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) 是对各种形式的心脏病做出决定的最有效工具。因此，一些研究人员将注意力集中在心电信号上，以高精度、高效地提取心跳特征。本文提供了一种混合方法，通过提供预处理的 ECG 信号作为激励，使用前馈反向传播神经网络 (FFBPNN) 对不同的心脏状况进行分类。修正后的心电信号是通过EMD（经验模式分解）和DWT（离散小波变换）的组合得到的。在这个提出的方法中，输入信号首先被分解为固有模式函数 (IMF)，并且前三个 IMF 被组合以获得修改的部分表示的 ECG 样本，然后使用 DWT 获得改进的去噪信号。该预处理信号使用神经网络架构进行分类。对于 EMD 方法，基于 ECG 的 EMD-DWT 信号为 DWT 方法提供了 67%、0762%、90%、4305% 和 95,0797% 的改进分类精度。该方法应用于 MIT-BIH 数据库，在分类准确性方面，发现高于不同的方法。