Key result
OLCPA-CNN model achieves ~98% classification accuracy for arrhythmia detection.
Why the study?
The colony predation algorithm suffers from poor exploratory ability and can become trapped in local optima when tuning convolutional neural networks.
The proposed OLCPA-CNN model demonstrates high accuracy in automated ECG signal classification, potentially aiding in efficient and automated arrhythmia detection.
Recently, swarm intelligence algorithms have received much attention because of their flexibility for solving complex problems in the real world. Recently, a new algorithm called the colony predation algorithm (CPA) has been proposed, taking inspiration from the predatory habits of groups in nature. However, CPA suffers from poor exploratory ability and cannot always escape solutions known as local optima. Therefore, to improve the global search capability of CPA, an improved variant (OLCPA) incorporating an orthogonal learning strategy is proposed in this paper. Then, considering the fact that the swarm intelligence algorithm can go beyond the local optimum and find the global optimum solution, a novel OLCPA-CNN model is proposed, which uses the OLCPA algorithm to tune the parameters of the convolutional neural network. To verify the performance of OLCPA, comparison experiments are designed to compare with other traditional metaheuristics and advanced algorithms on IEEE CEC 2017 benchmark functions. The experimental results show that OLCPA ranks first in performance compared to the other algorithms. Additionally, the OLCPA-CNN model achieves high accuracy rates of 97.7% and 97.8% in classifying the MIT-BIH Arrhythmia and European ST-T datasets.
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He et al. (2023) studied ECG signal classification (arrhythmia and ST-T variations) (n=13,000). OLCPA-CNN model vs. Other classification models (e.g., standard CNN, CPA-CNN) was evaluated on Classification accuracy. The OLCPA-CNN model achieved high classification accuracy rates of 97.9% and 97.8% on the MIT-BIH Arrhythmia and European ST-T datasets, respectively.
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