Adaptive motion artefact reduction in respiration and ECG signals for wearable healthcare monitoring systems

Wearable healthcare monitoring systems (WHMSs) have received significant interest from both academia and industry with the advantage of non-intrusive and ambulatory monitoring. The aim of this paper is to investigate the use of an adaptive filter to reduce motion artefact (MA) in physiological signals acquired by WHMSs. In our study, a WHMS is used to acquire ECG, respiration and triaxial accelerometer (ACC) signals during incremental treadmill and cycle ergometry exercises. With these signals, performances of adaptive MA cancellation are evaluated in both respiration and ECG signals. To achieve effective and robust MA cancellation, three axial outputs of the ACC are employed to estimate the MA by a bank of gradient adaptive Laguerre lattice (GALL) filter, and the outputs of the GALL filters are further combined with time-varying weights determined by a Kalman filter. The results show that for the respiratory signals, MA component can be reduced and signal quality can be improved effectively (the power ratio between the MA-corrupted respiratory signal and the adaptive filtered signal was 1.31 in running condition, and the corresponding signal quality was improved from 0.77 to 0.96). Combination of the GALL and Kalman filters can achieve robust MA cancellation without supervised selection of the reference axis from the ACC. For ECG, the MA component can also be reduced by adaptive filtering. The signal quality, however, could not be improved substantially just by the adaptive filter with the ACC outputs as the reference signals.Municipal Science & Technology Commission. Beijing Natural Science Foundation (Grants 3102028 and 3122034)General Logistics Science Foundation (Grant CWS11C108)National Institutes of Health (U.S.) (National Institute of General Medical Sciences (U.S.). Grant R01- EB001659)National Institutes of Health (U.S.) (National Institute for Biomedical Imaging and Bioengineering (U.S.) Cooperative Agreement U01- EB-008577

Estimation of respiratory rate from motion contaminated photoplethysmography signals incorporating accelerometry.

Estimation of respiratory rate (RR) from photoplethysmography (PPG) signals has important applications in the healthcare sector, from assisting doctors onwards to monitoring patients in their own homes. The problem is still very challenging, particularly during the motion for large segments of data, where results from different methods often do not agree. The authors aim to propose a new technique which performs motion reduction from PPG signals with the help of simultaneous acceleration signals where the PPG and accelerometer sensors need to be embedded in the same sensor unit. This method also reconstructs motion corrupted PPG signals in the Hilbert domain. An auto-regressive (AR) based technique has been used to estimate the RR from reconstructed PPGs. The proposed method has provided promising results for the estimation of RRs and their variations from PPG signals corrupted with motion artefact. The proposed platform is able to contribute to continuous in-hospital and home-based monitoring of patients using PPG signals under various conditions such as rest and motion states

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

An automated approach: from physiological signals classification to signal processing and analysis

By increased and widespread usage of wearable monitoring devices a huge volume of data is generated which requires various automated methods for analyzing and processing them and also extracting useful information from them. Since it is almost impossible for physicians and nurses to monitor physical activities of their patients for a long time, there is a need for automated data analysis techniques that abstract the information and highlight the significant events for clinicians and healthcare experts. The main objective of this thesis work was towards an automatic digital signal processing approach from physiological signal classification to processing and analyzing the two most vital physiological signals in long-term healthcare monitoring (ECG and IP). At the first stage, an automated generic physiological signal classifier for detecting an unknown recorded signal was introduced and then different algorithms for processing and analyzing the ECG and IP signals were developed and evaluated. This master thesis was a part of DISSE project which its aim was to design a new health-care system with the aim of providing medical expertise more accessible, affordable, and convenient. In this work, different publicly available databases such as MIT-BIH arrhythmia and CEBS were used in the development and evaluation phases. The proposed novel generic physiological signal classifier has the ability to distinguish five types of physiological signals (ECG, Resp, SCG, EMG and PPG) from each other with 100 % accuracy. Although the proposed classifier was not very successful in distinguishing lead I and II of ECG signal from each other (error of 27% was reported) which means that the general purpose features were enough discriminating to recognize different physiological signals from each other but not enough for classifying different ECG leads. For ECG processing and analysis section, three QRS detection methods were implemented which modified Pan-Tompkins gave the best performance with 97% sensitivity and 96,45% precision. The morphological based ectopic detection method resulted in sensitivity of 85,74% and specificity of 84,34%. Furthermore, for the first PVC detection algorithm (sum of trough) the optimal threshold and range were studied according to the AUC of ROC plot which the highest sensitivity and specificity were obtained with threshold of −5 and range of 11 : 25 that were equal to 87% and 82%, respectively. For the second PVC detection method (R-peak with minimum) the best performance was achieved with threshold of −0.7 that resulted in sensitivity of 68% and specificity of 72%. In the IP analysis section, an ACF approach was implemented for respiratory rate estimation. The estimated respira- tion rate obtained from IP signal and oronasal mask were compared and the total MAE and RMSE errors were computed that were equal to 0.40 cpm and 1.20 cpm, respectively. The implemented signal processing techniques and algorithms can be tested and improved with measured data from wearable devices for ambulatory applications

