论文标题
事件驱动的心律失常检测的事件驱动的压缩神经形态系统
An Event-Driven Compressive Neuromorphic System for Cardiac Arrhythmia Detection
论文作者
论文摘要
已开发了可穿戴心电图（ECG）记录和加工系统，以检测心律不齐，以帮助预防心脏病发作。但是，常规的可穿戴系统在电路和系统水平上都遭受高能量消耗。为了克服设计挑战，本文提出了一个事件驱动的压缩ECG记录和心律不齐检测的神经形态处理系统。所提出的系统通过系统级别的基于基于Spike的信息表示，实现了低功耗和高心律失常检测精度。在记录系统中利用事件驱动的级别跨ADC（LC-ADC），该系统利用ECG信号的稀疏性在静音信号周期内启用压缩记录并节省ADC能量。同时，基于拟议的尖峰卷积神经网络（SCNN）的神经形态心律失常检测方法与基于尖峰的LC-ADC输出固有地兼容，因此在系统水平上实现了准确的检测和低能量消耗。仿真结果表明，与MIT-BIH数据集中的Nyquist采样相比，具有5位LC-ADC的拟议系统可实现88.6 \％的采样数据点的减少，而SCNN的93.59 \％心律失常检测准确性，证明了LC-ADC的压缩能力以及SCN cons con scn的有效性。
Wearable electrocardiograph (ECG) recording and processing systems have been developed to detect cardiac arrhythmia to help prevent heart attacks. Conventional wearable systems, however, suffer from high energy consumption at both circuit and system levels. To overcome the design challenges, this paper proposes an event-driven compressive ECG recording and neuromorphic processing system for cardiac arrhythmia detection. The proposed system achieves low power consumption and high arrhythmia detection accuracy via system level co-design with spike-based information representation. Event-driven level-crossing ADC (LC-ADC) is exploited in the recording system, which utilizes the sparsity of ECG signal to enable compressive recording and save ADC energy during the silent signal period. Meanwhile, the proposed spiking convolutional neural network (SCNN) based neuromorphic arrhythmia detection method is inherently compatible with the spike-based output of LC-ADC, hence realizing accurate detection and low energy consumption at system level. Simulation results show that the proposed system with 5-bit LC-ADC achieves 88.6\% reduction of sampled data points compared with Nyquist sampling in the MIT-BIH dataset, and 93.59\% arrhythmia detection accuracy with SCNN, demonstrating the compression ability of LC-ADC and the effectiveness of system level co-design with SCNN.
