scholarly journals Noisy ECG Signal Data Transformation to Augment Classification Accuracy

2022 ◽  
Vol 71 (2) ◽  
pp. 2191-2207
Author(s):  
Iqra Afzal ◽  
Fiaz Majeed ◽  
Muhammad Usman Ali ◽  
Shahzada Khurram ◽  
Akber Abid Gardezi ◽  
...  
Keyword(s):  
Classification Accuracy ◽  
Data Transformation ◽  
Ecg Signal

Signal Analysis for ECG Interpretation

1990 ◽  
Vol 29 (04) ◽  
pp. 317-329 ◽  
Author(s):  
J. H. van Bemmel ◽  
Chr. Zywietz ◽  
J. A. Kors
Keyword(s):  
Feature Selection ◽  
Data Reduction ◽  
Signal Analysis ◽  
Digital Filtering ◽  
Data Transformation ◽  
Diagnostic Classification ◽  
Ecg Signal ◽  
Boundary Recognition ◽  
Transformation Stage ◽  
Ecg Interpretation

AbstractIn ECG interpretation usually two main areas are discerned: the signal analysis and the diagnostic classification. This article reviews the major developments in the first area. ECG signal analysis itself is subdivided into the stages data acquisition, data transformation, feature selection, and data reduction. These stages are consecutively reviewed, while in the data transformation stage digital filtering, detection, wave typing, beat selection, and boundary recognition are discussed.

Deep Learning Based Adaptive Recurrent Neural Network for Detection of Myocardial Infarction

2021 ◽  
Vol 11 (12) ◽  
pp. 3044-3053
Author(s):  
Rakesh Kumar Mahendran ◽  
V. Prabhu ◽  
V. Parthasarathy ◽  
A. Mary Judith
Keyword(s):  
Neural Network ◽  
Myocardial Infarction ◽  
Deep Learning ◽  
Recurrent Neural Network ◽  
Classification Accuracy ◽  
Learning Algorithm ◽  
Training Data ◽  
Discrete Wavelet ◽  
Ecg Signal ◽  
Detection Techniques

Myocardial infarction (MI) may precipitate severe health damage and lead to irreversible death of the heart muscle, the result of prolonged lack of oxygen if it is not treated in a timely manner. Lack of accurate and early detection techniques for this heart disease has reduced the efficiency of MI diagnosis. In this paper, the design, and implementation of an efficient deep learning algorithm called Adaptive Recurrent neural network (ARNN) is proposed for the MI detection. The main objective of the proposed work is the accurate identification of MI disease using ECG signals. ECG signal denoising has been performed using the Multi-Notch filter, which removes the specified noise frequency range. Discrete wavelet transform (DWT) is utilized for performing the feature extraction that decomposes the ECG signal into varied scales with waveletfiltering bank. After the extraction of specific QRS features, classification of the defected and normal ECG arrhythmic beat has been performed using the deep learning-based ARNN classifier. The MIT-BIH database has been used for testing and training data. The performance of the proposed algorithm is evaluated based on classification accuracy. Results that are attained include the classification accuracy of about 99.21%, 99% of sensitivity and 99.4% of specificity with PPV and NPV of about 99.4 and 99.01 values indicate the enhanced performance of our proposed work compared with the conventional LSTM-CAE and LSTM-CNN techniques.

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.

scholarly journals Nonlinear Analysis of Electrocardiography Signals for Atrial Fibrillation

2013 ◽  
Vol 2013 ◽  
pp. 1-4 ◽  
Author(s):  
Necmettin Sezgin
Keyword(s):  
Atrial Fibrillation ◽  
Classification Accuracy ◽  
Heart Beat ◽  
Ecg Signal ◽  
Bispectral Analysis ◽  
Classification Result ◽  
Ecg Signals ◽  
Learning Machine ◽  
Elm Classifier ◽  
Normal Ecgs

This paper aims to analyze the electrocardiography (ECG) signals for patient with atrial fibrillation (AF) by using bispectrum and extreme learning machine (ELM). AF is the most common irregular heart beat disease which may cause many cardiac diseases as well. Bispectral analysis was used to extract the nonlinear information in the ECG signals. The bispectral features of each ECG episode were determined and fed to the ELM classifier. The classification accuracy of ELM to distinguish nonterminating, terminating AF, and terminating immediately AF was 96.25%. In this study, the normal ECG signal was also compared with AF ECG signal due to the nonlinearity which was determined by bispectrum. The classification result of ELM was 99.15% to distinguish AF ECGs from normal ECGs.

