scholarly journals A Novel Technique in Classifying Heart Diseases based on Electrocardiogram (ECG) Signals using Deep Learning and Spectrogram Image Analysis

2019 ◽  
Vol 6 (4) ◽  
pp. 1734-1740
Author(s):  
Edward B. Panganiban, ◽  
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Heart Diseases ◽  
Ecg Signals ◽  
Novel Technique ◽  
Electrocardiogram Ecg

scholarly journals Hyperglycemia Identification Using ECG in Deep Learning Era

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6263
Author(s):  
Renato Cordeiro ◽  
Nima Karimian ◽  
Younghee Park
Keyword(s):  
Deep Learning ◽  
Blood Glucose Concentration ◽  
Area Under The Curve ◽  
High Sensitivity ◽  
Physiological Signals ◽  
Ecg Signals ◽  
Non Invasive ◽  
Learning Classifier ◽  
Electrocardiogram Ecg ◽  
Smart Wearable

A growing number of smart wearable biosensors are operating in the medical IoT environment and those that capture physiological signals have received special attention. Electrocardiogram (ECG) is one of the physiological signals used in the cardiovascular and medical fields that has encouraged researchers to discover new non-invasive methods to diagnose hyperglycemia as a personal variable. Over the years, researchers have proposed different techniques to detect hyperglycemia using ECG. In this paper, we propose a novel deep learning architecture that can identify hyperglycemia using heartbeats from ECG signals. In addition, we introduce a new fiducial feature extraction technique that improves the performance of the deep learning classifier. We evaluate the proposed method with ECG data from 1119 different subjects to assess the efficiency of hyperglycemia detection of the proposed work. The result indicates that the proposed algorithm is effective in detecting hyperglycemia with a 94.53% area under the curve (AUC), 87.57% sensitivity, and 85.04% specificity. That performance represents an relative improvement of 53% versus the best model found in the literature. The high sensitivity and specificity achieved by the 10-layer deep neural network proposed in this work provide an excellent indication that ECG possesses intrinsic information that can indicate the level of blood glucose concentration.

scholarly journals FPGA Implementation of Disease Detection System

2019 ◽  
Vol 8 (4) ◽  
pp. 6679-6684
Keyword(s):  
Field Programmable Gate Array ◽  
Diastolic Pressure ◽  
Detection System ◽  
Early Stage ◽  
Systolic Pressure ◽  
Ecg Signals ◽  
Novel Technique ◽  
Field Programmable ◽  
Body Parameters ◽  
Electrocardiogram Ecg

Nowadays Cardiovascular diseases are very trivial in every part of world. People are suffering from many such diseases; so a time to time check-up of their heart is very important and it is good if a person get to know about these diseases in advance. Heart checkup is again a very crucial task; hence to overcome this problem, a single system is needed, to know about few diseases depending upon electrocardiogram (ECG) of the patient under observation and few body parameters. Here to detect the disease in early stage, a new Field Programmable Gate Array (FPGA) based technique has been proposed using different ECG features along with few body parameters. This novel technique will diagnose diseases depending upon temperature, Systolic Pressure, Weight, Diastolic pressure, SPo2, and Sugar etc. MIT-BIH database was used to get ECG signals and for simulation and to display the results Xilinx version 8.2 was used.

scholarly journals Smartphone based ECG Acquisition and Analysis

2021 ◽  
Vol 9 (VI) ◽  
pp. 539-543
Author(s):  
Renuka Vijay Kapse
Keyword(s):  
Cardiovascular Diseases ◽  
Cause Of Death ◽  
Health Monitoring ◽  
Early Warning ◽  
Heart Diseases ◽  
Ecg Signal ◽  
Android Application ◽  
Ecg Signals ◽  
Heart Damage ◽  
Electrocardiogram Ecg

Health monitoring and technologies related to health monitoring is an appealing area of research. The electrocardiogram (ECG) has constantly being mainstream estimation plan to evaluate and analyse cardiovascular diseases. Heart health is important for everyone. Heart needs to be monitored regularly and early warning can prevent the permanent heart damage. Also heart diseases are the leading cause of death worldwide. Hence the work presents a design of a mini wearable ECG system and it’s interfacing with the Android application. This framework is created to show and analyze the ECG signal got from the ECG wearable system. The ECG signals will be shipped off an android application via Bluetooth device. This system will automatically alert the user through SMS.

scholarly journals Robustness of convolutional neural networks to physiological electrocardiogram noise

