scholarly journals Impact of Data Transformation: An ECG Heartbeat Classification Approach

2020 ◽  
Vol 2 ◽  
Author(s):  
Yongbo Liang ◽  
Ahmed Hussain ◽  
Derek Abbott ◽  
Carlo Menon ◽  
Rabab Ward ◽  
...  
Keyword(s):  
Heart Diseases ◽  
Data Transformation ◽  
Disease Diagnosis ◽  
Classification Performance ◽  
Detection Accuracy ◽  
Ecg Signal ◽  
Heartbeat Classification ◽  
Physiological Signal ◽  
Heart Disease Diagnosis ◽  
Transformation Methods

Cardiovascular diseases continue to be a significant global health threat. The electrocardiogram (ECG) signal is a physiological signal that plays a major role in preventing severe and even fatal heart diseases. The purpose of this research is to explore a simple mathematical feature transformation that could be applied to ECG signal segments in order to improve the detection accuracy of heartbeats, which could facilitate automated heart disease diagnosis. Six different mathematical transformation methods were examined and analyzed using 10s-length ECG segments, which showed that a reciprocal transformation results in consistently better classification performance for normal vs. atrial fibrillation beats and normal vs. atrial premature beats, when compared to untransformed features. The second best data transformation in terms of heartbeat detection accuracy was the cubic transformation. Results showed that applying the logarithmic transformation, which is considered the go-to data transformation, was not optimal among the six data transformations. Using the optimal data transformation, the reciprocal, can lead to a 35.6% accuracy improvement. According to the overall comparison tested by different feature engineering methods, classifiers, and different dataset sizes, performance improvement also reached 4.7%. Therefore, adding a simple data transformation step, such as the reciprocal or cubic, to the extracted features can improve current automated heartbeat classification in a timely manner.

HEART DISEASES DETECTION FROM NOISY RECORDINGS OF SMARTPHONE DEVICES

2018 ◽  
Vol 18 (04) ◽  
pp. 1850039
Author(s):  
WEN-HSIEN HO ◽  
YENMING J. CHEN ◽  
YUZHEN ZHANG ◽  
YANYUN TAO ◽  
HSIN-WEN KUO
Keyword(s):  
Time Series ◽  
Cardiovascular Health ◽  
Heart Diseases ◽  
Vital Signs ◽  
Disease Diagnosis ◽  
Heart Murmurs ◽  
Additional Equipment ◽  
Novel Method ◽  
Testing Accuracy ◽  
Heart Disease Diagnosis

This paper aims to develop an algorithm to detect heart diseases through ordinary smartphones without additional equipment for cost accessibility. Among various vital signs emitted by organs, sounds can be easily observed and carry ample information. However, these sounds are small and noisy. Detecting anomalies involves great challenges in signal processing. This study presents a novel method that overcomes noises to estimate cardiovascular health. We use time-scale techniques in time series analysis to extract disease traits and suppress excessive ambient noises. Using datasets from PhysioNet, our model achieved a nearly 100% accuracy in heart disease diagnosis. Our approach also performs well under excessive noises for diseases producing heart murmurs. With heavy noise contaminated signals, training accuracy still closed to 100%, and the testing accuracy still remained around 84%.

scholarly journals A New Method for Refined Recognition for Heart Disease Diagnosis Based on Deep Learning

Information ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 556
Author(s):  
Weibo Song
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Disease Diagnosis ◽  
Cardiac Conduction ◽  
Ecg Signal ◽  
Medical Experience ◽  
Diagnosis Method ◽  
Heart Disease Diagnosis ◽  
Cardiac Diagnosis ◽  
Image Format

The proper evaluation of heart health requires professional medical experience. Therefore, in clinical diagnosis practice, the development direction is to reduce the high dependence of the diagnosis process on medical experience and to more effectively improve the diagnosis efficiency and accuracy. Deep learning has made remarkable achievements in intelligent image analysis technology involved in the medical process. From the aspect of cardiac diagnosis, image analysis can extract more profound and abundant information than sequential electrocardiogram (ECG) signals. Therefore, a new region recognition and diagnosis method model of a two-dimensional ECG (2D-ECG) signal based on an image format is proposed. This method can identify and diagnose each refined waveform in the cardiac conduction cycle reflected in the image format ECG signal, so as to realize the rapid and accurate positioning and visualization of the target recognition area and finally get the analysis results of specific diseases. The test results show that compared with the results obtained by a one-dimensional sequential ECG signal, the proposed model has higher average diagnostic accuracy (98.94%) and can assist doctors in disease diagnosis with better visualization effect.

scholarly journals Heart Diseases Diagnose via Mobile Application

2021 ◽  
Vol 11 (5) ◽  
pp. 2430
Author(s):  
Mesut Güven ◽  
Fırat Hardalaç ◽  
Kanat Özışık ◽  
Funda Tuna
Keyword(s):  
Artificial Intelligence ◽  
Mobile Application ◽  
Heart Diseases ◽  
Disease Diagnosis ◽  
Heart Sounds ◽  
Diagnostic Ability ◽  
Heart Abnormalities ◽  
Heart Disease Diagnosis ◽  
Clinical Experiments ◽  
Better Than

