Abstract：
OBJECTIVE: This study aims to address the challenges of imbalanced heartbeat classification using electrocardiogram (ECG). In this proposed novel deep-learning method, the focus is on accurately identifying minority classes in conditions characterized by significant imbalances in ECG data.
Approach: We propose a Feature Fusion Neural Network enhanced by a Dynamic Minority-Biased Batch Weighting Loss Function. This network comprises three specialized branches: the Complete ECG Data Branch for a comprehensive view of ECG signals, the Local QRS Wave Branch for detailed features of the QRS complex, and the R Wave Information Branch to analyze R wave characteristics. This structure is designed to extract diverse aspects of ECG data. The dynamic loss function prioritizes minority classes while maintaining the recognition of majority classes, adjusting the network\'s learning focus without altering the original data distribution. Together, this fusion structure and adaptive loss function significantly improve the network\'s ability to distinguish between various heartbeat classes, enhancing the accuracy of minority class identification.
Main Results: The proposed method demonstrated balanced performance within the MIT-BIH dataset, especially for minority classes. Under the intra-patient paradigm, the accuracy, sensitivity, specificity, and positive predictive value (PPV) for Supraventricular ectopic beat were 99.63%, 93.62%, 99.81%, and 92.98%, respectively, and for Fusion beat were 99.76%, 85.56%, 99.87%, and 84.16%, respectively. Under the inter-patient paradigm, these metrics were 96.56%, 89.16%, 96.84%, and 51.99% for Supraventricular ectopic beat, and 96.10%, 77.06%, 96.25%, and 13.92% for Fusion beat, respectively.
Significance: This method effectively addresses the class imbalance in ECG datasets. By leveraging diverse ECG signal information and a novel loss function, this approach offers a promising tool for aiding in the diagnosis and treatment of cardiac conditions.
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摘要：
目的：本研究旨在解决使用心电图（ECG）进行不平衡心跳分类的挑战。在这个提出的新颖的深度学习方法中，重点是准确识别以ECG数据显着失衡为特征的少数群体。＆＃xD;＆＃xD;方法：我们提出了一种通过动态少数群体偏置批量加权损失函数增强的特征融合神经网络。该网络包括三个专门的分支：完整的ECG数据分支，用于全面查看ECG信号，本地QRS波分支，用于QRS波群的详细特征，和R波信息分支分析R波特征。该结构被设计为提取ECG数据的不同方面。动态损失函数优先考虑少数类，同时保持对多数类的识别，在不改变原始数据分布的情况下调整网络的学习重点。一起,这种融合结构和自适应损失函数显著提高了网络区分各种心跳类别的能力，提高了少数民族阶级识别的准确性。＆＃xD;＆＃xD;主要结果：所提出的方法在MIT-BIH数据集中展示了平衡的性能，尤其是少数民族。在患者内部范式下，准确性,灵敏度,特异性，室上性异位搏动的阳性预测值（PPV）为99.63％，93.62%,99.81%,92.98%，分别,融合节拍为99.76%,85.56%,99.87%,和84.16%，分别。在患者间范式下，这些指标是96.56%，89.16%，96.84%,室上性异位搏动为51.99%，和96.10%，77.06%,96.25%,和13.92%的融合节拍，分别。＆＃xD;＆＃xD;意义：该方法有效地解决了ECG数据集中的类不平衡。通过利用不同的ECG信号信息和新颖的损失函数，这种方法为心脏疾病的诊断和治疗提供了有希望的工具.
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