EKGNet：一种用于患者内心律失常分类的10.96μW全模拟神经网络
EKGNet: A 10.96μW Fully Analog Neural Network for Intra-Patient Arrhythmia Classification
AI总结：
本文提出了一种结合模拟计算与深度学习的全模拟心律失常分类架构EKGNet，利用亚阈值区晶体管和模拟MAC电路消除ADC与SRAM需求，在MIT-BIH和PTB数据集上分别实现95%和94.25%的平均平衡准确率，为低功耗生物医学系统提供了新途径。
摘要：
我们提出了一种结合模拟计算与深度学习的心电图（ECG）心律失常分类的集成方法。我们提出了EKGNet，这是一种硬件高效且全模拟的心律失常分类架构，在低功耗下实现了高精度。该架构利用在亚阈值区工作的晶体管的能效，消除了对模数转换器（ADC）和静态随机存取存储器（SRAM）的需求。系统设计包含一种新颖的模拟顺序乘加（MAC）电路，可减轻工艺、电源电压和温度变化的影响。在PhysioNet的MIT-BIH和PTB Diagnostics数据集上的实验评估证明了该方法的有效性，在患者内心律失常分类和心肌梗死（MI）分类中分别实现了95%和94.25%的平均平衡准确率。这一创新方法为在生物医学应用中开发具有增强准确性和可迁移性的低功耗心律失常分类系统提供了一条有前景的途径。
英文摘要：
We present an integrated approach by combining analog computing and deep learning for electrocardiogram (ECG) arrhythmia classification. We propose EKGNet, a hardware-efficient and fully analog arrhythmia classification architecture that archives high accuracy with low power consumption. The proposed architecture leverages the energy efficiency of transistors operating in the subthreshold region, eliminating the need for analog-to-digital converters (ADC) and static random access memory (SRAM). The system design includes a novel analog sequential Multiply-Accumulate (MAC) circuit that mitigates process, supply voltage, and temperature variations. Experimental evaluations on PhysioNet's MIT-BIH and PTB Diagnostics datasets demonstrate the effectiveness of the proposed method, achieving average balanced accuracy of 95% and 94.25% for intra-patient arrhythmia classification and myocardial infarction (MI) classification, respectively. This innovative approach presents a promising avenue for developing low-power arrhythmia classification systems with enhanced accuracy and transferability in biomedical applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