Continuous monitoring of vital parameters for clinically valid assessment of human health status

Tese de mestrado integrado, Engenharia Biomédica e Biofísica (Sinais e Imagens Médicas) Universidade de Lisboa, Faculdade de Ciências, 2019The lack of devices suitable for acquiring accurate and reliable measures of patients' physiolog-ical signals in a remote and continuous manner together with the advances in data acquisition technol-ogies during the last decades, have led to the emergence of wearable devices for healthcare. Wearable devices enable remote, continuous and long-term health monitoring in unattended setting. In this con-text, the Swiss Federal Laboratories for Material Science and Technology (Empa) developed a wearable system for long-term electrocardiogram measurements, referred to as textile belt. It consists of a chest strap with two embroidered textile electrodes. The validity of Empa’s system for electrocardiogram monitoring has been proven in a clinical setting. This work aimed to assess the validity of the textile belt for electrocardiogram monitoring in a home setting and to supplement the existing system with sensors for respiratory monitoring. Another objective was to evaluate the suitability of the same weara-ble, as a multi-sensor system, for activity monitoring. A study involving 12 patients (10 males and 2 females, interquartile range for age of 48–59 years and for body mass indexes of 28.0–35.5 kg.m-2) with suspected sleep apnoea was carried out. Overnight electrocardiogram was measured in a total of 28 nights. The quality of recorded signals was assessed using signal-to-noise ratio, artefacts detection and Poincaré plots. Study data were compared to data from the same subjects, acquired in the clinical setting. For respiratory monitoring, optical fibre-based sensors of different geometries were integrated into the textile belt. Signal processing algorithms for breathing rate and tidal volume estimation based on respiratory signals acquired by the sensors were developed. Pilot studies were conducted to compare the different approaches for respiratory monitoring. The quality of respiratory signals was determined based on signal segments “sinusoidality”, evaluated through the calculation of the cross-correlation between signal segments and segment-specific reference waves. A method for accelerometry-based lying position recognition was proposed, and the proof of concept of activity intensity classification through the combination of subjects’ inertial acceleration, heart rate and breathing rate data, was presented. Finally, a study with three participants (1 male and 2 females, aged 21 ± 2 years, body mass index of 20.3 ± 1.5 kg.m-2) was conducted to assess the validity of the textile belt for respiratory and activity monitoring. Electrocardiogram signals acquired by the textile belt in the home setting were found to have better quality than the data acquired by the same device in the clinical setting. Although a higher artefact percentage was found for the textile belt, signal-to-noise ratio of electrocardiogram signals recorded by the textile belt in the home setting was similar to that of signals acquired by the gel electrodes in the clinical setting. A good agreement was found between the RR-intervals derived from signals recorded in home and clinical settings. Besides, for artefact percentages greater than 3%, visual assessment of Poincaré plots proved to be effective for the determination of the primary source of artefacts (noise or ectopic beats). Acceleration data allowed posture recognition (i.e. lying or standing/sitting, lying position) with an accuracy of 91% and positive predictive value of 80%. Lastly, preliminary results of physical activity intensity classification yielded high accuracy, showing the potential of the proposed method. The textile belt proved to be appropriate for long-term, remote and continuous monitoring of subjects’ physical and physiological parameters. It can monitor not only electrocardiogram, but also breathing rate, body posture and physical activity intensity, having the potential to be used as tool for disease prediction and diagnose support.Contexto: A falta de dispositivos adequados para a monitorização de sinais fisiológicos de um modo remoto e contínuo, juntamente com avanços tecnológicos na área de aquisição de dados nas últimas décadas, levaram ao surgimento de wearable devices, i.e. dispositivos vestíveis, no sector da saúde. Wearable devices possibilitam a monitorização do estado de saúde, de uma forma remota, contínua e de longa duração. Quando feito em ambiente domiciliar, este tipo de monitorização (i.e. contínua, remota e de longa duração) tem várias vantagens: diminui a pressão posta sobre o sistema de saúde, reduz despesas associadas ao internamento e acelera a resposta a emergências, permitindo deteção precoce e prevenção de condições crónicas. Neste contexto, a Empa, Laboratórios Federais Suíços de Ciência e Tecnologia de Materiais, desenvolveu um sistema vestível para a monitorização de