Izadi Vahi, Shahri Pouria Karimi, Ahani Hamed
UNC Charlotte, Charlotte, NC USA.
Biomed Eng Lett. 2020 Feb 6;10(2):299-307. doi: 10.1007/s13534-020-00148-7. eCollection 2020 May.
Electrocardiogram (ECG) data compression has numerous applications. The time for generating compressed samples is a vital factor when we consider ambulatory devices, with the fact that data should be sent to the physician as soon as possible. In addition, there are some wearable ECG recorders that have limited power, and may only be capable of doing simple algorithms. With the aim of increasing the speed and simplicity of the compressors, we propose a system architecture that can generate compressed ECG samples, in a linear method and with CR 75%. We used sparsity of the ECG signal and proposed a system based on compressed sensing (CS) that can compress ECG samples, almost in real-time. We applied CS in a very small size in order to accelerate the compression phase and accordingly reducing the power consumption. Also, in the recovery phase, we used the recently developed Kronecker technique to improve the quality of the recovered signal. The system designed based on full-adder/subtractor (FAS) and shift registers, without using any external processor or any training algorithm.
心电图（ECG）数据压缩有许多应用。在考虑便携式设备时，生成压缩样本的时间是一个至关重要的因素，因为数据应尽快发送给医生。此外，一些可穿戴式心电图记录器功率有限，可能只能执行简单的算法。为了提高压缩器的速度和简易性，我们提出了一种系统架构，该架构能够以线性方式生成压缩的心电图样本，压缩率为75%。我们利用心电图信号的稀疏性，提出了一种基于压缩感知（CS）的系统，该系统几乎可以实时压缩心电图样本。我们以非常小的规模应用CS，以加速压缩阶段并相应降低功耗。此外，在恢复阶段，我们使用了最近开发的克罗内克技术来提高恢复信号的质量。该系统基于全加器/减法器（FAS）和移位寄存器设计，无需使用任何外部处理器或任何训练算法。
