Noise sensitivity analysis of different ECG detection algorithms

Abstract

The electrocardiography is fully non-invasive, totally harmless and quick method for measuring the electrical activity of the heart. Computer based automatic recognition of electrocardiogram (ECG) characteristic points is necessary to help physicians for quick and easy diagnosis of cardiac conditions. Because of its specific shape, the QRS complex serves as an entry point for almost all automated ECG analysis algorithms. Despite large variety of existing QRS detection algorithms, large diversity of the QRS complex waveforms and the noise & artifacts accompanying the ECG signals make no single algorithm universally acceptable. Also, most recent algorithms are not tested for noise corrupted ECG signal. In this thes.is, the noise. sensitivities of different QRS detection algorithms are analyzed for ECG signal taken from MIT-BIH Arrhythmia database. The algorithms based on amplitude and first derivative (AFD), first derivative (FD), first and second derivative (FSD), FIR digital filter, IIR digital filter, neural network (NN) and wavelet transform (WT) are applied to the ECG corrupted with five different types of synthesized noise with different noise levels. The noise types are electromyographic (EMG) interference, 50 Hz power line interference, base line drift due to respiration, abrupt baseline shift and a composite noise constructed from the other noise types. The noise levels arc 25%, 50%, 75% and 100%. The origin of noises, their' characteristics and consequently their effects on QRS complex detection are discussed. The number of false positives & negatives and the percentage error rate of QRS complexes detected are calculated for different types 0 f noisy and noiseless E CG. None of the algorithms are able to detect all QRS complexes without any error for all of the noise types at the highest noise level. Algorithms based on digital filter (both FIR and IIR), NN and WT show very small deviation of error rate to power line and baseline drift noise up to maximum level. Algorithms based on AFD, FD and FSD are' insensitive to base line drift but sensitive to power line noise of higher level and order of derivati~e. Algorithms based on derivatives arc very sensitive to abrupt base line shift and EMG noise and conventional digital filter cannot eliminate these noises also. Algorithms based on NN and WT show better performance considering all noise types. Up to 100% of all noise, total 235 data sets (5,37,070 beats) are used to evaluate the performance. WT based algorithm gives mean percentage error rate of 7.76% while NN based algorithm gives 7.84%. The result of this study will help to develop a more robust ECG detector and this will make ECG interpretation system more effective.