What is the clinical evidence from this study?

Study design: Other. Population: Cardiovascular diseases (myocardial infarction, bundle branch block, dysrhythmia) (n=290). Intervention: AlexNet and parallel dual branch fusion network model vs. State-of-the-art models (e.g., Hybrid AlexNet SVM, DCNN LSTM). Primary outcome: ECG classification test set accuracy.

November 6, 2024Scientific Reports41 citationsOpen Access

Deep learning hybrid model ECG classification using AlexNet and parallel dual branch fusion network model

MKManjur KolharKing Faisal University ARAhmed M. Al RajehKing Faisal University

Key Result

The proposed AlexNet and dual branch fusion network models achieved test set accuracies of 99% for categorizing ECG signals into myocardial infarction, bundle branch block, and dysrhythmia.

Structured PICO

Do AI models like AlexNet and a dual branch fusion network model improve ECG classification accuracy compared to state-of-the-art models?

Population

290 individuals from the PTB Diagnostic ECG Database, including healthy participants and those with various heart conditions, whose ECG recordings were used to train and test classification models.

Intervention

AI models (AlexNet and a parallel dual branch fusion network model)

Comparator

State of the art models such as Hybrid AlexNet SVM and DCNN LSTM

Outcome

ECG classification accuracysurrogate

Deep learning models like AlexNet and dual branch fusion networks achieve 99% accuracy in automated ECG classification, offering potential for clinical implementation.

Limitations

Clinical summaries were missing for the records of twenty-two subjects in the database.
Deciphering the clinical importance of certain conditions like right bundle branch block can pose intricate challenges when considering other heart conditions.

Abstract

Cardiovascular diseases are a cause of death making it crucial to accurately diagnose them. Electrocardiography plays a role in detecting heart issues such as heart attacks, bundle branch blocks and irregular heart rhythms. Manual analysis of ECGs is prone to mistakes and time consuming, underscoring the importance of automated methods. This study uses AI models like AlexNet and a dual branch model for categorizing ECG signals from the PTB Diagnostic ECG Database. AlexNet achieved a validation accuracy of 98.64% and a test set accuracy of 99% while the dual branch fusion network model achieved a test set accuracy of 99%. Data preprocessing involved standardizing, balancing and reshaping ECG signals. These models exhibited precision, sensitivity and specificity. In comparison to state of the arts' models such as Hybrid AlexNet SVM and DCNN LSTM our proposed models displayed performance. The high accuracy rates of 99% underscore their potential for ECG classification. These results validate the advantages of incorporating learning models into setups for automated ECG analysis providing adaptable solutions for various healthcare settings including rural areas.

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