IEEE Trans Biomed Circuits Syst. 2022 Aug;16(4):703-713. doi: 10.1109/TBCAS.2022.3196059. Epub 2022 Oct 12.
This paper presents an ultra-low power electrocardiography (ECG) processor application-specific integrated circuit (ASIC) for the real-time detection of abnormal cardiac rhythms (ACRs). The proposed ECG processor can support wearable or implantable ECG devices for long-term health monitoring. It adopts a derivative-based patient adaptive threshold approach to detect the R peaks in the PQRST complex of ECG signals. Two tiny machine learning classifiers are used for the accurate classification of ACRs. A 3-layer feed-forward ternary neural network (TNN) is designed, which classifies the QRS complex's shape, followed by the adaptive decision logics (DL). The proposed processor requires only 1 KB on-chip memory to store the parameters and ECG data required by the classifiers. The ECG processor has been implemented based on fully-customized near-threshold logic cells using thick-gate transistors in 65-nm CMOS technology. The ASIC core occupies a die area of 1.08 mm. The measured total power consumption is 746 nW, with 0.8 V power supply at 2.5 kHz real-time operating clock. It can detect 13 abnormal cardiac rhythms with a sensitivity and specificity of 99.10% and 99.5%. The number of detectable ACR types far exceeds the other low power designs in the literature.
本文提出了一种超低功耗心电图（ECG）处理器应用专用集成电路（ASIC），用于实时检测异常心脏节律（ACR）。所提出的 ECG 处理器可支持可穿戴式或植入式 ECG 设备进行长期健康监测。它采用基于导数的患者自适应阈值方法来检测 ECG 信号的 PQRST 复合波中的 R 波峰。两个微小的机器学习分类器用于准确分类 ACR。设计了一个 3 层前馈三进制神经网络（TNN），用于对 QRS 复合波的形状进行分类，然后是自适应决策逻辑（DL）。该处理器仅需要 1KB 的片上内存来存储分类器所需的参数和 ECG 数据。该 ECG 处理器是基于全定制近阈值逻辑单元，使用 65nmCMOS 技术中的厚栅晶体管实现的。ASIC 核心占用 1.08mm 的芯片面积。测量的总功耗为 746nW，在 0.8V 电源和 2.5kHz 实时工作时钟下工作。它可以以 99.10%的灵敏度和 99.5%的特异性检测 13 种异常心脏节律。可检测的 ACR 类型数量远远超过文献中的其他低功耗设计。
