Two-stage motion artefact reduction algorithm for electrocardiogram using weighted adaptive noise cancelling and recursive Hampel filter

The presence of motion artefacts in ECG signals can cause misleading interpretation of cardiovascular status. Recently, reducing the motion artefact from ECG signal has gained the interest of many researchers. Due to the overlapping nature of the motion artefact with the ECG signal, it is difficult to reduce motion artefact without distorting the original ECG signal. However, the application of an adaptive noise canceler has shown that it is effective in reducing motion artefacts if the appropriate noise reference that is correlated with the noise in the ECG signal is available. Unfortunately, the noise reference is not always correlated with motion artefact. Consequently, filtering with such a noise reference may lead to contaminating the ECG signal. In this paper, a two-stage filtering motion artefact reduction algorithm is proposed. In the algorithm, two methods are proposed, each of which works in one stage. The weighted adaptive noise filtering method (WAF) is proposed for the first stage. The acceleration derivative is used as motion artefact reference and the Pearson correlation coefficient between acceleration and ECG signal is used as a weighting factor. In the second stage, a recursive Hampel filter-based estimation method (RHFBE) is proposed for estimating the ECG signal segments, based on the spatial correlation of the ECG segment component that is obtained from successive ECG signals. Real-World dataset is used to evaluate the effectiveness of the proposed methods compared to the conventional adaptive filter. The results show a promising enhancement in terms of reducing motion artefacts from the ECG signals recorded by a cost-effective single lead ECG sensor during several activities of different subjects

Deep Learning using K-space Based Data Augmentation for Automated Cardiac MR Motion Artefact Detection

Quality assessment of medical images is essential for complete automation of image processing pipelines. For large population studies such as the UK Biobank, artefacts such as those caused by heart motion are problematic and manual identification is tedious and time-consuming. Therefore, there is an urgent need for automatic image quality assessment techniques. In this paper, we propose a method to automatically detect the presence of motion-related artefacts in cardiac magnetic resonance (CMR) images. As this is a highly imbalanced classification problem (due to the high number of good quality images compared to the low number of images with motion artefacts), we propose a novel k-space based training data augmentation approach in order to address this problem. Our method is based on 3D spatio-temporal Convolutional Neural Networks, and is able to detect 2D+time short axis images with motion artefacts in less than 1ms. We test our algorithm on a subset of the UK Biobank dataset consisting of 3465 CMR images and achieve not only high accuracy in detection of motion artefacts, but also high precision and recall. We compare our approach to a range of state-of-the-art quality assessment methods.Comment: Accepted for MICCAI2018 Conferenc

ECG Motion Artefact Reduction Improvements of a Chest-based Wireless Patient Monitoring System

Abstract An evaluation of motion artefact for a newly CE approved wireless bodyworn monitoring device is presented. This evaluation has shown that the system under test has greatly reduced motion artefact with comparison to an FDA-approved leaded system. Analysis of physiological data, such as quality of ECG signal, accuracy of recording of heart rate, temperature and ECG R-R interval has shown the system to offer high fidelity recordings and a robust service during a range of basic movements. Presented results have shown that the average difference in heart rate between the prototype and the reference device was 3.8bpm with standard deviation of 12.4bpm. Temperature analysis indicated the average difference between the prototype and the reference device was 5.66 o C, with standard deviation of 0.44 o C. R-R interval analysis highlighted mean interval difference as 78.96ms with standard deviation of 123.1ms. In general, the user activity of bending had highest errors due to the considerable torso movement

A Monte Carlo platform for the optical modeling of pulse oximetry

We investigated a custom Monte Carlo (MC) platform in the generation of opto-physiological models of motion artefact and perfusion in pulse oximetry. With the growing availability and accuracy of tissue optical properties in literatures, MC simulation of light-tissue interaction is providing increasingly valuable information for optical bio-monitoring research. Motion-induced artefact and loss of signal quality during low perfusion are currently the primary limitations in pulse oximetry. While most attempts to circumvent these issues have focused on signal post-processing techniques, we propose the development of improved opto-physiological models to include the characterisation of motion artefact and low perfusion. In this stage of the research, a custom MC platform is being developed for its use in determining the effects of perfusion, haemodynamics and tissue-probe optical coupling on transillumination at different positions of the human finger. The results of MC simulations indicate a useful and predictable output from the platform

Assessment of the feasibility of an ultra-low power, wireless digital patch for the continuous ambulatory monitoring of vital signs.

