This paper introduces LLT-ECG, a novel method for electrocardiogram (ECG) signal classification that leverages concepts from theoretical physics to automatically generate features from time series data. Unlike traditional deep learning approaches, LLT-ECG operates in a forward manner, eliminating the need for backpropagation and hyperparameter tuning. By identifying linear laws that capture shared patterns within specific classes, the proposed method constructs a compact and verifiable representation, enhancing the effectiveness of downstream classifiers. We demonstrate LLT-ECG's state-of-the-art performance on real-world ECG datasets from PhysioNet, underscoring its potential for medical applications where speed and verifiability are crucial.

翻译：本文提出LLT-ECG，一种用于心电图（ECG）信号分类的新方法，该方法借鉴理论物理学的概念，从时间序列数据中自动生成特征。与传统深度学习方法不同，LLT-ECG以前向方式运行，无需反向传播和超参数调优。通过识别能够捕捉特定类别内共享模式的线性定律，所提方法构建了紧凑且可验证的表示，从而提升了下游分类器的效能。我们在PhysioNet的真实心电数据集上验证了LLT-ECG的先进性能，突显了其在速度和可验证性至关重要的医疗应用中的潜力。