2021 ◽  
Vol 379 (2212) ◽  
Author(s):  
J. Venton ◽  
P. M. Harris ◽  
A. Sundar ◽  
N. A. S. Smith ◽  
P. J. Aston
Keyword(s):  
Deep Learning ◽  
Careful Consideration ◽  
Training Data ◽  
Learning Methods ◽  
Ecg Signals ◽  
Open Questions ◽  
Image Transforms ◽  
Challenges And Opportunities ◽  
Electrocardiogram Ecg ◽  
Image Transformations

The electrocardiogram (ECG) is a widespread diagnostic tool in healthcare and supports the diagnosis of cardiovascular disorders. Deep learning methods are a successful and popular technique to detect indications of disorders from an ECG signal. However, there are open questions around the robustness of these methods to various factors, including physiological ECG noise. In this study, we generate clean and noisy versions of an ECG dataset before applying symmetric projection attractor reconstruction (SPAR) and scalogram image transformations. A convolutional neural network is used to classify these image transforms. For the clean ECG dataset, F1 scores for SPAR attractor and scalogram transforms were 0.70 and 0.79, respectively. Scores decreased by less than 0.05 for the noisy ECG datasets. Notably, when the network trained on clean data was used to classify the noisy datasets, performance decreases of up to 0.18 in F1 scores were seen. However, when the network trained on the noisy data was used to classify the clean dataset, the decrease was less than 0.05. We conclude that physiological ECG noise impacts classification using deep learning methods and careful consideration should be given to the inclusion of noisy ECG signals in the training data when developing supervised networks for ECG classification. This article is part of the theme issue ‘Advanced computation in cardiovascular physiology: new challenges and opportunities’.

Detection of Heart Defects using Electrocardiogram (ECG)

2018 ◽  
Vol 4 (11) ◽  
Author(s):  
Kenil Shah ◽  
Mayur Rane ◽  
Dr. Vahid Emamian
Keyword(s):  
Neural Network ◽  
Signal Processing ◽  
Heart Diseases ◽  
Heart Defects ◽  
Healthy Person ◽  
Feature Extraction Method ◽  
Ecg Signals ◽  
Processing Techniques ◽  
Electrocardiogram Ecg ◽  
Ecg Data

Electrocardiogram (ECG) signals are vital to identifying cardiovascular disease. The numerous availability of signal processing and neural networks techniques for processing of ECG signals has inspired us to do research on extracting features of ECG signals to identify different cardiovascular diseases. We distinguish between a healthy person ECG data and person having disease ECG data using signal processing and neural network toolbox in Matlab. The data was downloaded from physiobank. To distinguish normal and abnormal ECG, Neural network is used. Feature extraction method is used to identify heart diseases. The diseases that are identified include Tachycardia, Bradycardia, first- degree Atrioventricular (AV) and a healthy person. Subsequently, ECG signals are very noisy; signal processing techniques are used to remove the noise impurity. The heart rate can be calculated by detecting the distance between R-R intervals of the signal. The algorithm successfully distinguished between normal and abnormal ECG data.

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 On the classification of arrhythmia using supplementary features from Tetrolet transforms

2019 ◽  
Vol 9 (6) ◽  
pp. 5006
Author(s):  
G. Jayagopi ◽  
S. Pushpa
Keyword(s):  
Heart Diseases ◽  
Classification Performance ◽  
Kernel Functions ◽  
Support Vector ◽  
Ecg Signals ◽  
The People ◽  
Vector Machines ◽  
Rbf Kernel ◽  
Electrocardiogram Ecg

<span>Heart diseases had been molded as potential threats to human lives, especially to elderly people in recent days due to the dynamically varying food habits among the people. However, these diseases could be easily caught by proper analysis of Electrocardiogram (ECG) signals acquired from individuals. This paper proposes a better method to detect and classify the arrhythmia using 15 features which include 4 R-R interval features, 3 statistical and 6 chaotic features estimated from ECG signals. Additionally, Entropy and Energy features had been gained after converting one dimensional ECG signals to two dimensional data and applied Tetrolet transforms on that.  Total numbers of 15 features had been utilized to classify the heart beats from the benchmark MIT-Arrhythmia database using Support Vector Machines (SVM). The classification performance was analyzed under various kernel functions and different Tetrolet decomposition levels. It is found that Radial Basis Function (RBF) kernel could perform better than linear and polynomial kernels. This research attempt yielded an accuracy of 99.35 % against the existing works. Moreover, addition of two more features had introduced a negligible overhead of time. Hence, this method is better suitable to detect and classify the Arrhythmia in both online and offline.</span>

scholarly journals Automatic Classification of Cardiac Arrhythmias based on ECG Signals Using Transferred Deep Learning Convolution Neural Network

