July 30, 2020International Journal of Recent Technology and Engineering (IJRTE)Open Access

Machine Learning Classification and Feature Extraction of Arrhythmic ECG Data

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Structured PICO

Population

Arrhythmic and non-arrhythmic ECG data (e.g., MIT-BIH ECG-ID database)

Intervention

Machine learning classification algorithms (SVM, KNN, ANN) and feature extraction processes

Outcome

Outcome of the system using various machine learning classification algorithms for feature extraction and analysis of ECG signals

This survey highlights the utility of machine learning algorithms like SVM, KNN, and ANN for feature extraction and classification of arrhythmic ECG signals.

Discussion

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Implication

Survey of ECG feature extraction methods leaves open optimal arrhythmia detection approaches; prospective validation required before clinical use.

Cite This Study

Sumanta Kuila (2020) studied this question.

synapsesocial.com/papers/6a71e8e4febe604dd70a57fb https://doi.org/10.35940/ijrte.b3548.079220

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Implication

Survey of ECG feature extraction methods leaves open optimal arrhythmia detection approaches; prospective validation required before clinical use.

Machine Learning Classification and Feature Extraction of Arrhythmic ECG Data

Structured PICO

Discussion

Implication

Cite This Study

Machine Learning Classification and Feature Extraction of Arrhythmic ECG Data

Structured PICO

Discussion

Implication

Cite This Study

Also Consider

Also Consider