An ECG Delineation and Arrhythmia Classification System Using Slope Variation Measurement by Ternary Second-Order Delta Modulators for Wearable ECG Sensors
An ECG Delineation and Arrhythmia Classification System Using Slope Variation Measurement by Ternary Second-Order Delta Modulators for Wearable ECG Sensors
复制标题
使用三元二阶 Delta 调制器对可穿戴 ECG 传感器进行斜率变化测量的 ECG 描绘和心律失常分类系统
DOI：
10.1109/tbcas.2021.3113665
复制
发表时间：
2021
影响因子：
5.1
通讯作者：
Tang, Wei
中科院分区：
文献类型：
--
作者：
Tang, Xiaochen;Tang, Wei
本文介绍了一个心电图描记和心律失常分类系统。该系统由前端集成电路、在FPGA板上实现的描绘算法和心律失常分类算法组成。前端电路采用三值二阶Delta调制器测量输入模拟ECG信号的斜率变化。该电路将模拟输入转换成脉冲密度调制比特流，其脉冲密度与输入模拟信号的斜率变化成比例，而与瞬时幅度无关。前端芯片可以在3 ms的时序误差内检测到3.2 mV/ms的最小斜率变化。前端集成电路采用180 nm CMOS工艺制造，面积为0.25 mm，在1 kS/s采样速率下功耗为151 nW。基于从前端电路获得的斜率变化，描绘算法被设计用于检测ECG波形中的基准点。该算法在Spartan-6 FPGA上进行了测试。描绘系统可以检测QRS/PT波的间期、斜率和形态，并形成包含22个特征的特征集。基于这些特征，旋转线性核支持向量机（SVM）被应用于室性异位搏动（VEB），室上性异位搏动（SVEB），起源于窦房结的心跳的患者特异性心律失常分类。所提出的系统的性能与最近公布的方法相当，同时为未来可穿戴ECG监测系统的低复杂性实现提供了一个有前途的解决方案。
This paper presents a system for electrocardiogram (ECG) delineation and arrhythmia classification. The proposed system consists of a front-end integrated circuit, a delineation algorithm implemented on an FPGA board, and an arrhythmia classification algorithm. The front-end circuit applies a ternary second-order Delta modulator to measure the slope variation of the input analog ECG signal. The circuit converts the analog inputs into a pulse density modulated bitstream, whose pulse density is proportional to the slope variation of the input analog signal regardless of the instantaneous amplitude. The front-end chip can detect the minimum slope variation of 3.2 mV/mswithin a 3 ms timing error. The front-end integrated circuit was fabricated with a 180 nm CMOS process occupying a 0.25 mmarea with a 151 nW power consumption at the sampling rate of 1 kS/s. Based on the slope variation obtained from the front-end circuit, a delineation algorithm is designed to detect fiducial points in the ECG waveform. The delineation algorithm was tested on a Spartan-6 FPGA. The delineation system can detect the intervals, slopes, and morphology of the QRS/PT waves and form a feature set that contains 22 features. Based on these features, a rotate linear kernel support vector machine (SVM) is applied for patient-specific arrhythmia classification of the ventricular ectopic beat (VEB), supraventricular ectopic beat (SVEB), and heartbeats originating in sinus node. The performance of the proposed system is comparable to the recently published methods while providing a promising solution for the low-complexity implementation of future wearable ECG monitoring systems.