ECG beat detection using filter banks

TLDR

The filter‑bank structure can support multiple ECG processing tasks using a single set of preprocessing filters. The study aims to design a multirate digital signal processing algorithm for detecting heartbeats in electrocardiograms. The algorithm decomposes the ECG into uniform‑bandwidth subbands with a filter bank, extracts features from each subband, fuses decisions from several one‑channel detectors, and operates at the subband rate for computational efficiency. Against the MIT/BIH database, the algorithm achieves 99.59 % sensitivity and 99.56 % positive predictivity, and its minimal beat‑detection latency makes it real‑time.

Abstract

The authors 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.