What is the clinical evidence from this study?

Study design: Other. Population: Cardiac arrhythmia (n=47). Intervention: 9-layer Convolutional Neural Network (CNN). Primary outcome: Overall classification accuracy.

January 1, 2023Computers, materials & continua/Computers, materials & continua (Print)Open Access

Classification of Electrocardiogram Signals for Arrhythmia Detection Using Convolutional Neural Network

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Key result

A proposed 9-layer Convolutional Neural Network classified ECG signals into five primary arrhythmia categories with an overall accuracy of 99.68%.

Why the study?

Early diagnosis of cardiac arrhythmia is challenging, and manual analysis of ECG data from Holter monitors is difficult.

Population

MIT-BIH arrhythmia dataset

Comparison

9-layer-based CNN model to classify ECG signals into five categories

Authors

MRMuhammad Aleem RazaLahore Leads University SASyed Muhammad AnwarChildren's National KNKashif NisarSussex County Community College

Discussion

Member takes

Implication

May aid automated ECG arrhythmia screening; leaves open prospective validation before clinical use.

Structured PICO

Population

47 patients providing 123,998 ECG signal segments from the MIT-BIH arrhythmia database used to evaluate a deep learning classification model.

Intervention

9-layer-based Convolutional Neural Network (CNN) model

Outcome

Classification accuracy, sensitivity, and positive predictive value for 5 primary categories of ECG signals

A 9-layer Convolutional Neural Network can classify ECG signals into five arrhythmia categories with 99.68% accuracy, demonstrating potential for automated Holter monitor analysis.

Cite This Study

Raza et al. (2023) studied Cardiac arrhythmia (n=47). 9-layer Convolutional Neural Network (CNN) was evaluated on Overall classification accuracy. A proposed 9-layer Convolutional Neural Network classified ECG signals into five primary arrhythmia categories with an overall accuracy of 99.68%.

synapsesocial.com/papers/6a32a73d4b167149fc4f982c https://doi.org/10.32604/cmc.2023.032275

View Full Paper

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Classification of Electrocardiogram Signals for Arrhythmia Detection Using Convolutional Neural Network

Discussion

Member takes

Implication

Structured PICO

Cite This Study

Classification of Electrocardiogram Signals for Arrhythmia Detection Using Convolutional Neural Network

Discussion

Member takes

Implication

Structured PICO

Cite This Study

Also Consider

Also Consider