scholarly journals Classification of Cardiac Arrhythmia using Hybrid Technology of Fast Discrete Stockwell-Transform (FDST) and Self Organising Map

2018 ◽  
Author(s):  
Raghuvendra Pratap Tripathi ◽  
G. R. Mishra ◽  
Dinesh Bhatia ◽  
Tapas Kumar Sinha
Keyword(s):  
Research Work ◽  
Vascular Diseases ◽  
Ecg Signal ◽  
Self Organizing Maps ◽  
Ecg Analysis ◽  
Hybrid Technology ◽  
Ecg Signals ◽  
Cardio Vascular ◽  
Stockwell Transform

The diagnosis of Cardio-Vascular diseases (CVD) is highly dependent on analysis of ECG signals. ECG analysis can be helpful in estimating the underlying cause and condition of heart in cardiac abnormality. The effectiveness of ECG signal analysis in detection of CVDs is widely accepted by professional healthcare service provider. Many algorithms have been proposed but almost all of them have some kind of limitations, and these limitations largely influence the effectiveness of ECG analysis. The performed research work is dedicated for design of unique self-organizing maps (SOMs) based neural network for classification of arrhythmia according to a particular ECG signal, the generation of SOMs is based on the certain unique signatures of ECG signals and have potential to classify different cardiac conditions. For extraction of unique features from ECG signals, we have proposed to use Fast Discrete Stockwell Transform (FDST). Since the proposed technique is a result of combining two different techniques hence called as hybrid technology. The purpose of using FDST is to identify unique signatures of ECG signals in a more improved manner than existing one, the term improved is used because it has several advantages over existing techniques such as wavelet and Fourier Transform based methods. Results obtained from the implementation of the technique are capable in visualizing the ECG sinus rhythm and arrhythmia conditions in form of unique SOM for each associated arrhythmia condition. This unique SOM based classification makes them ideal for being used as a diagnostic tool. This ability of arrhythmia classification using FDST and SOMs makes the technique unique and useful providing valuable information about patient condition. Using proposed technology a portable diagnosis tool for monitoring of patient at their site may be facilitated later, that will improve the quality of life of the patient by diagnosing cardiac condition.

scholarly journals Classification of Heart conditions by Statistical Characterization of ECG Signal

2020 ◽  
Vol 16 (2) ◽  
Author(s):  
Asma Haque ◽  
Abdur Rahman
Keyword(s):  
Ventricular Tachyarrhythmia ◽  
Research Work ◽  
Ecg Signal ◽  
Statistical Characterization ◽  
Ecg Signals ◽  
Detection Algorithms ◽  
Heart Patients ◽  
Proposed Model ◽  
Electrocardiogram Ecg

Electrocardiogram (ECG) signal exhibits important distinctive feature for different cardiac issues. Automatic classification of electrocardiogram (ECG) signal can be used for primary detection of various heart conditions. Information about heart and ischemic changes of heart may be obtained from cleaned ECG signals. ECG signal has an important role in monitoring and diacritic of the heart patients. An accurate ECG classification is challenging problem. The accuracy often depends on proper selection of observing parameters as well as detection algorithms. Heart disorder means abnormal rhythm or abnormalities present in the heart. In this research work, we have developed a decision tree based algorithm to classify heart problems by utilizing the statistical signal characteristic (SSC) of an ECG signal. The proposed model has been tested with real ECG signal to successfully (60-98%) detect normal, apnea and ventricular tachyarrhythmia condition.

What about Ethiology?

2009 ◽  
Vol 24 (S1) ◽  
pp. 1-1
Author(s):  
P. Varandas
Keyword(s):  
Vascular Diseases ◽  
Actual Knowledge ◽  
Clinical Methods ◽  
Cardio Vascular

Depression has became the main focus of psychiatry care and research, due to its epidemiologic and economic wheight in health. As a matter of fact depression is now the second condition after cardio-vascular diseases contributing to the costs in haelth in our modern societies attending to the Daly"s measures.From an old ethiologic prespective psychiatry shift to a pragmatic way to solve the main therotical issues in care and research. By this, we have now standard clinical methods to diagnose and classify depression, easy to use by any clinician (Psychiatrists and GPs), but we don"t know the essencial psychologic and biologic mechanisms of this disease as well as how the supposed ethiologic factors interact with these mechanisms.Psychopharmacologic treatments opened some doors to this mechanisms, but the knowledge is still unsatisfactory in order to differenciate the several types of depression and to drive specific treatments to patients.In this presentation included in the Symposium BACK TO THE BASICS: CHALLENGES IN CLASSIFICATION OF DEPRESSION it will be revised the actual knowledge concerning ethiology and phisiopathology of depression.

