Key result
A filter bank-based multirate digital signal processing algorithm achieved a beat detection sensitivity of 99.59% and a positive predictivity of 99.56% against the MIT/BIH database.
A novel filter bank-based ECG beat detection algorithm demonstrates high sensitivity and positive predictivity with minimal latency, suitable for real-time processing.
May support real-time ECG monitoring; leaves open prospective clinical validation.
We have designed a multirate digital signal processing algorithm to detect heart beats 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.
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Afonso et al. (1999) studied ECG beat detection. Filter bank-based multirate digital signal processing algorithm vs. MIT/BIH database benchmark was evaluated on Beat detection sensitivity and positive predictivity. A filter bank-based multirate digital signal processing algorithm achieved a beat detection sensitivity of 99.59% and a positive predictivity of 99.56% against the MIT/BIH database.
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