Chen Yunfan, Ye Jinxing, Li Yuting, Luo Zhe, Luo Jieqiang, Wan Xiangkui
Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy, Hubei University of Technology, Wuhan 430068, China.
School of Computer Science, Hubei University of Technology, Wuhan 430068, China.
Biosensors (Basel). 2025 Jun 17;15(6):392. doi: 10.3390/bios15060392.
Myocardial infarction (MI) is a critical cardiovascular disease characterized by extensive myocardial necrosis occurring within a short timeframe. Traditional MI detection and localization techniques predominantly utilize single-domain features as input. However, relying solely on single-domain features of the electrocardiogram (ECG) proves challenging for accurate MI detection and localization due to the inability of these features to fully capture the complexity and variability in cardiac electrical activity. To address this, we propose a multi-domain feature fusion convolutional neural network (MFF-CNN) that integrates the time domain, frequency domain, and time-frequency domain features of ECG for automatic MI detection and localization. Initially, we generate 2D frequency domain and time-frequency domain images to combine with single-dimensional time domain features, forming multi-domain input features to overcome the limitations inherent in single-domain approaches. Subsequently, we introduce a novel MFF-CNN comprising a 1D CNN and two 2D CNNs for multi-domain feature learning and MI detection and localization. The experimental results demonstrate that in rigorous inter-patient validation, our method achieves 99.98% detection accuracy and 84.86% localization accuracy. This represents a 3.43% absolute improvement in detection and a 16.97% enhancement in localization over state-of-the-art methods. We believe that our approach will greatly benefit future research on cardiovascular disease.
心肌梗死（MI）是一种严重的心血管疾病，其特征是在短时间内发生广泛的心肌坏死。传统的MI检测和定位技术主要利用单域特征作为输入。然而，由于心电图（ECG）的这些特征无法完全捕捉心脏电活动的复杂性和变异性，仅依靠ECG的单域特征进行准确的MI检测和定位具有挑战性。为了解决这个问题，我们提出了一种多域特征融合卷积神经网络（MFF-CNN），它整合了ECG的时域、频域和时频域特征，用于自动MI检测和定位。首先，我们生成二维频域和时频域图像，与一维时域特征相结合，形成多域输入特征，以克服单域方法固有的局限性。随后，我们引入了一种新颖的MFF-CNN，它由一个一维CNN和两个二维CNN组成，用于多域特征学习以及MI检测和定位。实验结果表明，在严格的患者间验证中，我们的方法实现了99.98%的检测准确率和84.86%的定位准确率。与现有方法相比，这在检测方面实现了3.43%的绝对提升，在定位方面提高了16.97%。我们相信我们的方法将极大地有益于未来心血管疾病的研究。
