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

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8169
Author(s):  
Hongzu Li ◽  
Pierre Boulanger

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.

2021 ◽  
Vol 10 (1) ◽  
pp. 45
Author(s):  
Eladio Altamira-Colado ◽  
Miguel Bravo-Zanoguera ◽  
Daniel Cuevas-González ◽  
Marco Reyna-Carranza ◽  
Roberto López-Avitia

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.


2015 ◽  
Vol 44 (8) ◽  
pp. 1483-1493 ◽  
Author(s):  
Young-Jin Moon ◽  
Jeongpyo Park ◽  
Mingyu Jeong ◽  
Sang-Hyun Kim ◽  
Jin-Gyu Kang ◽  
...  

2021 ◽  
Vol 19 ◽  
Author(s):  
Jen Sze Ong ◽  
Shuet Nee Wong ◽  
Alina Arulsamy ◽  
Jessica L. Watterson ◽  
Mohd. Farooq Shaikh

: Epilepsy is a devastating neurological disorder that affects nearly 70 million people worldwide. Epilepsy causes uncontrollable, unprovoked and unpredictable seizures that reduces the quality of life of those afflicted, with 1-9 epileptic patient deaths per 1000 patient occurring annually due to sudden unexpected death in epilepsy (SUDEP). Predicting the onset of seizures and managing them may help patients from harming themselves and may improve their well-being. For a long time, electroencephalography (EEG) devices have been the mainstay for seizure detection and monitoring. This systematic review aimed to elucidate and critically evaluate the latest advancements of medical devices, besides EEG, that have been proposed for the management and prediction of epileptic seizures. A literature search was performed on three databases; PubMed, Scopus and EMBASE. Following title/abstract screening by two independent reviewers, 27 articles were selected for critical analysis in this review. These articles revealed ambulatory, non-invasive and wearable medical devices such as the in-ear EEG devices, the accelerometer-based devices and the subcutaneous implanted EEG devices might be more acceptable than traditional EEG systems. In addition, extracerebral signal-based devices may be more efficient than EEG-based systems, especially when combined with an intervention trigger. Although further studies may still be required to improve and validate these proposed systems before commercialization, these findings may give hope to epileptic patients, particularly those with refractory epilepsy, to predict and manage their seizures. The use of medical devices for epilepsy may improve patients' independence and quality of life and possibly prevent sudden unexpected death in epilepsy (SUDEP).


Author(s):  
Younghyun Kim ◽  
Woosuk Lee ◽  
Anand Raghunathan ◽  
Vijay Raghunathan ◽  
Niraj K. Jha

Author(s):  
Dimitrios I. Fotiadis ◽  
Constantine Glaros ◽  
Aristidis Likas

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