54 research outputs found

    Perustason vaellush äiri on v ähentäminen elektrokardiografiassa Kalman-suotimilla

    Get PDF
    Developments in sensor technology have enabled the continuous electrocardiography monitoring during daily activities. These recordings can be valuable in the detection of arrhythmias and abnormal cardiac cycles that occur only under certain circumstances or infrequently. Unfortunately, the activities of the patient cause severe motion artifacts to the ECG signal that affect the signal quality and complicate the signal interpretation. The motion based baseline wander artifact can be reduced to a certain point by improving the stability of the electrode-skin interface. However, also computational signal processing methods, like adaptive filtering, are needed. The signal processing methods can be improved by utilizing additional variables that correlate with the artifact sources. For example, acceleration and impedance signals have been studied as possible references of motion. However, being able to do the measurements without additional sensors would enable the measurement device to be simpler, lighter, and lower in cost. This thesis presents an accelerometer-free ECG signal baseline wander reduction algorithm that uses electromyography signal as a Kalman filter reference signal. The EMG signal is extracted from the ECG signal itself and used as an estimate of local electrode motion. The motion estimate is then used as a reference signal for an adaptive Kalman filter baseline wander compensation algorithm. The algorithm is evaluated on data collected in clinical trials. In addition, the feasibility of removing the baseline wander using a reduced number of accelerometers as a motion reference for Kalman filter is studied. The results showed that the proposed method removed baseline wander successfully and without significant alterations in the signal morphology. The method proved to be at least equally proficient with the methods it was compared to. The results suggested that the baseline wander reduction from ambulatory ECG measurements could be achieved without additional sensors using EMG signal as a motion reference for the Kalman filter. In addition, also the reduced number of accelerometers proved to be a feasible source of the motion reference signal.Sensoriteknologian kehitys on mahdollistanut sydänsähkökäyrän jatkuvan mittaamisen päivittäisten aktiviteettien aikana. Jatkuvat mittaukset voivat auttaa havaitsemaan sellaisia rytmihäiriöitä ja epänormaaleja sydämen toimintakiertoja, jotka esiintyvät vain tietyissä olosuhteissa tai epäsäännöllisesti. Potilaan liikkeet kuitenkin aiheuttavat sydänsähkökäyrään voimakkaita liikeartefakteja, jotka heikentävät signaalin laatua ja vaikeuttavat signaalin tulkintaa. Liikkeestä aiheutuvaa perustason vaellushäiriötä voidaan hieman vähentää parantamalla ihon ja elektrodin välisen rajapinnan vakautta. Kuitenkin myös laskennallisia signaalinkäsittelymenetelmiä kuten adaptiivisia suotimia tarvitaan. Signaalinkäsittelymenetelmiä voidaan tehostaa hyödyntämällä lisämittaussuureita, jotka korreloivat artefaktien lähteen kanssa. Esimerkiksi kiihtyvyys- ja impedanssisignaaleja on tutkittu mahdollisina liikereferensseinä. Tässä diplomityössä ehdotetaan perustason vaellushäiriön vähentämiseen sydänsähkökäyrästä menetelmää, joka ei hyödynnä lisäsensoreita, vaan käyttää lihassähkökäyrää Kalman-suotimen liike-estimaattina. Lihassähkökäyrä erotetaan sydänsähkökäyrästä ja sitä käytetään estimaattina elektrodien paikallisesta liikkeestä. Liike-estimaattia puolestaan hyödynnetään adaptiiviseen Kalman-suotimeen perustuvan perustason vaellushäiriön kompensaatioalgoritmin referenssisignaalina. Algoritmi arvioidaan kliinisissä kokeissa kerätyllä datalla. Lisäksi tutkitaan Kalman-suotimen toimivuutta käytettäessä pienempää määrää kiihtyvyysantureita liike-estimaatin lähteenä. Tulokset osoittivat, että ehdotettu menetelmä poisti onnistuneesti perustason vaellushäiriön muuttamatta signaalin muotoa merkittävästi. Ehdotettu menetelmä osoittautui toimivan vähintään yhtä hyvin kuin menetelmät, joihin sitä verrattiin. Tulosten mukaan perustason vaellushäiriön vähentäminen liikkeen aikaisista sydänsähkökäyrämittauksista olisi mahdollista ilman lisäsensoreita käyttämällä lihassähkökäyrää Kalman-suotimen liikereferenssinä. Lisäksi, vähennetty määrä kiihtyvyysantureita osoittautui myös toimivaksi liike-estimaatin lähteeksi

    Motion artefact removal in electroencephalography and electrocardiography by using multichannel inertial measurement units and adaptive filtering