Filtration and Classification of ECG Signals

2018 ◽  
pp. 72-94 ◽  
Author(s):  
Satya Ranjan Dash ◽  
Asim Syed Sheeraz ◽  
Annapurna Samantaray
Keyword(s):  
Electrical Activity ◽  
Electrical Conduction ◽  
Diagnostic Tool ◽  
Noise Level ◽  
Medical Professionals ◽  
Ecg Signal ◽  
Ecg Signals ◽  
Electrocardiogram Ecg ◽  
Structure And Functions

Electrocardiogram (ECG) is a kind of process of recording the electrical activity/signals of the heart with respect to the time. ECG conveys a wide amount of information related to the structure and functions of the heart, its electrical conduction processes. ECG is a diagnostic tool that the doctors and medical professionals use to measure patients' heart activity by paying attention to the electric current flowing in the heart. Due to the presence of noises, it needs to carry out the filtration process. Filtration is the process of keeping the components of the signals of desired frequencies by setting up an “fc” value and removing the components apart from the said “fc” frequency. It is required to eliminate the noise level from the ECG signal, such that the resultant ECG signal must be free from noises. All these techniques and algorithms have their advantages and limitations which are discussed in this chapter.

Improved ECG Signal Analysis Using Wavelet and Feature Extraction

2007 ◽  
Vol 46 (02) ◽  
pp. 227-230 ◽  
Author(s):  
M. Jonkman ◽  
F. de Boer ◽  
A. Matsuyama
Keyword(s):  
Feature Extraction ◽  
Signal Analysis ◽  
Significant Variation ◽  
Wavelet Decomposition ◽  
Ecg Signal ◽  
Ecg Signals ◽  
Feature Vectors ◽  
Different Types ◽  
Before And After

Summary Objectives : Automatic detection of arrhythmias is important for diagnosis of heart problems. However, in ECG signals, there is significant variation of waveforms in both normal and abnormal beats. It is this phenomenon, which makes it difficult to analyse ECG signals. The aim of developing methodology is to distinguish between normal beats and abnormal beats in an ECG signal. Methods : ECG signals were first decomposed using wavelet transform. The feature vectors were then extracted from these decomposed signals as normalised energy and entropy. To improve the classification of the feature vectors of normal and abnormal beats, the normal beats which occur before and after the abnormal beats were eliminated from the group of normal beats. Results : With our proposed methods, the normal beats and abnormal beats formed different clusters of vector points. By eliminating normal beats which occur before and after the abnormal beats, the clusters of different types of beats showed more apparent separation. Conclusions : The combination of wavelet decomposition and the classification using feature vectors of the beats in ECG signals separate abnormal beats from normal beats. The elimination of the normal beats which occur before and after the abnormal beats succeeded in minimising the size of normal beats cluster.

scholarly journals Classification of Heart Arrhythmia in ECG Signals using PCA and SVM

2020 ◽  
Vol 9 (10) ◽  
pp. 193-198
Keyword(s):  
Research Work ◽  
Principal Component ◽  
Support Vector ◽  
Public Database ◽  
Ecg Signals ◽  
Heart Arrhythmia ◽  
Svm Algorithm ◽  
Different Types ◽  
Pca Algorithm

Electro cardiogram (ECG) signals records the vital information about the condition of heart of an individual. In this paper, we are aiming at preparing a model for classification of different types of heart arrhythmia. The MIT-BIH public database for heart arrhythmia has been used in the case of study. There are basically thirteen types of heart arrhythmia. The Principal Component Analysis (PCA) algorithm has been used to collect various important features of heart beats from an ECG signal. Then these features are trained and tested under Support Vector Machine (SVM) algorithm to classify the thirteen classes of heart arrhythmia. In the paper the proposed algorithm has been discussed and the outcome results have been validated. The result shows that the accuracy of our classifier in our research work is more than 91% in most of the cases.

scholarly journals Detection and Classification of ECG Signal through Machine learning

2019 ◽  
Vol 8 (10) ◽  
pp. 3221-3227
Keyword(s):  
The Body ◽  
Wave Form ◽  
Ecg Signal ◽  
Software Application ◽  
Ecg Signals ◽  
Temporal Features ◽  
Associate Anomaly ◽  
The Individual ◽  
Electrocardiogram Ecg

The electrical activity which might be acquired by inserting the probes on the body exterior that is originated within the individual muscle cells of the heart and is summed to indicate an indication wave form referred to as the EKG (ECG). Cardiac Arrhythmia is an associate anomaly within the heart which may be diagnosed with the usage of signals generated by Electrocardiogram (ECG). For the classification of ECG signals a software application model was developed and has been investigated with the usage of the MIT-BIH database. The version is based on some existing algorithms from literature, entails the extraction of a few temporal features of an ECG signal and simulating it with a trained FFNN. The software version may be employed for the detection of coronary heart illnesses in patients. The neural network’s structure and weights are optimized using Particle Swarm Optimization (PSO). The FFNN trained with set of rules by PSO increase its accuracy. The overall accuracy and sensitivity of the algorithm is about 93.687 % and 92%.

scholarly journals Fusion based Feature Extraction Analysis of ECG Signal Interpretation - A Systematic Approach

2021 ◽  
Vol 3 (1) ◽  
pp. 1-16
Author(s):  
Vijayakumar T ◽  
Vinothkanna R ◽  
Duraipandian M
Keyword(s):  
Feature Extraction ◽  
Research Work ◽  
Electrical Signal ◽  
The Body ◽  
Handheld Devices ◽  
Ecg Signal ◽  
Data Set ◽  
Raw Data ◽  
Feature Extraction Method

