ECG beat detection using filter banks
ECG beat detection using filter banks
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DOI：
10.1109/10.740882
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发表时间：
1999-02-01
影响因子：
4.6
通讯作者：
Luo, S
中科院分区：
文献类型：
--
作者：
Afonso, VX;Tompkins, WJ;Luo, S
设计了一种多采样率数字信号处理算法，用于检测心电图（ECG）中的心跳信号。该算法采用滤波器组（FB），将ECG信号分解为具有均匀带宽的子带。基于FB的算法能够对信号进行独立的时频分析。从一组子带和启发式检测策略计算的特征用于融合来自多个单通道节拍检测算法的决策。整体心跳检测算法对MIT/BIH数据库的灵敏度为99.59%，正预测率为99.56%。此外，这是一种实时算法，因为其心跳检测延迟最小。基于FB的搏动检测算法还固有地适合于计算上高效的结构，因为检测逻辑以子带速率操作。基于FB的结构对于使用一组预处理滤波器来执行多个ECG处理任务潜在地有用。
We have designed a multirate digital signal processing algorithm to detect heartbeats in the electrocardiogram (ECG), The algorithm incorporates a filter bank (FB) which decomposes the ECG into subbands with uniform frequency bandwidths, The FB-based algorithm enables independent time and frequency analysis to be performed on a signal. Features computed from a set of the subbands and a heuristic detection strategy are used to fuse decisions from multiple one-channel beat detection algorithms. The overall beat, detection algorithm has a sensitivity of 99.59% and a positive predictivity of 99.56% against the MIT/BIH database. Furthermore this is a real-time algorithm since its beat detection latency Is minimal. The FB-based beat detection algorithm also inherently lends itself to a computationally efficient structure since the detection logic operates at the subband rate, The FB-based structure is potentially useful for performing multiple ECG processing tasks using one set of preprocessing filters.