REDUCTION OF SKIN STRETCH INDUCED MOTION ARTIFACTS IN ELECTROCARDIOGRAM MONITORING USING ADAPTIVE FILTERING

Cardiovascular disease (CVD) is the leading cause of death in many regions worldwide, accounting for nearly one third of global deaths in 2001. Wearable electrocardiographic cardiovascular monitoring devices have contributed to reduce CVD mortality and cost by enabling the diagnosis of conditions with infrequent symptoms, the timely detection of critical signs that can be precursor to sudden cardiac death, and the long-term assessment/monitoring of symptoms, risk factors, and the effects of therapy. However, the effectiveness of ambulatory electrocardiography to improve the treatment of CVD can be significantly impaired by motion artifacts which can cause misdiagnoses, inappropriate treatment decisions, and trigger false alarms. Skin stretch associated with patient motion is a main source of motion artifact in current ECG monitors. A promising approach to reduce motion artifact is the use of adaptive filtering that utilizes a measured reference input correlated with the motion artifact to extract noise from the ECG signal. Previous attempts to apply adaptive filtering to electrocardiography have employed either electrode deformation or acceleration, body acceleration, or skin/electrode impedance as a reference input, and were not successful at reducing motion artifacts in a consistent and reproducible manner. This has been essentially attributed to the lack of correlation between the reference input selected and the induced noise. In this study, motion artifacts are adaptively filtered by using skin strain as the reference signal. Skin strain is measured non-invasively using a light emitting diode (LED) and an optical sensor incorporated in an ECG electrode. The optical strain sensor is calibrated on animal skin samples and finally in-vivo, in terms of sensitivity and measurement range. Skin stretch induced artifacts are extracted in-vivo using adaptive filters. The system and method are tested for different individuals and under various types of ambulatory conditions with the noise reduction performance quantified

Liikeartefaktat elektrokardiografiassa

Movement of the patient during electrocardiograph (ECG) recording is a severe source of artifacts. Recent technical developments have enabled ECG recording without continuous supervision by experts. However, ECG recording outside of hospitals is prone to poor quality and movement artifacts. Therefore, it is important to study how and how much ECG recordings are affected by movement. Movement artifacts can hide signal components or mimic them, which causes false negative or false positive detections. Methods to manage movement artifacts include both computational and non-computational approaches. Computational approaches include, for example, adaptive filtering and machine learning methods. Additional variables that correlate with the artifact sources can be utilized in artifact recognition. For example, acceleration, impedance, and pressure signals have been studied as possible movement references. These additional signals are recorded by sensors that are placed on the ECG electrodes or on the patient’s body. In this thesis, the effect of movement artifacts is quantified using a simulation. The simulation makes use of open ECG databases. This study investigates how automated ECG analysis is affected by incremental increase in the movement artifact level. According to the results QRS detection statistics worsen with increased artifact levels. Capturing a movement reference for ECG is studied by experimental research. ECG and inertial measurement unit signals were recorded during different movements in order to analyze the creation of movement artifacts and movement reference signals. According to the results, placement of the movement reference signal sensor has a significant effect on the results. Different movements are captured better by different sensors and affect different ECG leads with different strengths.Potilaan liike sydänsähkökäyrämittauksen (EKG) aikana on merkittävä artefaktien lähde. Viimeaikainen teknologinen kehitys on mahdollistanut EKG-mittauksen ilman asiantuntijoiden jatkuvaa valvontaa. EKG-mittaukset sairaalaolosuhteiden ulkopuolella ovat kuitenkin erityisen alttiita huonolle signaalilaadulle ja liikeartefaktoille. Tämän vuoksi on tärkeää tutkia, miten ja kuinka paljon liike vaikuttaa EKG-mittauksiin. Liikeartefaktat voivat joko peittää tai jäljitellä EKG-signaalin eri osia, aiheuttaen vääriä negatiivisia tai vääriä positiivisia havaintoja. Liikeartefaktojen vaikutusta voidaan vähentää sekä laskennallisten että muiden menetelmien avulla. Laskennallisia menetelmiä ovat esimerkiksi adaptiivinen suodatus ja koneoppimismenetelmät. Artefaktojen lähteen kanssa korreloivia muuttujia mittaamalla voidaan edistää artefaktojen tunnistusta EKG-signaalista. Esimerkiksi kiihtyvyys-, impedanssi- ja painesignaalien käyttöä liikereferensseinä on tutkittu. Kyseisiä referenssisignaaleja voidaan mitata EKG-elektrodeihin tai potilaan kehoon kiinnitettävillä sensoreilla. Liikeartefaktojen vaikutuksen suuruutta tutkitaan tässä työssä simulaation avulla. Simulaatiossa hyödynnetään avoimia EKG-tietokantoja. Tutkimuksessa tarkastellaan sitä, miten vähittäinen liikeartefaktatason kasvu vaikuttaa automaattiseen EKG-analyysiin. Tulosten mukaan QRS-detektioon liittyvät tilastot huononevat artefaktatason kasvaessa. Liikereferenssin luomista tarkastellaan kokeellisen tutkimuksen avulla. EKG- ja inertiamittausyksikkö-signaaleja mitattiin erilaisten liikkeiden aikana, jotta voitaisiin havainnoida liikeartefaktojen ja liikesignaalin syntymistä. Tulosten mukaan liikereferenssiä mittaavan sensorin sijoituspaikalla on merkittävä vaikutus tuloksiin. Tietyt liikkeet saadaan paremmin mitattua eri tavoin sijoitettujen sensorien avulla. Lisäksi liikkeet vaikuttavat eri vahvuuksilla eri EKG-kytkentöihin

