Kumar Sanjay, Mallik Abhishek, Kumar Akshi, Ser Javier Del, Yang Guang
Department of Computer Science and Engineering, Delhi Technological University, Main Bawana Road, New Delhi 110042, India.
Department of Computing & Mathematics, Faculty of Science & Engineering, Manchester Metropolitan University, Manchester, United Kingdom.
Comput Biol Med. 2023 Feb;153:106511. doi: 10.1016/j.compbiomed.2022.106511. Epub 2023 Jan 4.
Electrocardiogram (ECG) is a widely used technique to diagnose cardiovascular diseases. It is a non-invasive technique that represents the cyclic contraction and relaxation of heart muscles. ECG can be used to detect abnormal heart motions, heart attacks, heart diseases, or enlarged hearts by measuring the heart's electrical activity. Over the past few years, various works have been done in the field of studying and analyzing the ECG signals to detect heart diseases. In this work, we propose a deep learning and fuzzy clustering (Fuzz-ClustNet) based approach for Arrhythmia detection from ECG signals. We started by denoising the collected ECG signals to remove errors like baseline drift, power line interference, motion noise, etc. The denoised ECG signals are then segmented to have an increased focus on the ECG signals. We then perform data augmentation on the segmented images to counter the effects of the class imbalance. The augmented images are then passed through a CNN feature extractor. The extracted features are then passed to a fuzzy clustering algorithm to classify the ECG signals for their respective cardio diseases. We ran intensive simulations on two benchmarked datasets and evaluated various performance metrics. The performance of our proposed algorithm was compared with several recently proposed algorithms for heart disease detection from ECG signals. The obtained results demonstrate the efficacy of our proposed approach as compared to other contemporary algorithms.
心电图（ECG）是一种广泛用于诊断心血管疾病的技术。它是一种非侵入性技术，代表心肌的周期性收缩和舒张。通过测量心脏的电活动，心电图可用于检测异常的心脏运动、心脏病发作、心脏病或心脏扩大。在过去几年中，在研究和分析心电图信号以检测心脏病领域已经开展了各种工作。在这项工作中，我们提出了一种基于深度学习和模糊聚类（Fuzz-ClustNet）的方法，用于从心电图信号中检测心律失常。我们首先对收集到的心电图信号进行去噪，以消除诸如基线漂移、电力线干扰、运动噪声等误差。然后对去噪后的心电图信号进行分割，以便更专注于心电信号。接着我们对分割后的图像进行数据增强，以应对类别不平衡的影响。然后将增强后的图像通过一个卷积神经网络（CNN）特征提取器。提取的特征随后被传递到一个模糊聚类算法中，以对心电图信号进行分类，确定其各自的心脏病类型。我们在两个基准数据集上进行了密集模拟，并评估了各种性能指标。我们将所提出算法的性能与最近提出的几种用于从心电图信号中检测心脏病的算法进行了比较。所得结果表明，与其他当代算法相比，我们提出的方法是有效的。
