DWT based beat rate detection in ECG analysis

Abstract

Electrocardiogram (ECG) classification systems have the potential to benefit from the inclusion of the automated measurement capabilities. The first stage in computerized processing of the ECG is beat detection. The accuracy of the beat detector is very important for the overall system performance hence there is a benefit in improving the accuracy. In present study we introduce the concept of Discrete Wavelet Transform which is suitable for the non stationary ECG signals as it has adequate scale values and shifting in time. As baseline Wander and different types of noise elimination are considered as classical problem in ECG analysis we present a wavelet based search algorithm in different scales for Denoising and subtractive procedure to isolate baseline wander from noisy ECG signal(to remove the noise). This algorithm is tested using the data record from MIT-BIH database and excellent results are obtained.

Selection of Optimal Parameters for ECG Signal Smoothing and Baseline Drift Removal

“…We classify the methods based on wavelets into several groups and list several typical representatives:  The multi-resolution framework and thresholding strategy (Karel et al, 2005), (Cornelia and Romulus, 2005)  The orthogonal filter banks (Saritha et al, 2008)  The usage of low pass and high pass filters (Abdel-Rahman and Daqrquq, 2010)  Subtractive procedures to isolate the baseline wander (Frau and Eck, 2000), (Joshi and Ghule, 2010).…”

“…The main advantage of this kind of detection is less time consuming analysis allowing applicability on on-line long lasting ECG signals. It has been also shown that the concept of DWT is suitable for the non-stationary ECG signals (Cornelia and Romulus, 2005), (Saritha et al, 2008), (Joshi and Ghule, 2010). Additionally, the multi-resolution framework gives wavelets a very powerful compression and filtering tool.…”

“…Joshi proposed a wavelet based search algorithm in different scales for denoising and subtractive procedure to isolate the baseline wander from the noisy ECG signal. This algorithm is tested using the data records from the MIT-BIH database and promising results have been obtained (Joshi and Ghule, 2010). They utilised the concept of DWT and claim that the proposed concept is suitable for the non-stationary ECG signal as it has adequate scale values and shifting in time.…”

“…Authors claim that the concept is also suitable for the non-stationary ECG signals. Particular attention was given to baseline wander and different types of noise eliminations as authors consider that as a main problem in correct ECG analysis (Joshi and Ghule, 2010). Attention to the choice of particular wavelets has also been given consideration in the literature in order to identify the best wavelet for ECG signal preprocessing (Kaur and Singh, 2011), (Stantic and Jo, 2012).…”