eletrocardiograma de longa duração. Este sistema consiste num cinto peitoral com dois elétrodos têxteis integrados. Os elétrodos têxteis são feitos de fio de polietileno tereftalato revestido com prata e uma ultrafina camada de titânio no topo. De modo a garantir a aquisição de sinais de alta qualidade, o cinto tem nele integrado um reservatório de água que liberta vapor de água para humidificar os elétrodos. Este reservatório per-mite a monitorização contínua de eletrocardiograma por 5 a 10 dias, sem necessitar de recarga. A vali-dade do cinto para a monitorização de eletrocardiograma em ambiente clínico já foi provada. Objetivo: Este trabalho teve por objetivo avaliar a validade do cinto para a monitorização de eletrocar-diograma em ambiente domiciliar e complementar o sistema existente com sensores para monitorização respiratória. Um outro objetivo foi analisar a adequação do cinto, como um sistema multisensor, para monitorização da atividade física. Métodos: Um estudo com 12 pacientes com suspeita de apneia do sono (10 homens e 2 mulheres, am-plitude interquartil de 48–59 anos para a idade e de 28.0–35.5 kg.m-2 para o índice de massa corporal) foi conduzido para avaliar a qualidade do sinal de eletrocardiograma medido em ambiente domiciliar. O sinal de eletrocardiograma dos pacientes foi monitorizado continuamente, num total de 28 noites. A qualidade dos sinais adquiridos foi analisada através do cálculo da razão sinal-ruído; da deteção de ar-tefactos, i.e., intervalos RR com um valor inviável de um ponto de vista fisiológico; e de gráficos de Poincaré, um método de análise não linear da distribuição dos intervalos RR registados. Os dados ad-quiridos neste estudo foram comparados com dados dos mesmos pacientes, adquiridos em ambiente hospitalar. Para a monitorização respiratória, sensores feitos de fibra óptica foram integrados no cinto. Al-gorítmicos para a estimar a frequência respiratória e o volume corrente dos sujeitos tendo por base o sinal medido pelas fibras ópticas foram desenvolvidos neste trabalho. As diferentes abordagens foram comparadas através de estudos piloto. Diferentes métodos para avaliação da qualidade do sinal adquirido foram sugeridos. Um método de reconhecimento da postura corporal através do cálculo de ângulos de orientação com base na aceleração medida foi proposto. A prova de conceito da determinação da intensidade da atividade física pela combinação de informações relativas á aceleração inercial e frequências cardíaca e respiratória dos sujeitos, é também apresentada neste trabalho. Um estudo foi conduzido para avaliar a validade do cinto para monitorização da respiração e da atividade física. O estudo contou com 10 parti-cipantes, dos quais 3 vestiram o cinto para monitorização da respiração (1 homem e 2 mulheres, idade 21 ± 2 anos, índice de massa corporal 20.3 ± 1.5 kg.m-2). Resultados: O estudo feito com pacientes com suspeita de apneia do sono revelou que os sinais eletro-cardiográficos adquiridos pelo cinto em ambiente domiciliar foram de melhor qualidade que os sinais adquiridos pelo mesmo dispositivo em ambiente hospitalar. Uma percentagem de artefacto de 2.87% ±4.14% foi observada para os dados adquiridos pelos elétrodos comummente usados em ambiente hospi-talar, 7.49% ± 10.76% para os dados adquiridos pelo cinto em ambiente domiciliar e 9.66% ± 14.65% para os dados adquiridos pelo cinto em ambiente hospitalar. Embora tenham tido uma maior percenta-gem de artefacto, a razão sinal-ruído dos sinais eletrocardiográficos adquiridos pelo cinto em ambiente domiciliar foi semelhante á dos sinais adquiridos pelos elétrodos de gel em ambiente hospitalar. Resul-tados sugerem uma boa concordância entre os intervalos RR calculados com base nos eletrocardiogra-mas registados em ambientes hospitalar e domiciliar. Além disso, para sinais com percentagem de arte-facto superior a 3%, a avaliação visual dos gráficos de Poincaré provou ser um bom método para a determinação da fonte primária de artefactos (batimentos irregulares ou ruído). A monitorização da aceleração dos sujeitos permitiu o reconhecimento da postura corporal (isto é, deitado ou sentado/em pé) com uma exatidão de 91% e valor preditivo positivo de 80%. Por fim, a classificação da intensidade da atividade física baseado na aceleração inercial e frequências cardíaca e respiratória revelou elevada exatidão, mostrando o potencial desta técnica. Conclusão: O cinto desenvolvido pela Empa provou ser apropriado para monitorização de longa-dura-ção de variáveis físicas e fisiológicos, de uma forma remota e contínua. O cinto permite não só monito-rizar eletrocardiograma, mas também frequência respiratória, postura corporal e intensidade da atividade física. Outros estudos devem ser conduzidos para corroborar os resultados e conclusões deste trabalho. Outros sensores poderão ser integrados no cinto de modo a possibilitar a monitorização de outras vari-áveis fisiológicas de relevância clínica. Este sistema tem o potencial de ser usado como uma ferramenta para predição de doenças e apoio ao diagnóstico

