An Automatic Method to Reduce Baseline Wander and Motion Artifacts on Ambulatory Electrocardiogram Signals

Today’s wearable medical devices are becoming popular because of their price and ease of use. Most wearable medical devices allow users to continuously collect and check their health data, such as electrocardiograms (ECG). Therefore, many of these devices have been used to monitor patients with potential heart pathology as they perform their daily activities. However, one major challenge of collecting heart data using mobile ECG is baseline wander and motion artifacts created by the patient’s daily activities, resulting in false diagnoses. This paper proposes a new algorithm that automatically removes the baseline wander and suppresses most motion artifacts in mobile ECG recordings. This algorithm clearly shows a significant improvement compared to the conventional noise removal method. Two signal quality metrics are used to compare a reference ECG with its noisy version: correlation coefficients and mean squared error. For both metrics, the experimental results demonstrate that the noisy signal filtered by our algorithm is improved by a factor of ten.

Removal of ECG Baseline Wander Using a Resting Cycle Template

The development of electrocardiogram (ECG) wearable devices has increased due to its applications on ambulatory patients. ECG signals provide useful information about the heart behavior, but when daily activities are monitored, motion artifacts are introduced producing saturation of the signal, thus losing the information. The typical resolution used to record ECG signals is of maximum 16-bit, which might not be enough to detect low-amplitude potentials and at the same time avoid saturation due to baseline wander, since this last issue demands a low-gain signal chain. A high-resolution provides a more detailed ECG signal under a low gain input, and if the signal is corrupted by motion artifact noise but is not saturated, it can be filtered to recover the signal of interest. In this work, a 24-bit ADC is used to record the ECG, and a new method, the rest ECG cycle template, is proposed to remove the baseline wander. This new method is compared to high-pass filter and spline interpolation methods in their ability to remove baseline wander. This new method presumes that a user is able to establish a rest ECG during his/her daily activities.

Comparison of ECG Baseline Wander Removal Techniques and Improvement Based on Moving Average of Wavelet Approximation Coefficients

The baseline wander is among the artifacts that corrupt the ECG signal. This noise can affect some signal features, in particular the ST segment, which is an important marker for the diagnosis of ischemia. This paper presents a study on the effectiveness of several methods and techniques for suppressing the baseline wonder (BW) from the ECG signals. As a result, a new technique called moving average of wavelet approximation coefficients (DWT-MAV) is proposed. The techniques concerned are the moving average, the approximation of the baseline by polynomial fitting, the Savitzky-Golay filtering, and the discrete wavelet transform (DWT). The comparison of this techniques is performed using the main criteria for assessing the BW denoising quality criteria such mean square error (MSE), percent root mean square difference (PRD) and correlation coefficient (COR). In this paper, three other criteria of comparison are proposed namely the number of samples of the ECG signal, the baseline frequency variation and the time processing. Two of these new indices are related to possible real time ECG denoising. To improve the quality of BW suppression including the new indices, a new method is proposed. This technique is a combination of the DWT and the moving average methods. This new technique performs the best compromise in terms of MSE, PRD, coefficient correlation and the time processing. The simulations were performed on ECG recording from MIT-BIH database with synthetic and real baselines.

ECG Noise Removal Using Modified Distributed Arithmetic Based Finite Impulse Response Filter

ECG monitoring is essential to support human life. During signal acquisition, the signals are contaminated by various noises that occur due to different sources. This paper focuses on Baseline wander and Muscle Artifact noise removal using Distributed Arithmetic (DA) based FIR filters. An area-efficient modified DA based FIR filter consists of LUT-less structure and used for noise removal. The performance of the modified DA based FIR filter is compared with the conventional DA FIR filter. An arbitrary real-time ECG record is taken from MIT-BIH database and Baseline Wander noise, Muscle artifact noises are taken from MIT-BIH noise stress test database. The performance of both filters is evaluated in terms of output Signal to Noise Ratio (SNR) and Mean Square Error (MSE). For Baseline wander noise removal, the modified DA based FIR filter produces high output SNR and also low MSE of 76.6% than the conventional filter. Similarly, for Muscle Artifact noise removal, it produces high SNR, and MSE is reduced to 73.8%. A modified DA based FIR filter is synthesized for the target FPGA device Spartan3E XC3s2000-4fg900 and hardware resource utilization is presented.