◽  
L Venkateswara Reddy ◽  
J Avanija ◽  
KLS Soujanya ◽  
...  
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Cardiac Cells ◽  
P Wave ◽  
Computer Assisted ◽  
Ecg Signals ◽  
Coronary Diseases ◽  
Life Saving ◽  
Life Threatening ◽  
Assisted Diagnosis

Abstract The non-stationary ECG signals are used as a key tools in screening coronary diseases. ECG recording is collected from millions of cardiac cells’ and depolarization and re-polarization conducted in a synchronized manner as: The P-wave occurs first, followed by the QRScomplex and the T-wave, which will repeat in each beat. The signal is altered in a cardiac beat period for different heart conditions. This change can be observed in order to diagnose the patient’s heart status. There are life-threatening (critical) and non-life - threatening (noncritical) arrhythmia (abnormal Heart). Critical arrhythmia gives little time for surgery, whereas non-critical needs additional life-saving care. Simple naked eye diagnosis can mislead the detection. At that point, Computer Assisted Diagnosis (CAD) is therefore required. In this paper Dual Tree Wavelet Transform (DTWT) used as a feature extraction technique along with Convolution Neural Network (CNN) to detect abnormal Heart. The findings of this research and associated studies are without any cumbersome artificial environments. The CAD method proposed has high generalizability; it can help doctors efficiently identify diseases and decrease misdiagnosis.

scholarly journals Automatic ECG Classification Using Continuous Wavelet Transform and Convolutional Neural Network

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 119
Author(s):  
Tao Wang ◽  
Changhua Lu ◽  
Yining Sun ◽  
Mei Yang ◽  
Chun Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Convolutional Neural Network ◽  
Continuous Wavelet Transform ◽  
Continuous Wavelet ◽  
Time Frequency ◽  
Ecg Signals ◽  
Rr Interval ◽  
Frequency Components ◽  
Fully Connected

Early detection of arrhythmia and effective treatment can prevent deaths caused by cardiovascular disease (CVD). In clinical practice, the diagnosis is made by checking the electrocardiogram (ECG) beat-by-beat, but this is usually time-consuming and laborious. In the paper, we propose an automatic ECG classification method based on Continuous Wavelet Transform (CWT) and Convolutional Neural Network (CNN). CWT is used to decompose ECG signals to obtain different time-frequency components, and CNN is used to extract features from the 2D-scalogram composed of the above time-frequency components. Considering the surrounding R peak interval (also called RR interval) is also useful for the diagnosis of arrhythmia, four RR interval features are extracted and combined with the CNN features to input into a fully connected layer for ECG classification. By testing in the MIT-BIH arrhythmia database, our method achieves an overall performance of 70.75%, 67.47%, 68.76%, and 98.74% for positive predictive value, sensitivity, F1-score, and accuracy, respectively. Compared with existing methods, the overall F1-score of our method is increased by 4.75~16.85%. Because our method is simple and highly accurate, it can potentially be used as a clinical auxiliary diagnostic tool.

scholarly journals Convolution Neural Network Based Ecg Classifier

2019 ◽  
Vol 10 (3) ◽  
pp. 1626-1630
Author(s):  
Sharanya S ◽  
Sridhar PA ◽  
Poornakala J ◽  
Muppala Vasishta ◽  
Tharani U
Keyword(s):  
Neural Network ◽  
Noise Removal ◽  
Convolution Neural Network ◽  
P Wave ◽  
Time Interval ◽  
Ecg Signal ◽  
Related Condition ◽  
Simple Method ◽  
Medical Practitioners ◽  
Ecg Signals

Classification of Electrocardiogram (ECG) signals plays a significant role in the identification of the functioning of the heart. This work pertains with the ECG signals, where the classifier is developed for identification of normal or abnormal conditions of the heart. The raw ECG signals are collected from an online database (www.physioNet.org) for classification. The raw ECG signal is pre-processed for noise removal, and the frequency spectrum is analysed to compare raw and denoised ECG signal. Attributes (P, Q, R, S, T time intervals) from denoised ECG signal is analysed and classified using Convolution Neural Network (CNN). The paper reports a classification technique to differentiate ECG signals from the MIT-BIH database (arrhythmia database, arrhythmia p-wave annotations, atrial fibrillation). The CNN analyses the deviation between nominal ranges of attributes (amplitude and time interval) and classifies between the abnormality and normal ECG wave. This work provides a simple method for interpreting ECG related condition for the clinician and helps medical practitioners to make diagnostic decisions.

scholarly journals Methodology for detection of paroxysmal atrial fibrillation based on P-Wave, HRV and QR electrical alternans features