    Get PDF
    From Wiley via Jisc Publications RouterHistory: received 2021-01-22, rev-recd 2021-05-14, accepted 2021-05-28, pub-electronic 2021-06-24Article version: VoRPublication status: PublishedFunder: Engineering and Physical Sciences Research Council; Id: http://dx.doi.org/10.13039/501100000266; Grant(s): EP/S020179/1, EP/P02713X/1Abstract: This paper presents a new active electrode design for electroencephalogram (EEG) and electrocardiogram (ECG) sensors based on inertial measurement units to remove motion artefacts during signal acquisition. Rather than measuring motion data from a single source for the entire recording unit, inertial measurement units are attached to each individual EEG or ECG electrode to collect local movement data. This data is then used to remove the motion artefact by using normalised least mean square adaptive filtering. Results show that the proposed active electrode design can reduce motion contamination from EEG and ECG signals in chest movement and head swinging motion scenarios. However, it is found that the performance varies, necessitating the need for the algorithm to be paired with more sophisticated signal processing to identify scenarios where it is beneficial in terms of improving signal quality. The new instrumentation hardware allows data driven artefact removal to be performed, providing a new data driven approach compared to widely used blind‐source separation methods, and helps enable in the wild EEG recordings to be performed

    Body sensor network for in-home personal healthcare

    Get PDF
    A body sensor network solution for personal healthcare under an indoor environment is developed. The system is capable of logging the physiological signals of human beings, tracking the orientations of human body, and monitoring the environmental attributes, which covers all necessary information for the personal healthcare in an indoor environment. The major three chapters of this dissertation contain three subsystems in this work, each corresponding to one subsystem: BioLogger, PAMS and CosNet. Each chapter covers the background and motivation of the subsystem, the related theory, the hardware/software design, and the evaluation of the prototype’s performance

    Multidimensional embedded MEMS motion detectors for wearable mechanocardiography and 4D medical imaging

    Get PDF
    Background: Cardiovascular diseases are the number one cause of death. Of these deaths, almost 80% are due to coronary artery disease (CAD) and cerebrovascular disease. Multidimensional microelectromechanical systems (MEMS) sensors allow measuring the mechanical movement of the heart muscle offering an entirely new and innovative solution to evaluate cardiac rhythm and function. Recent advances in miniaturized motion sensors present an exciting opportunity to study novel device-driven and functional motion detection systems in the areas of both cardiac monitoring and biomedical imaging, for example, in computed tomography (CT) and positron emission tomography (PET). Methods: This Ph.D. work describes a new cardiac motion detection paradigm and measurement technology based on multimodal measuring tools — by tracking the heart’s kinetic activity using micro-sized MEMS sensors — and novel computational approaches — by deploying signal processing and machine learning techniques—for detecting cardiac pathological disorders. In particular, this study focuses on the capability of joint gyrocardiography (GCG) and seismocardiography (SCG) techniques that constitute the mechanocardiography (MCG) concept representing the mechanical characteristics of the cardiac precordial surface vibrations. Results: Experimental analyses showed that integrating multisource sensory data resulted in precise estimation of heart rate with an accuracy of 99% (healthy, n=29), detection of heart arrhythmia (n=435) with an accuracy of 95-97%, ischemic disease indication with approximately 75% accuracy (n=22), as well as significantly improved quality of four-dimensional (4D) cardiac PET images by eliminating motion related inaccuracies using MEMS dual gating approach. Tissue Doppler imaging (TDI) analysis of GCG (healthy, n=9) showed promising results for measuring the cardiac timing intervals and myocardial deformation changes. Conclusion: The findings of this study demonstrate clinical potential of MEMS motion sensors in cardiology that may facilitate in time diagnosis of cardiac abnormalities. Multidimensional MCG can effectively contribute to detecting atrial fibrillation (AFib), myocardial infarction (MI), and CAD. Additionally, MEMS motion sensing improves the reliability and quality of cardiac PET imaging.Moniulotteisten sulautettujen MEMS-liiketunnistimien käyttö sydänkardiografiassa sekä lääketieteellisessä 4D-kuvantamisessa Tausta: Sydän- ja verisuonitaudit ovat yleisin kuolinsyy. Näistä kuolemantapauksista lähes 80% johtuu sepelvaltimotaudista (CAD) ja aivoverenkierron häiriöistä. Moniulotteiset mikroelektromekaaniset järjestelmät (MEMS) mahdollistavat sydänlihaksen mekaanisen liikkeen mittaamisen, mikä puolestaan tarjoaa täysin uudenlaisen ja innovatiivisen ratkaisun sydämen rytmin ja toiminnan arvioimiseksi. Viimeaikaiset teknologiset edistysaskeleet mahdollistavat uusien pienikokoisten liiketunnistusjärjestelmien käyttämisen sydämen toiminnan tutkimuksessa sekä lääketieteellisen kuvantamisen, kuten esimerkiksi tietokonetomografian (CT) ja positroniemissiotomografian (PET), tarkkuuden parantamisessa. Menetelmät: Tämä väitöskirjatyö esittelee uuden sydämen kineettisen toiminnan mittaustekniikan, joka pohjautuu MEMS-anturien käyttöön. Uudet laskennalliset lähestymistavat, jotka perustuvat signaalinkäsittelyyn ja koneoppimiseen, mahdollistavat sydämen patologisten häiriöiden havaitsemisen MEMS-antureista saatavista signaaleista. Tässä tutkimuksessa keskitytään erityisesti mekanokardiografiaan (MCG), joihin kuuluvat gyrokardiografia (GCG) ja seismokardiografia (SCG). Näiden tekniikoiden avulla voidaan mitata kardiorespiratorisen järjestelmän mekaanisia ominaisuuksia. Tulokset: Kokeelliset analyysit osoittivat, että integroimalla usean sensorin dataa voidaan mitata syketiheyttä 99% (terveillä n=29) tarkkuudella, havaita sydämen rytmihäiriöt (n=435) 95-97%, tarkkuudella, sekä havaita iskeeminen sairaus noin 75% tarkkuudella (n=22). Lisäksi MEMS-kaksoistahdistuksen avulla voidaan parantaa sydämen 4D PET-kuvan laatua, kun liikeepätarkkuudet voidaan eliminoida paremmin. Doppler-kuvantamisessa (TDI, Tissue Doppler Imaging) GCG-analyysi (terveillä, n=9) osoitti lupaavia tuloksia sydänsykkeen ajoituksen ja intervallien sekä sydänlihasmuutosten mittaamisessa. Päätelmä: Tämän tutkimuksen tulokset osoittavat, että kardiologisilla MEMS-liikeantureilla on kliinistä potentiaalia sydämen toiminnallisten poikkeavuuksien diagnostisoinnissa. Moniuloitteinen MCG voi edistää eteisvärinän (AFib), sydäninfarktin (MI) ja CAD:n havaitsemista. Lisäksi MEMS-liiketunnistus parantaa sydämen PET-kuvantamisen luotettavuutta ja laatua