scholarly journals Intellectual heartbeats classification model for diagnosis of heart disease from ECG signal using hybrid convolutional neural network with GOA

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Ankita Tyagi ◽  
Ritika Mehra
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Convolutional Neural Network ◽  
Classification Accuracy ◽  
Heart Diseases ◽  
Classification Model ◽  
Ecg Signal ◽  
Grasshopper Optimization Algorithm ◽  
Grasshopper Optimization ◽  
Selection Of

AbstractAutomatic heart disease detection from human heartbeats is a challenging and intellectual assignment in signal processing because periodically monitoring of the heart beat arrhythmia for patient is an essential task to reduce the death rate due to cardiovascular disease (CVD). In this paper, the focus of research is to design hybrid Convolutional Neural Network (CNN) architecture by making use of Grasshopper Optimization Algorithm (GOA) to classify different types of heart diseases from the ECG signal or human heartbeats. Convolutional Neural Network (CNN) as an artificial intelligence approach is widely used in computer vision-based medical data analysis. However, the traditional CNN cannot be used for classification of heart diseases from the ECG signal because lots of noise or irrelevant data is mixed with signal. So this study utilizes the pre-processing and selection of feature for proper heart diseases classification, where Discrete Wavelet Transform (DWT) is used for the noise reduction as well as segmentation of ECG signal and Grasshopper Optimization Algorithm (GOA) is used for selection of R-peaks features from the extracted feature sets in terms of R-peaks and R-R intervals that help to attain better classification accuracy. For training as well as testing of projected Heartbeats Classification Model (HCM), the Standard MIT-BIH arrhythmia database is utilized with hybrid Convolutional Neural Network (CNN) architecture. The assortment of proper R-peaks and R-R intervals is a major factor and because of the deficiency of apposite pre-processing phases like noise removal, signal decomposition, smoothing and filtering, the uniqueness of extracted feature is less. The experimental outcomes show that the planned HCM is effective for detecting irregular human heartbeats via R-peaks and R-R intervals. When the proposed Heartbeats Classification Model (HCM) was verified on the database, model achieved higher efficiency than other state-of-the-art techniques for 16 heartbeat disease categories and the average classification accuracy is 99.58% with fast and robust responses where the correctly classified heartbeats are 86,005 and misclassified beats is only 108 with 0.42% error rate.

scholarly journals Analysis of Inducing Factors of Chronic Pulmonary Heart Disease Caused by Chronic Obstructive Pulmonary Disease at High Altitude through Epidemiological Investigation under Intelligent Medicine and Big Data

2022 ◽  
Vol 2022 ◽  
pp. 1-10
Author(s):  
Jiong Huang ◽  
Fulin Dang
Keyword(s):  
Neural Network ◽  
Risk Factors ◽  
Chronic Obstructive Pulmonary Disease ◽  
Big Data ◽  
High Altitude ◽  
Classification Accuracy ◽  
Chronic Obstructive ◽  
Ecg Signal ◽  
Obstructive Pulmonary Disease ◽  
Chronic Pulmonary Heart Disease

This study explores the risk factors of chronic pulmonary heart disease (CPHD) induced by plateau chronic obstructive pulmonary disease (COPD) based on intelligent medical treatment and big data of electrocardiogram (ECG) signal. Based on GPU, a wavelet algorithm is introduced to extract features of ECG signal, and it was combined with generalized regression neural network (GRNN) to improve classification accuracy. From June 2018 to December 2020, 10,185 patients diagnosed with COPD in the plateau area by pulmonary function testing, ECG, and chest X-ray at X Hospital are taken as the research objects to evaluate the distribution of CPHD incidence at different ages and altitudes. The running time of GTX780Ti is about 15 times shorter than that of CPU. The accuracy of N detection based on the GPU-accelerated neural network model reached 98.06%. Accuracy (Acc), sensitivity (Se), specificity (Sp), and positive rate (PR) of V were 99.03%, 89.17%, 98.92%, and 93.18%, respectively. The Acc, Se, Sp, and PR of S were 99.54%, 86.22%, 99.74%, and 92.56%, respectively. The GRNN classification accuracy was up to 98%. 19% of COPD patients were diagnosed with CPHD, including 1,409 males (72.82%) and 526 females (36.24%). The highest prevalence of CPHD was 64.60% when the altitude was 1,900–2,499 m, and the prevalence was only 2.43% when the altitude was ≥3,500 m. The highest prevalence of CPHD was 63.77% at the age of 61–70 years, and the lowest prevalence at the age of 15∼20 years was only 0.26%. Therefore, the GPU-based neural network model improved the classification accuracy of ECG signals. Age and altitude were risk factors for CPHD induced by high-altitude COPD, which provided a reference for the prevention, diagnosis, and treatment of CPHD in high-altitude areas.

