Unobtrusive Monitoring of Heart Rate and Respiration Rate during Sleep

Sleep deprivation has various adverse psychological and physiological effects. The effects range from decreased vigilance causing an increased risk of e.g. traffic accidents to a decreased immune response causing an increased risk of falling ill. Prevalence of the most common sleep disorder, insomnia can be, depending on the study, as high as 30 % in adult population. Physiological information measured unobtrusively during sleep can be used to assess the quantity and the quality of sleep by detecting sleeping patterns and possible sleep disorders. The parameters derived from the signals measured with unobtrusive sensors may include all or some of the following: heartbeat intervals, respiration cycle lengths, and movements. The information can be used in wellness applications that include self-monitoring of the sleep quality or it can also be used for the screening of sleep disorders and in following-up of the effect of a medical treatment. Unobtrusive sensors do not cause excessive discomfort or inconvenience to the user and are thus suitable for long-term monitoring. Even though the monitoring itself does not solve the sleeping problems, it can encourage the users to pay more attention on their sleep. While unobtrusive sensors are convenient to use, their common drawback is that the quality of the signals they produce is not as good as with conventional measurement methods. Movement artifacts, for example, can make the detection of the heartbeat intervals and respiration impossible. The accuracy and the availability of the physiological information extracted from the signals however depend on the measurement principle and the signal analysis methods used. Three different measurement systems were constructed in the studies included in the thesis and signal processing methods were developed for detecting heartbeat intervals and respiration cycle lengths from the measured signals. The performance of the measurement systems and the signal analysis methods were evaluated separately for each system with healthy young adult subjects. The detection of physiological information with the three systems was based on the measurement of ballistocardiographic and respiration movement signals with force sensors placed under the bedposts, the measurement of electrocardiographic (ECG) signal with textile electrodes attached to the bed sheet, and the measurement of the ECG signal with non-contact capacitive electrodes. Combining the information produced by different measurement methods for improving the detection performance was also tested. From the evaluated methods, the most accurate heartbeat interval information was obtained with contact electrodes attached to the bed sheet. The same method also provided the highest heart rate detection coverage. This monitoring method, however, has a limitation that it requires a naked upper body, which is not necessarily acceptable for everyone. For respiration cycle length detection, better results were achieved by using signals recorded with force sensors placed under a bedpost than when extracting the respiration information from the ECG signal recorded with textile bed sheet electrodes. From the data quality point of view, an ideal night-time physiological monitoring system would include a contact ECG measurement for the heart rate monitoring and force sensors for the respiration monitoring. The force sensor signals could also be used for movement detection

Vastasyntyneen ja imeväisikäisen vauvan unenaikaisen hengitys- ja syketaajuuden tarkkailu puettavalla liikeanturilla

