BASELINE DRIFT REMOVAL USING WAVELET DECOMPOSITION

Baseline drift elimination is considered a classica l problem. In electrocardiography (ECG) signals, ba seline drift can influence the accurate diagnosis of heart disea se such as ischemia and arrhythmia. We present a wa velettransform(WT-) based search algorithm using the e n rgy of the signal in different scales to isolate baseline drift from the ECG signal. The algorithm computes w avelet decomposition and then in each scale the ene rgy of the signal is calculated. Keywords— Electrocardiography, Wavelet Decompositio n. Introduction In recent years computer aided ECG signal analysis has gained momentum and a tremendous amount of work has been carried out. One area of interest has been removing artifacts from data records [1, 2]. Artifacts are the noise induced to ECG signals that result from movements of electrodes. This in turn causes deformation and change in the electrical characteristics of the skin under and around the electrodes. These electrical changes appear in the ECG as motion artifacts and baseline drifts. Baseline wanders are considered as an artifact which produces inaccurate data when measuring the ECG parameters. The ST-segment measures are especially strongly affected by this wandering. In most of the ECG recordings the respiration, electrode impedance changes due to perspiration and increased body movements are the main causes of the baseline wandering [3]. Therefore, elimination of the baseline drifts can very much change the clinical information of the ECG signal. * Corresponding Author E. mail: abhishekchoubey84@gmail.com Related Work In [4], digital linear phase high-pass filter is us ed which decreases undesirable distortion of ECG signal. It requires a multi rate architecture havin g linear low pass filter working at low sampling rate with cut-off frequency of 0.9 Hz. But it requires large computational complexity. Another method for estimating the baseline drift is cubic spline fitting [5]. In this method lowfrequency noise superimposed on the baseline may be removed without affecting ST-segments. Rapid, simplified computations are accomplished using state-space techniques. This is a nonlinear method, and its performance is based on estimation of reference points in the PR intervals. The main disadvantage of this method is estimating reference points that may not belong to baseline. Some transform methods are also used for baseline drifts. In [6], Short time Fourier transform (STFT) used to detect the presence of baseline drift in EC G signal and use a time-varying filter. This method has two problems. First, STFT cannot have good time and frequency resolution at the same time. Second, it will have the same problems that are associated with filtering that is already mentioned above. Overview Electrocardiogram (ECG) Electrocardiogram, also known as EKG (from the German language) is a method of recording the electrical activity of the heart. Each heartbeat is caused by a section of the heart generating an electrical signal, which then conducts through specialized pathways to all parts of the heart. The se electrical signals also get transmitted through the chest to the skin where they can be recorded as a graph. [Choubey, 2(2): April-June, 2012] ISSN: 2277-5528 Int. J. of Engg. Sci. & Mgmt. (IJESM), Vol. 2, Issue 2: April-June: 2012, 151-153 Wavelets A wavelet is a wave-like oscillation with an amplitude that starts out at zero, increases, and t hen decreases back to zero. It can typically be visualized as a "brief oscillation" like one might see recorded by a seismograph or heart monitor. Generally, wavelets are purposefully crafted to have specific properties that make them useful for signal processing. Wavelets can be combined, using a "reverse, shift, multiply and sum" techniqu e called convolution, with portions of an unknown signal to extract information from the unknown signal. In other words wavelet is a mathematical function used to divide a given function or continuous-time signal into different scale components. Usually one can assign a frequency range to each scale component. Each scale component can then be studied with a resolution that matches its scale. A wavelet transform is the representation of a function by wavelets. Wavelet packet decomposition Wavelet packet decomposition is a wavelet transform where the signal is passed through more filters than the discrete wavelet transform (DWT).