Cardiac arrhythmia detection in ECG signals by feature Extraction and support vector machine

Malik, Gorav Kumar; Kumar, Yatindra; Panda, Manoj Kumar

doi:10.15439/2017r63

Cited by 7 publications

(6 citation statements)

References 8 publications

(10 reference statements)

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“…A unique RNN configuration made up of two LSTM networks was defined by Banerjee et al [12]. This network is used to analyze the ECG signal's temporal features, such as the RR and PR intervals.…”

Section: One Dimensional (1dmentioning

confidence: 99%

Deep Learning Frameworks for Cardiovascular Arrhythmia Classification

Malleswari,

Padala,

Pullarao

et al. 2023

IJRITCC

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Arrhythmia classification is a prominent research problem due to the computational complexities of learning the morphology of various ECG patterns and its wide prevalence in the medical field, particularly during the COVID-19 pandemic. In this article, we used Empirical Mode Decomposition and Discrete Wavelet Transform for preprocessing and then the modified signal is classified using various classifiers such as Decision Tree, Logistic Regression, Gaussian Naïve Bayes, Random Forest, Linear SVM, Polynomial SVM, RBF SVM, Sigmoid SVM and Convolutional Neural Networks. The proposed method classify the data into five classes N (Normal), S (Supraventricular premature) beat, (V) Premature ventricular contraction, F (Fusion of ventricular and normal), and Q, (Unclassifiable Beat) using softmax regressor at the end of the network. The proposed approach performs well in terms of classification accuracy when tested using ECG signals acquired from the MIT-BIH database. In comparison to existing classifiers, the Accuracy, Precision, Recall, and F1 score values of the proposed technique are 98.5%, 96.9%, 94.3%, and 91.32%, respectively.

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Section: One Dimensional (1dmentioning

confidence: 99%

Deep Learning Frameworks for Cardiovascular Arrhythmia Classification

Malleswari,

Padala,

Pullarao

et al. 2023

IJRITCC

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“…From the number of heart rate can be detected abnormalities. Gaurav Kumar Malik et al [13] develop an automated physiological signal diagnostic tool that can help to detect Heart disease at the early stage. The paper gives the use of methods like fourth order wavelet decomposition, wavelet decomposition used for time-frequency representation and feature extraction.…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction and Analysis of Heart Disease Using SVM Algorithm

Patil¹

2019

IJRASET

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In this paper the proposed system predicts Heart Disease using SVM algorithm. Heart disease is the most common cause of death globally. Heart disease prediction using data mining is one of the most interesting and challenging tasks. The shortage of specialists and high wrongly diagnosed cases has necessitated the need to develop a fast and efficient detection system. According to past system the integration of clinical decision support with computer based patient record can reduce medical errors, can be made more precise and hence enhance patient safety. The system helps in prediction of heart disease by considering risky factor associated with heart disease. Here system applies support vector machine algorithm on historical information/data of patient and it provides features like Age, Sex, Smoking, Overweight, Alcohol Intake, Bad Cholesterol ,Blood Pressure and Heart Rate to make prediction of coronary heart disease with higher accuracy.

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“…For example , the intensity of heartbea ts is differ ent betw een children vs. adults, men vs. women, and athletes vs. non-athletes. Some studies consider ed personal data (i.e., stereotypes) of the patient in the classif ication process to enhance the results of the classif ication algorithms [7,8]. Researchers have been working on ECG classif ication for the last few years.…”

Section: Introductionmentioning

confidence: 99%

Arrhythmia Detection by Using Chaos Theory with Machine Learning Algorithms

Aboghazalah,

El-kafrawy,

Ahmed

et al. 2024

CMC

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Heart monitoring improves life quality. Electrocardiograms (ECGs or EKGs) detect heart irregularities. Machine learning algorithms can create a few ECG diagnosis processing methods. The first method uses raw ECG and time-series data. The second method classifies the ECG by patient experience. The third technique translates ECG impulses into Q waves, R waves and S waves (QRS) features using richer information. Because ECG signals vary naturally between humans and activities, we will combine the three feature selection methods to improve classification accuracy and diagnosis. Classifications using all three approaches have not been examined till now. Several researchers found that Machine Learning (ML) techniques can improve ECG classification. This study will compare popular machine learning techniques to evaluate ECG features. Four algorithms-Support Vector Machine (SVM), Decision Tree, Naive Bayes, and Neural Network-compare categorization results. SVM plus prior knowledge has the highest accuracy (99%) of the four ML methods. QRS characteristics failed to identify signals without chaos theory. With 99.8% classification accuracy, the Decision Tree technique outperformed all previous experiments.

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Cardiac arrhythmia detection in ECG signals by feature Extraction and support vector machine

Cited by 7 publications

References 8 publications

Deep Learning Frameworks for Cardiovascular Arrhythmia Classification

Deep Learning Frameworks for Cardiovascular Arrhythmia Classification

Prediction and Analysis of Heart Disease Using SVM Algorithm

Arrhythmia Detection by Using Chaos Theory with Machine Learning Algorithms

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