ECG signal processing using classifier to analyses cardiovascular disease

Khandait, Neha Vikas; Shirolkar, Amardeep

doi:10.1109/iccmc.2019.8819777

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…They also suggested that designed channels are based on ousting collectibles. Furthermore, the paper contains SVM estimation that includes a customized classifier to perceive the five pathologies [2].…”

Section: ░ 2 Literature Reviewmentioning

confidence: 99%

Smart Heart Disease Detection using Particle Swarm Optimization and Support Vector Machine

Awati¹,

Patil²,

Kumbhar³

2021

IJEER

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Healthcare and disease detection in early stage is important in every human being. Proper and optimum detection of disease with smart controller is done using Particle swarm optimization (PSO) and Support Vector Machine (SVM). The research includes the Fuzzy Proportional Integral and Derivative (Fuzzy PID) controller was used with support vector machine to classify the heart disease. Particle Swarm Optimization is designed to remove the noise introduced in Electrocardiogram signal. Fuzzy PID controller was implemented for disease detection and prediction. Fuzzy PID controller provides most accurate and stable results.

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Section: ░ 2 Literature Reviewmentioning

confidence: 99%

Smart Heart Disease Detection using Particle Swarm Optimization and Support Vector Machine

Awati¹,

Patil²,

Kumbhar³

2021

IJEER

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“…Signal processing in electrocardiogram covered detection signal pattern. Electrocardiogram signal pattern detection such as pattern P wave detection [15], R peaks detection [16], filter for noise reduction in electrocardiogram [17], and filter for QRS pattern detection [18] [19].…”

Section: Introductionmentioning

confidence: 99%

Electrocardiogram Abnormal Classification Based on Abnormality Signal Feature

Purnama¹,

Afandi²

2021

JNTE

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Heart rate abnormalities can lead to many cardiovascular diseases such as heart arrythmia, heart failure, heart valve disease and many more. Some cardiovascular disease can cause death. Abnormalities signal feature can be seen using electrocardiogram. Electrocardiogram is an electric signal record from heart activity. Normal heart and abnormal heart have a different electrocardiogram signal pattern. This research is aim to detect abnormality from heart rate using electrocardiogram abnormality signal feature. Abnormality signal pattern can be used to classify normal and abnormal heart rate. Abnormality feature consists of P signal condition, R signal condition, P R interval rate, and double R interval. Electrocardiogram data that used in this study is obtain from MIT-BIH Arrythmia database. 20 electrocardiogram data have been used to see detection and classification performance while classifying normal and abnormal heart rate. Research result shows that feature based has 90.00% in accuracy, 90.00%in precision, and 90.00% in sensitivity while classify normal and abnormal heart rate. Research result can conclude that abnormality feature can be used to classify normal and abnormal heart rate. This method can be used for embedded system device that has limitation in memory and size.

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ECG Signal Analysis for the Detection of Myocardial Infarction

Adarsh,

Kiranmayi

2024

2024 International Conference on Smart Systems for Applications in Electrical Sciences (ICSSES)

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ECG signal processing using classifier to analyses cardiovascular disease

Cited by 4 publications

References 5 publications

Smart Heart Disease Detection using Particle Swarm Optimization and Support Vector Machine

Smart Heart Disease Detection using Particle Swarm Optimization and Support Vector Machine

Electrocardiogram Abnormal Classification Based on Abnormality Signal Feature

ECG Signal Analysis for the Detection of Myocardial Infarction

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