One of the oldest and most common methods of diagnosing heart abnormalities is auscultation. Even for experienced medical doctors, it is not an easy task to detect abnormal patterns in the heart sounds. Most digital stethoscopes are now capable of recording and transferring heart sounds. Moreover, it is proven that auscultation records can be classified as healthy or unhealthy via artificial intelligence techniques. In this work, an artificial intelligence-powered mobile application that works in a connectionless fashion is presented. According to the clinical experiments, the mobile application can detect heart abnormalities with approximately 92% accuracy, which is comparable to if not better than humans since only a small number of well-trained cardiologists can analyze auscultation records better than artificial intelligence. Using the diagnostic ability of artificial intelligence in a mobile application would change the classical way of auscultation for heart disease diagnosis.

scholarly journals Classification of Multi Heart Diseases With Android Based Monitoring System

2020 ◽  
pp. 14-22
Keyword(s):  
Feature Extraction ◽  
Heart Diseases ◽  
Confusion Matrix ◽  
Predictive Coding ◽  
Classification Performance ◽  
Support Vector ◽  
Ecg Signal ◽  
Linear Predictive Coding ◽  
Electrocardiogram Ecg

Electrocardiogram (ECG) examination via computer techniques that involve feature extraction, pre-processing and post-processing was implemented due to its significant advantages. Extracting ECG signal standard features that requires high processing operation level was the main focusing point for many studies. In this paper, up to 6 different ECG signal classes are accurately predicted in the absence of ECG feature extraction. The corner stone of the proposed technique in this paper is the Linear predictive coding (LPC) technique that regress and normalize the signal during the pre-processing phase. Prior to the feature extraction using Wavelet energy (WE), a direct Wavelet transform (DWT) is implemented that converted ECG signal to frequency domain. In addition, the dataset was divided into two parts , one for training and the other for testing purposes Which have been classified in this proposed algorithm using support vector machine (SVM). Moreover, using MIT AI2 Companion was developed by MIT Center for Mobile Learning, the classification result was shared to the patient mobile phone that can call the ambulance and send the location in case of serious emergency. Finally, the confusion matrix values are used to measure the proposed classification performance. For 6 different ECG classes, an accuracy ration of about 98.15% was recorded. This ratio became 100% for 3 ECG signal classes and decreases to 97.95% by increasing ECG signal to 7 classes.

scholarly journals Heart Disease Diagnosis by Neural Networks

2021 ◽  
pp. 202-208
Author(s):  
Khyati Varshney ◽  
Mrinal Paliwal
Keyword(s):  
Neural Networks ◽  
Heart Disease ◽  
Mortality Rate ◽  
Intelligent Systems ◽  
Heart Diseases ◽  
Disease Diagnosis ◽  
Daily Basis ◽  
The World ◽  
Multilayer Neural Networks ◽  
Heart Disease Diagnosis

In the present time the Mortality rate will be increased all around the world on their daily basis. So the cause for this might possibly be largely ascribe to the developing in the numbers of the patients with the cardiovascular patient’s diseases. To aggravate the cases, many physicians that have been known for the misdiagnosis of the patients announce heart related ailments. In this research paper, the intelligent systems have been designed in which they will help in the successful diagnosis of the forbearing to avoiding misdiagnosis. In the dataset of a UCI stat log of heart disease that will be using in this investigation. The dataset contains 14 attributes which are essential in the diagnosis of the heart diseases. A system is sculpted on the multilayer neural networks trained with convolutional & simulated convolutional neural networks. The identification of 89% was acquired from the testing of the networks.

scholarly journals Heart Diseases Diagnose via Artificial Intelligence-Powered Mobile Application

2021 ◽  
Author(s):  
Mesut Guven ◽  
Firat Hardalac ◽  
Kanat Ozisik ◽  
Funda Tuna
Keyword(s):  
Artificial Intelligence ◽  
Mobile Application ◽  
Heart Diseases ◽  
Disease Diagnosis ◽  
Heart Sounds ◽  
Diagnostic Ability ◽  
Heart Abnormalities ◽  
Heart Disease Diagnosis ◽  
Clinical Experiments ◽  
Better Than

One of the oldest and common methods of diagnosing heart abnormalities is auscultation. Even for experienced medical doctors, it is not an easy task to detect abnormal patterns in the heart sounds. Most of the digital stethoscopes are now capable of recording and transferring the heart sounds. Moreover, it is proven that auscultation records can be classified as healthy or unhealthy via artificial intelligence techniques. In this work, an artificial intelligence-powered mobile application that works in a connectionless fashion is presented. According to the clinical experiments, the mobile application can detect heart abnormalities with approximately 92% accuracy which is comparable if not better than humans since only a small number of well-trained cardiologists can analyze auscultation records better than artificial intelligence. Using the diagnostic ability of artificial intelligence in a mobile application would change the classical way of auscultation for heart disease diagnosis.