A multi-channel opto-electronic sensor to accurately monitor heart rate against motion artefact during exercise

This study presents the use of a multi-channel opto-electronic sensor (OEPS) to effectively monitor critical physiological parameters whilst preventing motion artefact as increasingly demanded by personal healthcare. The aim of this work was to study how to capture the heart rate (HR) efficiently through a well-constructed OEPS and a 3-axis accelerometer with wireless communication. A protocol was designed to incorporate sitting, standing, walking, running and cycling. The datasets collected from these activities were processed to elaborate sport physiological effects. t-test, Bland-Altman Agreement (BAA), and correlation to evaluate the performance of the OEPS were used against Polar and Mio-Alpha HR monitors. No differences in the HR were found between OEPS, and either Polar or Mio-Alpha (both p > 0.05); a strong correlation was found between Polar and OEPS (r: 0.96, p < 0.001); the bias of BAA 0.85 bpm, the standard deviation (SD) 9.20 bpm, and the limits of agreement (LOA) from −17.18 bpm to +18.88 bpm. For the Mio-Alpha and OEPS, a strong correlation was found (r: 0.96, p < 0.001); the bias of BAA 1.63 bpm, SD 8.62 bpm, LOA from −15.27 bpm to +18.58 bpm. These results demonstrate the OEPS to be capable of carrying out real time and remote monitoring of heart rate

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

The 2023 wearable photoplethysmography roadmap

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

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

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