scholarly journals Classication of Electrocardiogram Signals With Waveform Morphological Analysis and Support Vector Machines

2021 ◽  
Author(s):  
Hongqiang Li ◽  
Zhixuan An ◽  
Shasha Zuo ◽  
Wei Zhu ◽  
Lu Cao ◽  
...  
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Classification Accuracy ◽  
Morphological Analysis ◽  
Wavelet Packet ◽  
Feature Space ◽  
Support Vector ◽  
Ecg Signal ◽  
Rr Intervals ◽  
Ecg Signals

Abstract Background: Electrocardiogram (ECG) indicates the occurrence of various cardiac diseases, and the accurate classification of ECG signals is important for the automatic diagnosis of arrhythmia. Methods: This paper presents a novel classification method based on multifeatures by combining waveform morphology and frequency-domain statistical analysis, which offer a better classification accuracy and minimise the time spent for classifying signals. A wavelet packet is used to decompose a de-noised ECG signal, and the singular value, maximum value and standard deviation of the decomposed wavelet packet coefficients are calculated to obtain the frequency domain feature space. The slope threshold method is applied to detect R peak and calculate RR intervals, and the first two RR intervals are extracted as time-domain features. The fusion feature space is composed of time-domain and frequency-domain features. Results: A combination of support vector machine (SVM) with the help of grid search and waveform morphological analysis is applied to complete nine types of ECG signal classification. Computer simulations show that the accuracy of the proposed algorithm on multiple types of arrhythmia databases can reach 96.67%.Conclusions: The proposed approach classified the arrhythmias of ECG signals with promising results. The experimental results reveal that classification accuracy can reach 96.67% when the penalty factor C is 9.1896, and the kernel function parameter g is 0.10882.

scholarly journals PENGARUH TRANSFORMASI DATA PADA METODE LEARNING VECTOR QUANTIZATION TERHADAP AKURASI KLASIFIKASI DIAGNOSIS PENYAKIT JANTUNG

2021 ◽  
Vol 10 (1) ◽  
pp. 21-30
Author(s):  
Arafa Rahman Aziz ◽  
Budi Warsito ◽  
Alan Prahutama
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Vector Quantization ◽  
Classification Accuracy ◽  
Data Transformation ◽  
Learning Vector Quantization ◽  
Z Score ◽  
Raw Data ◽  
Neural Network Learning ◽  
Transformation Methods

Learning Vector Quantization (LVQ) is a type of Artificial Neural Network with a supervised learning process based on competitive learning. Despite the absence of assumptions in LVQ is an advantage, it can be a problem when the predictor variables have big different ranges.This problems can be overcome by equalizing the range of all variables by data transformation so that all variables have relatively same effect. Heart Disease UCI dataset which used in this study is transformed by several transformation methods, such as minmax, decimal scaling, z-score, mean-MAD, sigmoid, and softmax. The result show that the six transformed data can provide better LVQ classification accuracy than the raw data which has 75.99% for training performance accuracy. LVQ classification accuracy with data transformation of minmax, decimal scaling, z-score, mean-MAD, sigmoid, and softmax are 89.16%, 88.22%, 89.7%, 90.1%, 88.17% and 92.18%. Based on the One-way ANOVA test and DMRT post hoc test  known that there are significant differences between the results of the classification with data transformations and raw data in 0,05 significant level of α. It is also known that the best data transformation methods are softmax for training and sigmoid for testing. Keywords: heart disease, neural network, learning vector quantization, classification, data transformation

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