Comparative Study of ECG Classification Performance Using Decision Tree Algorithms

2012 ◽  
Vol 3 (4) ◽  
pp. 102-120 ◽  
Author(s):  
Faiza Charfi ◽  
Ali Kraiem
Keyword(s):  
Decision Tree ◽  
Heart Diseases ◽  
Classification Performance ◽  
Primary Diagnosis ◽  
Health Condition ◽  
Ecg Signal ◽  
Chi Square ◽  
Ecg Signals ◽  
Tree Algorithms ◽  
And Control

The electrocardiogram (ECG) signal has often been reported to play an important role in the primary diagnosis, prognosis, and survival analysis of heart diseases. Electrocardiography has brought several valuable impacts on the practice of medicine. This paper deals with the feature extraction and automatic analysis of different ECG signal waves using derivative based/ Pan-Tompkins based algorithms. The ECG signal contains an important amount of information that can be exploited in different way. It allows for the analysis of cardiac health condition. The discrimination of ECG signals using the Data Mining Decision Tree techniques is of crucial importance in the cardiac disease therapy and control of cardiac arrhythmias. Different ECG signals from MIT/BIH Arrhythmia data base are used for ECG features extraction and analysis. Two pathologies are considered: atrial fibrillation and right bundle branch block. Some decision tree classification algorithms currently in use, including C4.5, Improved C4.5, CHAID (Chi square Automatic Interaction Detector) and Improved CHAID are performed for performance analysis. Promising results have been achieved using the C4.5 classifier, with an overall accuracy of 96.87%.

Proposed Methodology for Knowledge Acquisition: A Study on Congenital Heart Disease Diagnosis

1990 ◽  
Vol 29 (01) ◽  
pp. 30-40 ◽  
Author(s):  
F. B. Leãot ◽  
F. A. Rocha
Keyword(s):  
Knowledge Acquisition ◽  
Congenital Heart ◽  
Heart Diseases ◽  
Disease Diagnosis ◽  
Congenital Heart Diseases ◽  
Entropy Analysis ◽  
Heuristic Rules ◽  
Human Power ◽  
Heart Disease Diagnosis ◽  
Multiple Experts

Abstract “Knowledge and human power are synonymous, since the ignorance of the cause frustrates the effect:…“ Francis Bacon1 This paper proposes a methodology for knowledge acquisition (KA) from multiple experts, in an attempt to elicit the heuristic rules followed by the physician in diagnosing twelve frequently occurring congenital heart diseases (CHD). Twenty-two pediatric cardiologists and twenty-three general cardiologists were interviewed with this technique; 274 interviews were conducted, 169 with the 22 experts, 105 with the 23 non-experts. A graph formalism was employed to represent their reasoning model, leading to the construction of a “mean reasoning model” for each diagnosis, separately for experts and non-experts. The results indicate that experts, compared to non-experts, tend to build knowledge representation models (KRM) that are smaller and less complex. Qualitative differences in information utilization between the two groups were also observed. Entropy analysis suggests a greater objectivity and cohesion of the experts’ model.

ECG Analysis: A Brief Review

2019 ◽  
Vol 12 ◽  
Author(s):  
Saumendra Kumar Mohapatra ◽  
Mihir Narayan Mohanty
Keyword(s):  
Technology Development ◽  
Accurate Result ◽  
Early Stage ◽  
Heart Diseases ◽  
Short Review ◽  
Disease Diagnosis ◽  
Accurate Analysis ◽  
Ecg Analysis ◽  
Proper Analysis ◽  
Living Being

Background: In recent years cardiac problems found proportional to technology development. Cardiac signal (Electrocardiogram) relates to the electrical activity of the heart of a living being and it is an important tool for diagnosis of heart diseases. Method: Accurate analysis of ECG signal can provide support for detection, classification, and diagnosis. Physicians can detect the disease and start the diagnosis at an early stage. Apart from cardiac disease diagnosis ECG can be used for emotion recognition, heart rate detection, and biometric identification. Objective: The objective of this paper is to provide a short review of earlier techniques used for ECG analysis. It can provide support to the researchers in a new direction. The review is based on preprocessing, feature extraction, classification, and different measuring parameters for accuracy proof. Also, different data sources for getting the cardiac signal is presented and various application area of the ECG analysis is presented. It explains the work from 2008 to 2018. Conclusion: Proper analysis of the cardiac signal is essential for better diagnosis. In automated ECG analysis, it is essential to get an accurate result. Numerous techniques were addressed by the researchers for the analysis of ECG. It is important to know different steps related to ECG analysis. A review is done based on different stages of ECG analysis and its applications in society.

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