What is the clinical evidence from this study?

Study design: Other. Population: Cardiac arrhythmia. Intervention: MPA-CNN (Marine Predators Algorithm and Convolutional Neural Network) vs. CNN and other existing classification approaches. Primary outcome: Classification accuracy on MIT-BIH dataset.

January 1, 2021Open Access

A Hybrid Heartbeats Classification Approach Based on Marine Predators Algorithm and Convolution Neural Networks

Resultado clave

The proposed MPA-CNN approach achieved classification accuracy levels of 99.31%, 99.76%, and 99.47% on the MIT-BIH, EDB, and INCART databases, respectively, outperforming existing methods.

PICO estructurado

Población

ECG records from MIT-BIH (47 patients), EDB (79 participants), and INCART (32 Holter records) databases

Intervención

Hybrid approach based on marine predators algorithm (MPA) and convolutional neural network (CNN) called MPA-CNN

Comparador

Other existing classification approaches and metaheuristic algorithms

Resultado

Classification precision/accuracy for non-ectopic, ventricular ectopic, supraventricular ectopic, and fusion ECG types of arrhythmia

A novel hybrid MPA-CNN approach achieved high precision (>99%) in automatically classifying ECG arrhythmias across multiple standard databases.

Resultado numérico

Tasa de eventos absoluta: 99.31% vs 93.94%

Limitaciones

Imbalanced datasets where regular heartbeats occur much more often than irregular ones
Requires synthetic minority oversampling technique (SMOTE) to handle class imbalance

Resumen

The electrocardiogram (ECG) is a non-invasive tool used to diagnose various heart conditions. Arrhythmia is one of the primary causes of cardiac arrest. Early ECG beat classification plays a significant role in diagnosing life-threatening cardiac arrhythmias. However, the ECG signal is very small, the anti-interference potential is low, and the noise is easily influenced. Thus, clinicians face challenges in diagnosing arrhythmias. Thus, a method to automatically identify and distinguish arrhythmias from the ECG signal is invaluable. In this paper, a hybrid approach based on marine predators algorithm (MPA) and convolutional neural network (CNN) called MPA-CNN is proposed to classify the non-ectopic, ventricular ectopic, supraventricular ectopic, and fusion ECG types of arrhythmia. The proposed approach is a combination of heavy feature extraction and classification techniques; hence, outperforms other existing classification approaches. Optimal characteristics were derived directly from the raw signal to decrease the time required for and complexity of the computation. Precision levels of 99.31%, 99.76%, and 99.47% were achieved by the proposed approach on the MIT-BIH,EDB, and INCART databases, respectively.

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