ECG beat detection using filter banks. Afonso, V., X.; Tompkins, W., J.; Nguyen, T., Q.; and Luo, S. IEEE transactions on bio-medical engineering, 46(2):192-202, 2, 1999.
ECG beat detection using filter banks. [pdf]Paper  abstract   bibtex   
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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 keywords = {Algorithms,Databases, Factual,Electrocardiography,Electrocardiography: instrumentation,Electrocardiography: methods,Electrocardiography: statistics & numerical data,Equipment Design,Heart Rate,Humans,Sensitivity and Specificity,Signal Processing, Computer-Assisted,Signal Processing, Computer-Assisted: instrumentat,Time Factors},
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 abstract = {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.},
 bibtype = {article},
 author = {Afonso, V X and Tompkins, W J and Nguyen, T Q and Luo, S},
 journal = {IEEE transactions on bio-medical engineering},
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