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We propose an algorithm which can potentially perform multiple ECG processing tasks using a filter bank (FB). One set of filters in the FB decomposes the ECG into uniform frequency subbands. Since the subbands have a narrower bandwidth than the input ECG, they can be downsampled to get a lower rate subband signal. Time and frequency dependent processing can be performed at a lower rate and hence reduce the computation cost. A beat detection algorithm is presented which has minimal detection latency and good beat detection performance on the MIT/BIH database. The ECG can be enhanced by processing the subbands to remove noise. Features computed from the subbands can be used to distinguish between some ventricular and sinus beats. The FB offers a strategy to perform multiple tasks on the ECG using one set of filters operating at a computationally efficient rate.