2020 ◽  
Vol 10 (4) ◽  
pp. 4023
Author(s):  
Henry Castro ◽  
Juan David Garcia-Racines ◽  
Alvaro Bernal-Noreña
Keyword(s):  
Neural Network ◽  
Atrial Fibrillation ◽  
Paroxysmal Atrial Fibrillation ◽  
P Wave ◽  
Computational Techniques ◽  
K Nearest Neighbors ◽  
Ecg Signals ◽  
Complex Process ◽  
Artificial Neural Network Ann ◽  
Electrical Alternans

The detection of Paroxysmal Atrial Fibrillation (PAF) is a fairly complex process performed manually by cardiologists or electrophysiologists by reading an electrocardiogram (ECG). Currently, computational techniques for automatic detection based on fast Fourier transform (FFT), Bayes optimal classifier (BOC), k-nearest neighbors (K-NNs), and artificial neural network (ANN) have been proposed. In this study, six features were obtained based on the morphology of the P-Wave, the QRS complex and the heart rate variability (HRV) of the ECG. The performance of this methodology was validated using clinical ECG signals from the Physionet arrhythmia database MIT-BIH. A feedforward neural network was used to detect the presence of PAF reaching a general accuracy of 97.4%. The results obtained show that the inclusion of the information of the P-Wave, HRV and QR Electrical alternans increases the accuracy to identify the PAF event compared to other works that use the information of only one or at most two of them.

APPLICATION OF EMPIRICAL MODE DECOMPOSITION–BASED FEATURES FOR ANALYSIS OF NORMAL AND CAD HEART RATE SIGNALS

2016 ◽  
Vol 16 (01) ◽  
pp. 1640002 ◽  
Author(s):  
SURABHI SOOD ◽  
MOHIT KUMAR ◽  
RAM BILAS PACHORI ◽  
U. RAJENDRA ACHARYA
Keyword(s):  
Heart Rate ◽  
Empirical Mode Decomposition ◽  
P Value ◽  
Computer Assisted ◽  
Intrinsic Mode Functions ◽  
Mean Frequency ◽  
Ecg Signals ◽  
Mode Decomposition ◽  
Artery Disease ◽  
Assisted Diagnosis

Coronary Artery Disease (CAD) is a heart disease caused due to insufficient supply of nutrients and oxygen to the heart muscles. Hence, reduced supply of nutrients and oxygen causes heart attack or stroke and may cause death. Also significant number of people are suffering from CAD around the world so timely diagnosis of CAD can save the life of patients. In this work, we have proposed computer assisted diagnosis of CAD using Heart Rate (HR) signals obtained from Electrocardiogram (ECG) signals. We have used the Empirical Mode Decomposition (EMD) technique to process the HR signals. The features namely: Second-Order Difference Plot (SODP) area, Analytic Signal Representation (ASR) area, Amplitude Modulation (AM) bandwidth, Frequency Modulation (FM) bandwidth and Fourier–Bessel expansion (FBE)- based mean frequency computed from the Intrinsic Mode Functions (IMFs) are extracted to discriminate normal and CAD subjects. Thereafter, Kruskal–Wallis statistical test is performed on these features. The features having p-value less than 0.05 are considered to be significant. Our results show that three features namely: AM bandwidth, FM bandwidth and FBE-based mean frequency are more suitable than ASR area and SODP area features for discrimination of normal and CAD subjects.

scholarly journals WAVELET-BASED MULTIFRACTAL FORMALISM TO ASSIST IN DIAGNOSIS IN DIGITIZED MAMMOGRAMS

2011 ◽  
Vol 20 (3) ◽  
pp. 169 ◽  
Author(s):  
Pierre Kestener ◽  
Jean Marc Lina ◽  
Philippe Saint-Jean ◽  
Alain Arneodo
Keyword(s):  
Wavelet Transform ◽  
Hurst Exponent ◽  
Multifractal Analysis ◽  
Computer Assisted ◽  
Scaling Properties ◽  
Multifractal Formalism ◽  
Long Range Correlations ◽  
Multifractal Scaling ◽  
Self Similar ◽  
Assisted Diagnosis

We apply the 2D wavelet transform (WTMM) method to perform a multifractal analysis of digitized mammograms. We show that normal regions display monofractal scaling properties as characterized by the socalled Hurst exponent H =0.3±0.1 in fatty areas which look like antipersistent self-similar random surfaces, while H=0.65±0.1 in dense areas which exibit long-range correlations and possibly multifractal scaling properties. We further demonstrate that the 2D WTMM method provides a very efficient way to detect tumors as well as microcalcifications (MC) which correspond to much stronger singularities than those involved in the background tissue roughness fluctuations. These preliminary results indicate that the texture discriminatory power of the 2D WTMM method may lead to significant improvement in computer-assisted diagnosis in digitized mammograms.

