Deep Differentiable Logic Gate Networks (LGNs) and Lookup Table Networks (LUTNs) are demonstrated to be suitable for the automatic classification of electrocardiograms (ECGs) using the inter-patient paradigm. The methods are benchmarked using the MIT-BIH arrhythmia data set, achieving up to 94.28% accuracy and a $jκ$ index of 0.683 on a four-class classification problem. Our models use between 2.89k and 6.17k FLOPs, including preprocessing and readout, which is three to six orders of magnitude less compared to SOTA methods. A novel preprocessing method is utilized that attains superior performance compared to existing methods for both the mixed-patient and inter-patient paradigms. In addition, a novel method for training the Lookup Tables (LUTs) in LUTNs is devised that uses the Boolean equation of a multiplexer (MUX). Additionally, rate coding was utilized for the first time in these LGNs and LUTNs, enhancing the performance of LGNs. Furthermore, it is the first time that LGNs and LUTNs have been benchmarked on the MIT-BIH arrhythmia dataset using the inter-patient paradigm. Using an Artix 7 FPGA, between 2000 and 2990 LUTs were needed, and between 5 to 7 mW (i.e. 50 pJ to 70 pJ per inference) was estimated for running these models. The performance in terms of both accuracy and $jκ$-index is significantly higher compared to previous LGN results. These positive results suggest that one can utilize LGNs and LUTNs for the detection of arrhythmias at extremely low power and high speeds in heart implants or wearable devices, even for patients not included in the training set.

翻译：深度可微分逻辑门网络（LGN）与查找表网络（LUTN）被证明适用于采用患者间范式的心电图（ECG）自动分类。该方法在MIT-BIH心律失常数据集上进行了基准测试，在四分类问题上取得了高达94.28%的准确率及$jκ$指数0.683。我们的模型（包含预处理与读出过程）仅需使用2.89k至6.17k次浮点运算，比现有最优方法低三到六个数量级。本文采用了一种新颖的预处理方法，其在混合患者与患者间两种范式下均优于现有方法。此外，提出了一种基于多路复用器（MUX）布尔方程训练LUTN中查找表（LUT）的新方法。同时，首次在LGN与LUTN中应用脉冲编码，提升了LGN的性能。本研究亦首次在患者间范式下对LGN与LUTN在MIT-BIH心律失常数据集上进行了基准评估。在Artix 7 FPGA上运行这些模型需要2000至2990个LUT资源，估计功耗为5至7 mW（即每次推理消耗50 pJ至70 pJ能量）。其准确率与$jκ$指数均显著优于先前LGN的研究结果。这些积极成果表明，LGN与LUTN可用于心脏植入设备或可穿戴装置中以极低功耗与高速实现心律失常检测，即使对于未包含在训练集中的患者也具备适用性。