2021 ◽  
Vol 2089 (1) ◽  
pp. 012058
Author(s):  
P. Giriprasad Gaddam ◽  
A Sanjeeva reddy ◽  
R.V. Sreehari
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Cardiac Arrhythmias ◽  
Learning Algorithm ◽  
Heart Diseases ◽  
Normal Sinus Rhythm ◽  
Automatic Classification ◽  
Ecg Signals ◽  
Major Interest

Abstract In the current article, an automatic classification of cardiac arrhythmias is presented using a transfer deep learning approach with the help of electrocardiography (ECG) signal analysis. Now a days, an ECG waveform serves as a powerful tool used for the analysis of cardiac arrhythmias (irregularities). The goal of the present work is to implement an algorithm based on deep learning for classification of different cardiac arrhythmias. Initially, the one dimensional (1-D) ECG signals are transformed to two dimensional (2-D) scalogram images with the help of Continuous Wavelet(CWT). Four different categories of ECG waveform were selected from four PhysioNet MIT-BIH databases, namely arrhythmia database, Normal Sinus Rhythm database, Malignant Ventricular Ectopy database and BIDMC Congestive heart failure database to examine the proposed technique. The major interest of the present study is to develop a transferred deep learning algorithm for automatic categorization of the mentioned four different heart diseases. Final results proved that the 2-D scalogram images trained with a deep convolutional neural network CNN with transfer learning technique (AlexNet) pepped up with a prominent accuracy of 95.67%. Hence, it is worthwhile to say the above stated algorithm demonstrates as an effective automated heart disease detection tool

Arrhythmia Classification Using Deep Learning: A Review

2021 ◽  
Vol 18 ◽  
pp. 96-105
Author(s):  
Deepali Koppad
Keyword(s):  
Deep Learning ◽  
Human Error ◽  
Traditional Approach ◽  
Learning Technology ◽  
Ecg Signals ◽  
The Past ◽  
Medical Disorders ◽  
Medical Reports ◽  
Diagnosis Method ◽  
Electrocardiogram Ecg

In most hospitals, the diagnosis of medical disorders involves the traditional approach of doctors manually analyzing the medical reports of the patient. This method is not only time consuming and strenuous, but is also highly prone to human error. With the advent of deep learning technology, an efficient autonomous diagnosis method holds the possibility of replacing the existing tedious approach. This in turn results in the reduction of human error which is of major concern in the medical industry today. Through this paper, we aim to put forth an articulate review of the different deep learning methodologies, observed in the past four years, to classify arrhythmia using electrocardiogram (ECG) signals.

scholarly journals From ECG signals to images: a transformation based approach for deep learning

2021 ◽  
Vol 7 ◽  
pp. e386
Author(s):  
Mahwish Naz ◽  
Jamal Hussain Shah ◽  
Muhammad Attique Khan ◽  
Muhammad Sharif ◽  
Mudassar Raza ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ventricular Arrhythmias ◽  
Ventricular Tachyarrhythmia ◽  
Heart Rhythm ◽  
Support Vector ◽  
Learning Approach ◽  
Ecg Signals ◽  
Different Types ◽  
Electrical Impulses ◽  
Electrocardiogram Ecg

Provocative heart disease is related to ventricular arrhythmias (VA). Ventricular tachyarrhythmia is an irregular and fast heart rhythm that emerges from inappropriate electrical impulses in the ventricles of the heart. Different types of arrhythmias are associated with different patterns, which can be identified. An electrocardiogram (ECG) is the major analytical tool used to interpret and record ECG signals. ECG signals are nonlinear and difficult to interpret and analyze. We propose a new deep learning approach for the detection of VA. Initially, the ECG signals are transformed into images that have not been done before. Later, these images are normalized and utilized to train the AlexNet, VGG-16 and Inception-v3 deep learning models. Transfer learning is performed to train a model and extract the deep features from different output layers. After that, the features are fused by a concatenation approach, and the best features are selected using a heuristic entropy calculation approach. Finally, supervised learning classifiers are utilized for final feature classification. The results are evaluated on the MIT-BIH dataset and achieved an accuracy of 97.6% (using Cubic Support Vector Machine as a final stage classifier).

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