An Analysis in Tissue Classification for Colorectal Cancer Histology Using Convolution Neural Network and Colour Models

2018 ◽  
Vol 9 (4) ◽  
pp. 1-19 ◽  
Author(s):  
Shamik Tiwari
Keyword(s):  
Neural Network ◽  
Colorectal Cancer ◽  
Extraction Methods ◽  
Learning Technologies ◽  
Computer Assisted ◽  
Learning Approaches ◽  
Computer Assisted Diagnosis ◽  
Histological Images ◽  
Assisted Diagnosis ◽  
The Cost

Computer vision-based identification of different tissue categories in histological images is a critical application of the computer-assisted diagnosis (CAD). Computer-assisted diagnosis systems support to reduce the cost and increase the efficiency of this process. Traditional image classification approaches depend on feature extraction methods designed for a specific problem based on domain information. Deep learning approaches are becoming important alternatives with advance of machine learning technologies to overcome the numerous difficulties of the feature-based approaches. A method for the classification of histological images of human colorectal cancer containing seven different types of tissue using convolutional neural network (CNN) is proposed in this article. The method is evaluated using four different colour models in absence and presence of Gaussian noise. The highest classification accuracies are achieved with HVI colour model, which is 95.8% in nonexistence and 78.5% in existence of noise respectively.

scholarly journals P-Wave Detection Using a Fully Convolutional Neural Network in Electrocardiogram Images

2020 ◽  
Vol 10 (3) ◽  
pp. 976
Author(s):  
Rana N. Costandy ◽  
Safa M. Gasser ◽  
Mohamed S. El-Mahallawy ◽  
Mohamed W. Fakhr ◽  
Samir Y. Marzouk
Keyword(s):  
Neural Network ◽  
Atrial Fibrillation ◽  
Convolutional Neural Network ◽  
P Wave ◽  
Ecg Signal ◽  
P Waves ◽  
Ecg Signals ◽  
Wave Detection ◽  
Automated Algorithm ◽  
Electrocardiogram Ecg

Electrocardiogram (ECG) signal analysis is a critical task in diagnosing the presence of any cardiac disorder. There are limited studies on detecting P-waves in various atrial arrhythmias, such as atrial fibrillation (AFIB), atrial flutter, junctional rhythm, and other arrhythmias due to P-wave variability and absence in various cases. Thus, there is a growing need to develop an efficient automated algorithm that annotates a 2D printed version of P-waves in the well-known ECG signal databases for validation purposes. To our knowledge, no one has annotated P-waves in the MIT-BIH atrial fibrillation database. Therefore, it is a challenge to manually annotate P-waves in the MIT-BIH AF database and to develop an automated algorithm to detect the absence and presence of different shapes of P-waves. In this paper, we present the manual annotation of P-waves in the well-known MIT-BIH AF database with the aid of a cardiologist. In addition, we provide an automatic P-wave segmentation for the same database using a fully convolutional neural network model (U-Net). This algorithm works on 2D imagery of printed ECG signals, as this type of imagery is the most commonly used in developing countries. The proposed automatic P-wave detection method obtained an accuracy and sensitivity of 98.56% and 98.78%, respectively, over the first 5 min of the second lead of the MIT-BIH AF database (a total of 8280 beats). Moreover, the proposed method is validated using the well-known automatically and manually annotated QT database (a total of 11,201 and 3194 automatically and manually annotated beats, respectively). This results in accuracies of 98.98 and 98.9%, and sensitivities of 98.97 and 97.24% for the automatically and manually annotated QT databases, respectively. Thus, these results indicate that the proposed automatic method can be used for analyzing long-printed ECG signals on mobile battery-driven devices using only images of the ECG signals, without the need for a cardiologist.

scholarly journals Deep Neural Network Based Real Time Multi-Class Arrhythmia Classification in IoT-Cloud Platform

2021 ◽  
Author(s):  
AKHILA NAZ K A ◽  
R S Jeena ◽  
P Niyas
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Deep Neural Network ◽  
Time Signal ◽  
Ecg Signals ◽  
Proposed Model ◽  
Heart Beating ◽  
Life Threatening ◽  
Manual Processing

Abstract An arrhythmia is a condition which represents irregular beating of the heart, beating of the heart too fast, too slow, or too early compared to a normal heartbeat. Diagnosis of various cardiac conditions can be done by the proper analysis, detection, and classification of life-threatening arrhythmia. Computer aided automatic detection can provide accurate and fast results when compared with manual processing. This paper proposes a reliable and novel arrhythmia classification approach using deep learning. A Deep Neural Network (DNN) with three hidden layers has been developed for arrhythmia classification using MIT-BIH arrhythmia database. The network classifies the input ECG signals into six groups: normal heartbeat and five arrhythmia classes. The proposed model was found to be very promising with an accuracy of 99.45 percent. The real time signal classification and the application of internet of things (IOT) are the other highlights of the work.

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