Mokhtari Rabah, Brahim Belhouari Samir, Kassoul Khelil, Hocini Abderraouf
Computer Science Department, Faculty of Mathematics and Computer Science, University of M'sila, PO Box 166, Ichbilia, 28000, M'sila, Algeria.
Division of Information and Computing Technology, College of Science and Engineering, Hamad Ben Khalifa University, Doha, Qatar.
Sci Rep. 2025 Feb 4;15(1):4285. doi: 10.1038/s41598-025-88119-9.
This paper presents the Progressive Moving Average Transform (PMAT), a novel signal transformation method for converting time-domain signals into 2D representations by progressively computing Moving Averages (MAs) with varying window sizes. The approach aims to enhance signal analysis and classification, particularly in the context of heartbeat classification. Our approach integrates PMAT with a 2D-Convolutional Neural Network (CNN) model for the classification of ECG heartbeat signals. The 2D-CNN model is employed to extract meaningful features from the transformed 2D representations and classify them efficiently. To assess the effectiveness of our approach, we conducted extensive simulations utilizing three widely-used databases: the MIT-BIH database and the INCART database, chosen to cover a wide range of heartbeats. Our experiments involved classifying more than 6 heartbeat types grouped into three main classes. Results indicate high accuracy and F1-scores, with 99.09% accuracy and 92.13% F1-score for MIT-BIH, and 98.37% accuracy and 79.37% F1-score for INCART. Notably, the method demonstrates robustness when trained on one database and tested on another, achieving accuracy rates exceeding 95% in both cases. Specifically, the method achieves 96% accuracy when trained on MIT-BIH and tested on the ST-T European database. These findings underscore the effectiveness and stability of the proposed approach in accurately classifying heartbeats across different datasets, suggesting its potential for practical implementation in medical diagnostics and healthcare systems.
本文提出了渐进移动平均变换（PMAT），这是一种新颖的信号变换方法，通过逐步计算具有不同窗口大小的移动平均值（MA），将时域信号转换为二维表示。该方法旨在增强信号分析和分类，特别是在心跳分类的背景下。我们的方法将PMAT与二维卷积神经网络（CNN）模型集成，用于心电图心跳信号的分类。二维CNN模型用于从变换后的二维表示中提取有意义的特征并进行有效分类。为了评估我们方法的有效性，我们利用三个广泛使用的数据库进行了广泛的模拟：麻省理工学院-比哈尔数据库（MIT-BIH）和INCART数据库，选择它们是为了涵盖广泛的心跳类型。我们的实验涉及对分为三个主要类别的6种以上心跳类型进行分类。结果表明具有较高的准确率和F1分数，对于MIT-BIH数据库，准确率为99.09%，F1分数为92.13%；对于INCART数据库，准确率为98.37%，F1分数为79.37%。值得注意的是，该方法在一个数据库上训练并在另一个数据库上测试时表现出鲁棒性，在两种情况下准确率均超过95%。具体而言，该方法在MIT-BIH数据库上训练并在ST-T欧洲数据库上测试时，准确率达到96%。这些发现强调了所提出方法在跨不同数据集准确分类心跳方面的有效性和稳定性，表明其在医学诊断和医疗保健系统中实际应用的潜力。