    Deep Learning Algorithms for Time Series Analysis of Cardiovascular Monitoring Systems

    Get PDF
    This thesis investigates and develops methods to enable ubiquitous monitoring of the most examined cardiovascular signs, blood pressure, and heart rate. Their continuous measurement can help improve health outcomes, such as the detection of hypertension, heart attack, or stroke, which are the leading causes of death and disability. Recent research into wearable blood pressure monitors sought predominately to utilise a hypothesised relationship with pulse transit time, relying on quasiperiodic pulse event extractions from photoplethysmography local signal characteristics and often used only a fraction of typically bivariate time series. This limitation has been addressed in this thesis by developing methods to acquire and utilise fused multivariate time series without the need for manual feature engineering by leveraging recent advances in data science and deep learning methods that showed great data analysis potential in other domains

    Advanced Signal Processing in Wearable Sensors for Health Monitoring

    Get PDF
    Smart, wearables devices on a miniature scale are becoming increasingly widely available, typically in the form of smart watches and other connected devices. Consequently, devices to assist in measurements such as electroencephalography (EEG), electrocardiogram (ECG), electromyography (EMG), blood pressure (BP), photoplethysmography (PPG), heart rhythm, respiration rate, apnoea, and motion detection are becoming more available, and play a significant role in healthcare monitoring. The industry is placing great emphasis on making these devices and technologies available on smart devices such as phones and watches. Such measurements are clinically and scientifically useful for real-time monitoring, long-term care, and diagnosis and therapeutic techniques. However, a pertaining issue is that recorded data are usually noisy, contain many artefacts, and are affected by external factors such as movements and physical conditions. In order to obtain accurate and meaningful indicators, the signal has to be processed and conditioned such that the measurements are accurate and free from noise and disturbances. In this context, many researchers have utilized recent technological advances in wearable sensors and signal processing to develop smart and accurate wearable devices for clinical applications. The processing and analysis of physiological signals is a key issue for these smart wearable devices. Consequently, ongoing work in this field of study includes research on filtration, quality checking, signal transformation and decomposition, feature extraction and, most recently, machine learning-based methods

