An Adaptive Learning Algorithm for ECG Noise and Baseline Drift Removal

Abstract

Abstract. Electrical noise and power line interference may alter ECG morphology. Noise reduction in ECG is accomplished applying filtering techniques. However, such filtering may mutate the original wave making difficult the interpretation of pathologies. To overcome this problem an adaptive neural method able to filter ECGs without causing the loss of important information is proposed. The method has been tested on a set of 110 ECGs segments from the European ST-T database and compared with a recent morphological filtering technique. Results showed that morphological filters cause inversions and alterations of the original signal in 65 over 110 ECGs, while the neural method does not. In 96% of the cases the signal processed by the network is coherent with the original one within a coherence value of 0.92, whereas this values for the morphological filter is 0.70. Moreover, the adaptability of the neural method does not require estimating appropriate filter parameters for each ECG segments.

Machine Learning: A Crucial Tool for Sensor Design

“…A conventional baseline removal process for spectral or time series data consists of three major steps [11]: (1) to determine baseline key points in spectrum, (2) to build a baseline model for the whole spectrum using the detected baseline points, and (3) to correct the signal by subtracting the baseline from original signal. Recently, some new algorithms have been developed, such as an adaptive learning algorithm for electrocardiogram (ECG) baseline removal[12] and a selective filter for ECG baseline correction [13]. …”

Adaptive Interference Cancellation of ECG Signals

“…Based on the classical least mean square (LMS) algorithm [ 10 ], two improved adaptive algorithms are proposed, namely, the normalized LMS (NLMS) algorithm [ 11 ] based on symbol function and the normalized block-processing LMS (BLMS) algorithm based on symbol function [ 12 ]. The symbolic functions [ 13 ] and the block-processing [ 14 ] concept are introduced and applied to the the elimination of two kinds of interference: ECG signal frequency interference and BW interference.…”