Vastasyntyneelle ja imeväisikäiselle nukkuminen on elintärkeä toiminto, ja se on välttämätöntä aivoverkkojen kehitykselle. Tiedetään, että huono unenlaatu aiheuttaa pitkällä tähtäimellä muun muassa kasvun hidastumista ja käyttäytymisongelmia. Imeväisikäisillä melko yleisesti esiintyvät unihäiriöt, kuten yöheräily ja nukahtamisvaikeudet aiheuttavat merkittävää rasitusta ja huolta vanhemmille. Objektiivisen mittausmenetelmän puutteen vuoksi ei ole kuitenkaan voitu selvittää imeväisikäisen unen kehittymistä kotiolosuhteissa. Tässä tutkimuksessa tarkasteltiin puettaviin pöksyihin kiinnitetyn liikeanturin ja EKG-kangaselektrodien soveltuvuutta vastasyntyneiden ja imeväisikäisten vauvojen unenaikaisen hengityksen ja sykkeen tarkkailuun. Tutkimuksen ensimmäisessä vaiheessa päiväaikaisten uni-EEG-tutkimuksien yhteydessä verrattiin liikeanturin mittauskanavien rekisteröimiä mittauskäyriä pietsoanturilla varustettuun hengitysvyöhön. Saatujen tutkimustuloksien perusteella liikeanturin gyroskooppi osoittautui tarkimmaksi hengitystaajuutta mittaavaksi parametriksi, kun taas anturin välittämä EKG-signaali oli tulkintakelpoisin osin luotettavaa. Tutkimuksen toisessa vaiheessa vauvaperheille annettiin unipöksyt ja älypuhelimet kotiin arvioidaksemme yön yli kestävää kotikäyttöä. Tutkimustulokset viittaavat siihen, että eri unitilojen tunnistaminen hengityksen vaihtelusta olisi todennäköisesti mahdollista gyroskooppisignaalista. Vanhemmilta saadun palautteen perusteella unipöksyjä pidettiin käytännöllisinä ja helppokäyttöisinä. Tulevissa tutkimuksissa tulisi keskittyä liikeanturin validointiin kliinisesti hyväksyttyjen mittausparametrien avulla, jotta algoritmeja voisi opettaa tunnistamaan eri uni-valve rytmejä automaattisesti. Näin puettava liikeanturi voisi tarjota tietoa vauvan luonnollisen unirakenteen kehittymisestä pitkällä aikavälillä. Lisäksi anturin kliininen validointi voisi mahdollistaa imeväisikäisten kardiorespiratoristen ongelmien ja liikehäiriöiden diagnostisen lisätyökalun kehittämisen.Sleep is one of the most vital functions of newborns and infants, and it is essential for neuronal network development. Therefore, long-term sleep disturbances have been associated with growth delays and behavioral disorders. Commonly reported infant sleep disturbances, such as night awakenings and difficulties falling asleep, cause distress to parents. Yet, the development of infant sleep in the home environment has not been fully elucidated due to lack of objective measurement parameters. In the current study, we assessed the feasibility of a motion sensor, attached to wearable pants, and ECG textile electrodes to monitor sleep-related respiration and heart rate of newborns and infants. First, we compared signals recorded by the motion sensor’s measurement channels to the standard respiratory piezo effort belt’s signal during daytime EEG recordings. According to our results, the motion sensor’s gyroscope proved to measure respiratory rate most accurately, while the ECG signal transmitted by the sensor was reliable in interpretable sections. We then provided wearable garments and smartphones to families with infants to assess overnight home-use. Our results indicate that different sleep states could likely be identified based on respiration fluctuation visible in the gyroscope’s signals. Moreover, the wearable system was considered practical and easy to use by the parents. Future studies should focus on validating the sensor with clinically approved measures, in order to train the algorithms to automatically identify different sleep-wake states. By doing so, the wearable sensor could provide information on natural infant sleep structure development over long time periods. Additionally, clinical validation of the sensor may result in the development of a companion diagnostic tool for infant cardiorespiratory and movement disorders

Guest Editorial Cardiovascular Health Informatics: Risk Screening and Intervention

Despite enormous efforts to prevent cardiovascular disease (CVD) in the past, it remains the leading cause of death in most countries worldwide. Around two-thirds of these deaths are due to acute events, which frequently occur suddenly and are often fatal beforemedical care can be given. New strategies for screening and early intervening CVD, in addition to the conventional methods, are therefore needed in order to provide personalized and pervasive healthcare. In this special issue, selected emerging technologies in health informatics for screening and intervening CVDs are reported. These papers include reviews or original contributions on 1) new potential genetic biomarkers for screening CVD outcomes and high-throughput techniques for mining genomic data; 2) new imaging techniques for obtaining faster and higher resolution images of cardiovascular imaging biomarkers such as the cardiac chambers and atherosclerotic plaques in coronary arteries, as well as possible automatic segmentation, identification, or fusion algorithms; 3) new physiological biomarkers and novel wearable and home healthcare technologies for monitoring them in daily lives; 4) new personalized prediction models of plaque formation and progression or CVD outcomes; and 5) quantifiable indices and wearable systems to measure them for early intervention of CVD through lifestyle changes. It is hoped that the proposed technologies and systems covered in this special issue can result in improved CVD management and treatment at the point of need, offering a better quality of life to the patient

Wearable smart textiles for long-term electrocardiography monitoring : a review

The continuous and long-term measurement and monitoring of physiological signals such as electrocardiography (ECG) are very important for the early detection and treatment of heart disorders at an early stage prior to a serious condition occurring. The increasing demand for the continuous monitoring of the ECG signal needs the rapid development of wearable electronic technology. During wearable ECG monitoring, the electrodes are the main components that affect the signal quality and comfort of the user. This review assesses the application of textile electrodes for ECG monitoring from the fundamentals to the latest developments and prospects for their future fate. The fabrication techniques of textile electrodes and their performance in terms of skin–electrode contact impedance, motion artifacts and signal quality are also reviewed and discussed. Textile electrodes can be fabricated by integrating thin metal fiber during the manufacturing stage of textile products or by coating textiles with conductive materials like metal inks, carbon mate-rials, or conductive polymers. The review also discusses how textile electrodes for ECG function via direct skin contact or via a non-contact capacitive coupling. Finally, the current intensive and promising research towards finding textile-based ECG electrodes with better comfort and signal quality in the fields of textile, material, medical and electrical engineering are presented as a perspective