    Sensing and Signal Processing in Smart Healthcare

    Get PDF
    In the last decade, we have witnessed the rapid development of electronic technologies that are transforming our daily lives. Such technologies are often integrated with various sensors that facilitate the collection of human motion and physiological data and are equipped with wireless communication modules such as Bluetooth, radio frequency identification, and near-field communication. In smart healthcare applications, designing ergonomic and intuitive human–computer interfaces is crucial because a system that is not easy to use will create a huge obstacle to adoption and may significantly reduce the efficacy of the solution. Signal and data processing is another important consideration in smart healthcare applications because it must ensure high accuracy with a high level of confidence in order for the applications to be useful for clinicians in making diagnosis and treatment decisions. This Special Issue is a collection of 10 articles selected from a total of 26 contributions. These contributions span the areas of signal processing and smart healthcare systems mostly contributed by authors from Europe, including Italy, Spain, France, Portugal, Romania, Sweden, and Netherlands. Authors from China, Korea, Taiwan, Indonesia, and Ecuador are also included

    The 2023 wearable photoplethysmography roadmap

    Get PDF
    Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology

    Wearable and Nearable Biosensors and Systems for Healthcare

    Get PDF
    Biosensors and systems in the form of wearables and “nearables” (i.e., everyday sensorized objects with transmitting capabilities such as smartphones) are rapidly evolving for use in healthcare. Unlike conventional approaches, these technologies can enable seamless or on-demand physiological monitoring, anytime and anywhere. Such monitoring can help transform healthcare from the current reactive, one-size-fits-all, hospital-centered approach into a future proactive, personalized, decentralized structure. Wearable and nearable biosensors and systems have been made possible through integrated innovations in sensor design, electronics, data transmission, power management, and signal processing. Although much progress has been made in this field, many open challenges for the scientific community remain, especially for those applications requiring high accuracy. This book contains the 12 papers that constituted a recent Special Issue of Sensors sharing the same title. The aim of the initiative was to provide a collection of state-of-the-art investigations on wearables and nearables, in order to stimulate technological advances and the use of the technology to benefit healthcare. The topics covered by the book offer both depth and breadth pertaining to wearable and nearable technology. They include new biosensors and data transmission techniques, studies on accelerometers, signal processing, and cardiovascular monitoring, clinical applications, and validation of commercial devices

    Applications Of Wearable Sensors In Delivering Biologically Relevant Signals

    Get PDF
    With continued advancements in wearable technologies, the applications for their use are growing. Wearable sensors can be found in smart watches, fitness trackers, and even our cellphones. The common applications in everyday life are usually step counting, activity tracking, and heart rate monitoring. However, researchers have developed ways to use these similar sensors for clinically relevant diagnostic measures, as well as, improved athletic training and performance. Two areas of interest for the use of wearable sensors are mental health diagnostics in children and heart rate monitoring during intense physical activity from new locations, which are discussed further in this thesis. About 20% of children will experience an anxiety or depressive disorder. These disorders, if left untreated, can lead to comorbidity, substance abuse, and even suicide. Current methods for diagnosis are time consuming and only offered to those most at risk (i.e., reported or referred by a teacher, doctor, or parent). For the children that do get referred to a specialist, the process is often inaccurate. Researchers began using mood induction task to observe behavioral responses to specific stimuli in hopes to improve the accuracy of diagnostics. However, these methods involve long hours of training and watching videos of the activities. Recently, a few studies have focused on using wearable sensors during mood induction tasks in hopes to pick up on relevant movements to distinguish those with and without an internalizing disorder. The first study presented in this thesis focuses on using wearable inertial measurement units during the ‘Bubbles’ mood induction task. A decision tree was developed to identify children with internalizing disorders, accuracy of this model was 71% based on leave-one-subject-out cross validation. The second study focuses on estimating heart rate using wearable photoplethysmography sensors at multiple body locations. Heart rate is an important vital sign used across a variety of contexts. For example, athletes use heart rate to determine whether they are hitting their desired heart rate zones during training and doctors can use heart rate as an early indicator of disease. With the advancements made in wearables, photoplethysmography can now be used to collect signals from devices anywhere on the body. However, estimating heart rate accurately during periods of intense physical activity remains a challenge due to signal corruption cause by motion artifacts. This study focuses on evaluating algorithms for accurately estimating heart rate from photoplethysmograms and determining the optimal body location for wear. A phase vocoder and Wiener filtering approach was used to estimate heart rate from the forearm, shank, and sacrum. The algorithm estimated heart rate to within 6.2 6.9, and 6.7 beats per minute average absolute error for the forearm, shank, and sacrum, respectively, across a wide variety of physical activities selected to induce varying levels of motion artifact
    corecore