No 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.

No abstract

With an introduction to a filter bank based approach here are two predominant types of noise T that contaminate the electrocardiogram (ECG) acquired during a stress test: the baseline wander noise (BW) and electrode motion artifact, and electromyogram-induced noise (EMG) [ 11. BW noise is at a lower frequency, caused by respiration and motion of the subject or the leads. The frequency components of BW noise are usually below 0.5 Hz, and extend into the frequency range of the ST segment during a stress test. EMG noise, on the other hand, is predominantly at higher frequencies, caused by increased muscle activity and by mechanical forces acting on the electrodes. The frequency spectrum of the EMG noise overlaps that of the ECG signal and extends even higher in the frequency domain. In this article, we review some of the published ECG enhancing techniques to overcome the noise problems, and compare their performance on stress ECG signals under adverse noise scenarios. We also describe the Filter Bank (FB) based ECG enhancing algorithm [9]. Figure 1 shows a noise-free ECG beat with ST-segment depression induced by exercise (top) and various epochs of this ECG with different noise conditions, as during a stress test. It is important to measure the dynamic changes in the morphology of segments of the ECG induced by the exercise, even in the presence of noise. OverviewMany ECG enhancing techniques to address the noise problem have been reported in the literature. In Ref.[2] a combination of mean and median algorithms is used on the filtered ECG. Reference [3] presents a BW noise removal filter which meets specifications in Ref.[41 and a timevarying filter to remove high-frequency EMG noise. Reference [5] provides a technique which subtracts the current heart-beat average to get a 'QRS-free' signal, estimates the BW from the downsampled QRS-free signal, and then subtracts the estimated BW from the noisy ECG. Reference [6] uses a source consistency filtering technique that seeks to develop a transfer function of the cardiac dipole. In Ref. [7], an adaptive baseline wander filter is designed as a cascade of two adaptive filters. Reference [8] uses a cubic spline technique to estimate and then subtract the BW in the ECG.The cubic spline method works well

No abstract