University College of Engineering, JNTUK, Kakinada, AP, India.
ECE Department, Vasavi College of Engineering, Hyderabad, Telangana, India.
Interdiscip Sci. 2021 Sep;13(3):443-450. doi: 10.1007/s12539-021-00416-9. Epub 2021 Jan 22.
Electrocardiogram (ECG) is the most effective instrument for making decisions about various forms of heart disease. As a result, several researchers have focused on the ECG signal to extract the features of heartbeats with high precision and efficiency. This article offers a hybrid approach to classifying different cardiac conditions using the Feed Forward Back Propagation Neural Network (FFBPNN), by providing a pre-processed ECG signal as an excitation. The modified ECG signal is obtained through the combination of EMD (Empirical Mode Decomposition) and DWT (Discrete Wavelet Transform). In this proposed method, the input signal is first decomposed into the Intrinsic Mode Functions (IMF's) and the first three IMF's are combined to obtain a modified partially denoted ECG sample and then DWT is used to obtain an improved denoised signal. This pre-processed signal is classified using the Neural Network architecture. For the EMD approach, the ECG-based EMD-DWT signal provides improved classification accuracy of 67, 0762 percent, 90, 4305 percent for the DWT approach, and 95,0797 percent for the proposed technique. The methodology is applied to the MIT-BIH database and, in terms of classification accuracy, is found to be higher than the different methodologies.
心电图（ECG）是用于决策各种形式心脏病的最有效仪器。因此，有几位研究人员专注于 ECG 信号，以高精度和高效率提取心跳特征。本文提出了一种使用前馈反向传播神经网络（FFBPNN）对不同心脏状况进行分类的混合方法，通过提供预处理的 ECG 信号作为激励。通过经验模态分解（EMD）和离散小波变换（DWT）的组合，获得了修改后的 ECG 信号。在该方法中，输入信号首先分解为固有模式函数（IMF），然后将前三个 IMF 组合以获得修改后的部分表示 ECG 样本，然后使用 DWT 获得改进的去噪信号。使用神经网络结构对预处理后的信号进行分类。对于 EMD 方法，基于 ECG 的 EMD-DWT 信号提供了改进的分类准确性，分别为 67.0762%、90.4305%和 95.0797%，而对于 DWT 方法则为 95.0797%。该方法应用于 MIT-BIH 数据库，从分类准确性的角度来看，它高于其他不同的方法。