Our human heart is classified into four sections called the left side and right side of the atrium and ventricle accordingly. Monitoring and taking care of the heart of every human is the very essential part. Therefore, the early prediction is essential to save and give awareness to humans about diet plan, lifestyle schedule. Also, this is used to improve the clinical diagnosis and treatment of any patients. To predict or identifying any cardiovascular problems, Electro Cardio Gram (ECG) is used to record the electrical signal of the heart from the body surface of humans. The algorithm learns the dataset from before cluster is called supervised; The algorithm learns to train the data from the set of a dataset is called unsupervised. Then the classification of more amount of heartbeat for different category of normal, abnormal, irregular heartbeats to detect cardiovascular diseases. In this research article, a comparison of various methods to classify the dataset with a fusion-based feature extraction method. Besides, our research work consists of a de-noising filter to reconstruct the raw data from the original input. Our proposed framework performing preprocessing that consists of a filtering approach to remove noises from the raw data set. The signal is affected by thermal noise and instrumentation noise, calibration noise due to power line fluctuation. This interference is high in many handheld devices which can be eliminated by de-noising filters. The output of the de-noising filter is input for fusion-based feature extraction and prediction model construction. This workflow progress has given good results of classifier effectiveness and imbalance arrangement conditions. We achieved good accuracy 96.5% and minimum computation time for classification of ECG signal.

scholarly journals LABVIEW BASED ECG SIGNAL ACQUISITION AND ANALYSIS

2021 ◽  
Vol 5 (9) ◽  
Author(s):  
Shila Dhande
Keyword(s):  
Heart Rate ◽  
Research Work ◽  
Heart Rhythm ◽  
The Body ◽  
P Wave ◽  
Signal Acquisition ◽  
Ecg Signal ◽  
Qrs Complex ◽  
Ecg Signals ◽  
Rr Interval

The system “LabVIEW based ECG signal acquisition and analysis” is developed to assist patients and doctors in health care. An arrhythmia is an abnormal heart rhythm. It may be so brief that it doesn’t change the overall heart rate, but it can cause the heart rate to be too slow or too fast. When arrhythmias are severe or last long enough, the heart may not be able to pump enough blood to the body. This can cause the patient to feel tired, lightheaded or may make him pass out. It can also cause death. Before treatment, it’s important for the doctor to know where an arrhythmia starts in the heart and whether it’s abnormal. An electrocardiogram (ECG) is often used to diagnose arrhythmias. “LabVIEW based ECG signal acquisition and analysis” is meant to acquire ECG signals from the patient and analyze it to detect and classify its anomalies and abnormalities. This is achieved by extracting amplitudes and durations of parameters of ECG waveform such as P wave, QRS complex, RR interval, and PR durations. These parameters are compared with the normal values to determine the type of abnormality- Tachycardia or Bradycardia. The database of the patient is maintained for further use by the doctor. The objective of LabVIEW based ECG signal acquisition and analysis aims at acquiring and analyzing temporal parameters of ECG signal such as P wave, QRS complex, RR interval, PR durations and amplitudes of the P wave, ST wave, identification of cardiac arrhythmia using LabVIEW. The research work has helped us to explore various features of LabVIEW like signal processing and automated database generation.

scholarly journals Development of deep CNN architecture for anomalies in ECG signals

2020 ◽  
Vol 11 (3) ◽  
pp. 3490-3495
Author(s):  
Sharanya S ◽  
Sridhar PA ◽  
Anshika Singh ◽  
Ankit Dash ◽  
Ayushi Sharma
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Time Interval ◽  
Ecg Signal ◽  
Qrs Complex ◽  
Poincaré Plot ◽  
Ecg Signals ◽  
Test Input ◽  
Deep Cnn

In this paper, we propose a deep learning convolutional neural network (CNN) approach to classify arrhythmia based on the time interval of the QRS complex of the ECG signal. The ECG signal was denoised using multiple filters based on the Pan Tompkins algorithm. QRS detection has been done using Pan Tompkins Algorithm. Then the QRS complex is identified using local peaks based technique inside the layers of the Convolutional Neural Network where the repeated application of the same filter to our input results in a map of activations called a feature map, indicating the locations and strength of a detected feature in an input which in our case is the changes in the q-s time interval. Based on the R-R time interval, Heart rate variability (HRV) was computed, and Poincare plot was generated. Instead of using raw ECG signal to train the CNN, we used the feature extracted from ECG signal obtained from Physionet database to train the CNN and map the pattern changes for different classes of diseases. The classifier was then used to classify the test input as either or normal, tachyarrhythmia or intracardiac atrial fibrillation. Data acquisition, ECG data pre-processing and CNN classifier are the several methods that are involved for the classification of several arrhythmias.

Role of Emotional Processes in Cardio-vascular Diseases

2012 ◽  
Author(s):  
Suman Balhara ◽  
Nov Rattan Sharma ◽  
Amrita Yadav
Keyword(s):  
Vascular Diseases ◽  
Emotional Processes ◽  
Cardio Vascular
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