A review of ECG-based diagnosis support systems for obstructive sleep apnea

Humans need sleep. It is important for physical and psychological recreation. During sleep our consciousness is suspended or least altered. Hence, our ability to avoid or react to disturbances is reduced. These disturbances can come from external sources or from disorders within the body. Obstructive Sleep Apnea (OSA) is such a disorder. It is caused by obstruction of the upper airways which causes periods where the breathing ceases. In many cases, periods of reduced breathing, known as hypopnea, precede OSA events. The medical background of OSA is well understood, but the traditional diagnosis is expensive, as it requires sophisticated measurements and human interpretation of potentially large amounts of physiological data. Electrocardiogram (ECG) measurements have the potential to reduce the cost of OSA diagnosis by simplifying the measurement process. On the down side, detecting OSA events based on ECG data is a complex task which requires highly skilled practitioners. Computer algorithms can help to detect the subtle signal changes which indicate the presence of a disorder. That approach has the following advantages: computers never tire, processing resources are economical and progress, in the form of better algorithms, can be easily disseminated as updates over the internet. Furthermore, Computer-Aided Diagnosis (CAD) reduces intra- and inter-observer variability. In this review, we adopt and support the position that computer based ECG signal interpretation is able to diagnose OSA with a high degree of accuracy

Classification tree for risk assessment in patients suffering from congestive heart failure via long-term heart rate variability

This study aims to develop an automatic classifier for risk assessment in patients suffering from congestive heart failure (CHF). The proposed classifier separates lower risk patients from higher risk ones, using standard long-term heart rate variability (HRV) measures. Patients are labeled as lower or higher risk according to the New York Heart Association classification (NYHA). A retrospective analysis on two public Holter databases was performed, analyzing the data of 12 patients suffering from mild CHF (NYHA I and II), labeled as lower risk, and 32 suffering from severe CHF (NYHA III and IV), labeled as higher risk. Only patients with a fraction of total heartbeats intervals (RR) classified as normal-to-normal (NN) intervals (NN/RR) higher than 80% were selected as eligible in order to have a satisfactory signal quality. Classification and regression tree (CART) was employed to develop the classifiers. A total of 30 higher risk and 11 lower risk patients were included in the analysis. The proposed classification trees achieved a sensitivity and a specificity rate of 93.3% and 63.6%, respectively, in identifying higher risk patients. Finally, the rules obtained by CART are comprehensible and consistent with the consensus showed by previous studies that depressed HRV is a useful tool for risk assessment in patients suffering from CHF

Impedance plethysmography-based method in the assessment of subclinical atherosclerosis

Background and aimsThe aim of this study was to examine an association of individual and combined pulse waveform parameters derived from bioimpedance measurements, that is pulse waves from a distal impedance plethysmographic (IPG), a whole-body impedance cardiographic (ICG) and transformed distal impedance plethysmographic (tIPG) signals, with markers of subclinical atherosclerosis, i.e. carotid intima-media thickness (cIMT), brachial artery flow-mediated dilation (FMD) and carotid artery distensibility (Cdist). The level of the association was also compared for arterial pulse wave velocity (PWV) and cIMT, FMD, and Cdist.MethodsIPG, ICG, tIPG signals were measured from 1741 Finnish adults aged 30-45 years. The association between pulse wave parameters and cIMT, FMD and Cdist was studied using bootstrapped stepwise Akaike's Information Criterion method resulting in selection of parameters other than PWV, i.e. parameters having stronger association with cIMT, FMD and Cdist than PWV, in the model. Then risk scores were calculated from the selected pulse wave parameters and their association between cIMT, FMD and Cdist was studied with multivariable linear regression analysis.Results The risk score was found to be the third strongest predictor of subclinical atherosclerosis as indicated by cIMT measurement, the second strongest predictor of FMD and the strongest predictor of Cdist. These findings show that several individual pulse wave parameters were associated more strongly with cIMT, FMD, and Cdist than PWV when adjusted with clinical risk factors.ConclusionsImpedance based pulse waveform analysis provides a useful tool for assessing cardiovascular risk and estimating presence of structural changes in the vasculature.</div