Motion artifact reduction of electrocardiograms using multiple motion sensors

An electrocardiogram (ECG) is a measurement of the electrical signal produced by the heart as it beats. This is a signal very commonly used by medical professionals, as it gives an indication of an individual’s heart rate and can further be used to detect specific abnormalities within the heart. There are a number of sources of noise that can corrupt the ECG signal, the most problematic being that of motion artifacts. As an individual wearing a surface ECG moves, their movements will add noise to the signal. This noise is particularly difficult to remove, as it will change depending on the movements of the user and will often fall in the same spectrum as the ECG signal itself. The effectiveness of the adaptive filtering method in reducing motion artifacts is investigated using multiple motion sensors on key locations of the body and by combining the motion data through the use of various blind source separation methods. An adaptive filter is a filter that can use a reference signal in order to readjust itself to a constantly changing noise signal and is commonly used to clean ECG signals. The adaptive filter uses noise estimations based on the reference signal as well as previous noise estimations in order to continually clean the noisy signal. Since motion artifacts are based directly off the movements of the user, collected motion data will be directly correlated with the noise being introduced to the ECG, and can therefore be used in the adaptive filter to produce a desirable ECG signal

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

Signal quality assessment of a novel ecg electrode for motion artifact reduction

Background: The presence of noise is problematic in the analysis and interpretation of the ECG, especially in ambulatory monitoring. Restricting the analysis to high-quality signal segments only comes with the risk of excluding significant arrhythmia episodes. Therefore, the development of novel electrode technology, robust to noise, continues to be warranted. Methods: The signal quality of a novel wet ECG electrode (Piotrode) is assessed and compared to a commercially available, commonly used electrode (Ambu). The assessment involves indices of QRS detection and atrial fibrillation detection performance, as well as signal quality indices (ensemble standard deviation and time–frequency repeatability), computed from ECGs recorded simultaneously from 20 healthy subjects performing everyday activities. Results: The QRS detection performance using the Piotrode was considerably better than when using the Ambu, especially for running but also for lighter activities. The two signal quality indices demonstrated similar trends: the gap in quality became increasingly larger as the subjects became increasingly more active. Conclusions: The novel wet ECG electrode produces signals with less motion artifacts, thereby offering the potential to reduce the review burden, and accordingly the cost, associated with ambulatory monitoring

Dynamic Study of Flexible Sensors to Reduce Motion Artifacts

The field of wearable electronics is changing healthcare and increasing possibilities for human-machine interfaces. Soft electronics stretch with the skin to monitor long-term heart rate trends or direct the motion of smart prosthetics. The capabilities are only as good as the signal quality. A significant challenge for these devices is that by their very definition – wearable – these flexible sensors suffer from motion artifacts not previously found when measured in a stationary setting. This thesis investigates three significant sources of motion artifacts for flexible sensors: relative motion between sensor and signal source, the unique challenges of skin strain, and change in contact impedance. Relative motion is not a unique problem for wearable electronics. Still, human tissue's elastic nature means that most body-mounted sensors undergo more relative motion than on a comparable rigid machine. Device design and placement are analyzed to reduce the movement between the sensor and signal source. Dynamic effects of jogging are numerically simulated for a chest-mounted device showing a small form factor, and lightweight designs reduce device motion. Human skin is an unstable platform to mount devices. Skin strain causes device movement and changes the biopotential during measurement. Experimental examples show material and design solutions to increase adhesion, reduce strain within the device, and maintain breathability for long-term recordings. Flexible sensors measuring biopotential are susceptible to changes in contact impedance. Skin strain and vibrations create motion artifacts that can mimic or disrupt many biosignals, making them hard to filter out. A prototype device is presented that uses a strain isolating layer to reduce skin strain at the electrode, which stabilizes contact impedance and reduces motion artifacts. Experimental data from the device compensating for these three sources of motion artifacts is presented for quantitative comparison.M.S

Graphene textile smart clothing for wearable cardiac monitoring

Wearable electronics is a rapidly growing field that recently started to introduce successful commercial products into the consumer electronics market. Employment of biopotential signals in wearable systems as either biofeedbacks or control commands are expected to revolutionize many technologies including point of care health monitoring systems, rehabilitation devices, human–computer/machine interfaces (HCI/HMIs), and brain–computer interfaces (BCIs). Since electrodes are regarded as a decisive part of such products, they have been studied for almost a decade now, resulting in the emergence of textile electrodes. This study reports on the synthesis and application of graphene nanotextiles for the development of wearable electrocardiography (ECG) sensors for personalized health monitoring applications. In this study, we show for the first time that the electrocardiogram was successfully obtained with graphene textiles placed on a single arm. The use of only one elastic armband, and an “all-textile-approach” facilitates seamless heart monitoring with maximum comfort to the wearer. The functionality of graphene textiles produced using dip coating and stencil printing techniques has been demonstrated by the non-invasive measurement of ECG signals, up to 98% excellent correlation with conventional pre-gelled, wet, silver/silver-chloride (Ag / AgCl) electrodes. Heart rate have been successfully determined with ECG signals obtained in different situations. The system-level integration and holistic design approach presented here will be effective for developing the latest technology in wearable heart monitoring devices