BACKGROUND AND OBJECTIVES: Vital signs are usually recorded at 4–8 h intervals in hospital patients, and deterioration between measurements can have serious consequences. The primary study objective was to assess agreement between a new ultra-low power, wireless and wearable surveillance system for continuous ambulatory monitoring of vital signs and a widely used clinical vital signs monitor. The secondary objective was to examine the system's ability to automatically identify and reject invalid physiological data. SETTING: Single hospital centre. PARTICIPANTS: Heart and respiratory rate were recorded over 2 h in 20 patients undergoing elective surgery and a second group of 41 patients with comorbid conditions, in the general ward. OUTCOME MEASURES: Primary outcome measures were limits of agreement and bias. The secondary outcome measure was proportion of data rejected. RESULTS: The digital patch provided reliable heart rate values in the majority of patients (about 80%) with normal sinus rhythm, and in the presence of abnormal ECG recordings (excluding aperiodic arrhythmias such as atrial fibrillation). The mean difference between systems was less than ±1 bpm in all patient groups studied. Although respiratory data were more frequently rejected as invalid because of the high sensitivity of impedance pneumography to motion artefacts, valid rates were reported for 50% of recordings with a mean difference of less than ±1 brpm compared with the bedside monitor. Correlation between systems was statistically significant (p<0.0001) for heart and respiratory rate, apart from respiratory rate in patients with atrial fibrillation (p=0.02). CONCLUSIONS: Overall agreement between digital patch and clinical monitor was satisfactory, as was the efficacy of the system for automatic rejection of invalid data. Wireless monitoring technologies, such as the one tested, may offer clinical value when implemented as part of wider hospital systems that integrate and support existing clinical protocols and workflows

Motion artefact detection in structured illumination microscopy for live cell imaging

The reconstruction process of structured illumination microscopy (SIM) creates substantial artefacts if the specimen has moved during the acquisition. This reduces the applicability of SIM for live cell imaging, because these artefacts cannot always be recognized as such in the final image. A movement is not necessarily visible in the raw data, due to the varying excitation patterns and the photon noise. We present a method to detect motion by extracting and comparing two independent 3D wide-field images out of the standard SIM raw data without needing additional images. Their difference reveals moving objects overlaid with noise, which are distinguished by a probability theory-based analysis. Our algorithm tags motion-artefacts in the final high-resolution image for the first time, preventing the end-user from misinterpreting the data. We show and explain different types of artefacts and demonstrate our algorithm on a living cell

Mapping of motion artefact in fMRI

Tato práce shrnuje princip magnetické rezonance a metodu funkční magnetické rezonance. Je zaměřena na projevy pohybových artefaktů ve fMRI datech a na metody předzpracování snímků, především jejich zarovnání. Zabývá se možností využití pohybových parametrů získaných při procesu zarovnání funkčních skenů k vytvoření map projevu pohybových artefaktů. V práci jsou navrženy, implementovány a testovány tři metody sloužící k vytvoření pravděpodobnostních, výkonových a statistických skupinových map ukazujících místa typicky postižená pohybovým artefaktem.This thesis summarizes a theory of magnetic resonance and the method of functional magnetic resonance. It is focused on the influence of motion artifacts and image preprocessing methods, especially realign. It deals with the possibility of using movement parameters obtained in the process of alignment of functional scans to create maps that show the expression of motion artifacts. In this thesis, three different methods were designed, implemented a tested. These methods lead to the creation of probability, power and statistical group maps showing areas typically affected by movement artifacts.