Data-driven methods for analyzing ballistocardiograms in longitudinal cardiovascular monitoring

Cardiovascular disease (CVD) is the leading cause of death in the US; about 48% of American adults have one or more types of CVD. The importance of continuous monitoring of the older population, for early detection of changes in health conditions, has been shown in the literature, as the key to a successful clinical intervention. We have been investigating environmentally-embedded in-home networks of non-invasive sensing modalities. This dissertation concentrates on the signal processing techniques required for the robust extraction of morphological features from the ballistocardiographs (BCG), and machine learning approaches to utilize these features in non-invasive monitoring of cardiovascular conditions. At first, enhancements in the time domain detection of the cardiac cycle are addressed due to its importance in the estimation of heart rate variability (HRV) and sleep stages. The proposed enhancements in the energy-based algorithm for BCG beat detection have shown at least 50% improvement in the root mean square error (RMSE) of the beat to beat heart rate estimations compared to the reference estimations from the electrocardiogram (ECG) R to R intervals. These results are still subject to some errors, primarily due to the contamination of noise and motion artifacts caused by floor vibration, unconstrained subject movements, or even the respiratory activities. Aging, diseases, breathing, and sleep disorders can also affect the quality of estimation as they slightly modify the morphology of the BCG waveform.Includes bibliographical reference

Adaptive Heartbeat Modeling for Beat-to-Beat Heart Rate Measurement in Ballistocardiograms

Peer reviewe

Automatic heart rate detection from FBG sensors using sensor fusion and enhanced empirical mode decomposition

International audienceCardiovascular diseases are the world's top leading causes of death. Real time monitoring of patients who have cardiovascular abnormalities can provide comprehensive and preventative health care. We investigate the role of the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and sensor fusion for automatic heart rate detection from a mat with embedded Fiber Bragg Grating (FBG) sensor arrays. The fusion process is performed in the time domain by averaging the readings of the sensors for each sensor array. Subsequently, the CEEMDAN is applied to obtain the interbeat intervals. Experiments are performed with 10 human subjects (males and females) lying on two different positions on a bed for a period of 20 minutes. The overall system performance is assessed against the reference ECG signals. The average and standard deviation of the mean relative absolute error are 0.049, 0.019 and 0.047, 0.038 for fused and best sensors respectively. Sensor fusion together with CEEMDAN proved to be robust against motion artifacts caused by body movements

Performance Comparison for Ballistocardiogram Peak Detection Methods

Citation: Suliman, A., Carlson, C., Ade, C. J., Warren, S., & Thompson, D. E. (2019). Performance Comparison for Ballistocardiogram Peak Detection Methods. IEEE Access, 7, 53945–53955. https://doi.org/10.1109/ACCESS.2019.2912650A number of research groups have proposed methods for ballistocardiogram (BCG) peak detection toward the identification of individual cardiac cycles. However, objective comparisons of these proposed methods are lacking. This paper, therefore, conducts a systematic and objective performance evaluation and comparison of several of these approaches. Five peak-detection methods (three replicated from the literature and two adapted from code provided by the methods' authors) are compared using data from 30 volunteers. A basic cross-correlation approach was also included as a sixth method. Two high-performing methods were identified: the method proposed by Sadek et al. and the method proposed by Brüser et al. The first achieved the highest average peak-detection rate of 94%, the lowest average false alarm rate of 0.0552 false alarms per second, and a relatively small mean absolute error between the real and detected peaks: 0.0175 seconds. The second method achieved the lowest mean absolute error of 0.0088 seconds between the real and detected peaks, an average peak-detection success rate of 89%, and 0.0766 false alarms per second. All metrics are averaged across participants

A NOVEL BCG SENSOR-ARRAY FOR UNOBTRUSIVE CARDIAC MONITORING

Unobtrusive heart rate monitoring is a popular research topic in biomedical engineering. The reason is that convential methods, e.g. the clinical gold standard electrocardiography, require conductive contact to the human body. Other methods such as ballistocardiography try to record these vital signs without electrodes that are attached to the body. So far, these systems cannot replace routine procedures. Most systems have some drawbacks that cannot be compensated, such as aging of the sensor materials or movement artifacts. In addition, the signal form differs greatly from an ECG, which is an electrical signal. The ballistocardiogram has a mechanical source, which makes it harder to evaluate. We have developed a new sensor array made of near-IR-LEDs to record BCGs. IR-sensors do not age in relevant time scales. Analog filtering was neccesary, because the signal amplitude was very small. The digitized data was then processed by various algorithms to extract beat-to-beat or breath-to-breath intervals. The redundancy of multiple BCG channels was used to provide a robust estimation of beat-to-beat intervals and heart rate. We installed the system beneath a mattress topper of a hospital bed, but any other bed would have been sufficient. The validation of this measurement system shows that it is well suited for BCG recordings. The use of multiple channels has proven to be superior to relying on a single BCG channel

A smart cushion for real-time heart rate monitoring

10.1109/BioCAS.2012.64185122012 IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Electronics and Systems for Better Life and Better Environment, BioCAS 2012 - Conference Publications53-5

Unen mittaaminen voimasensoreilla

This thesis presents methods for comfortable sleep measurement at home. Existing medical sleep measurement systems are costly, disturb sleep quality, and are only suited for short-term measurement. As sleeping problems are affecting about 30% of the population, new approaches for everyday sleep measurement are needed. We present sleep measurement methods that are based on measuring the body with practically unnoticeable force sensors installed in the bed. The sensors pick up forces caused by heartbeats, respiration, and movements, so those physiological parameters can be measured. Based on the parameters, the quality and quantity of sleep is analyzed and presented to the user. In the first part of the thesis, we propose new signal processing algorithms for measuring heart rate and respiration during sleep. The proposed heart rate detection method enables measurement of heart rate variability from a ballistocardiogram signal, which represents the mechanical activity of the heart. A heartbeat model is adaptively inferred from the signal using a clustering algorithm, and the model is utilized in detecting heartbeat intervals in the signal. We also propose a novel method for extracting respiration rate variation from a force sensor signal. The method solves a problem present with some respiration sensors, where erroneous cyclicity arises in the signal and may cause incorrect measurement. The correct respiration cycles are found by filtering the input signal with multiple filters and selecting correct results with heuristics. The accuracy of heart rate measurement has been validated with a clinical study of 60 people and the respiration rate method has been tested with a one-person case study. In the second part of the thesis, we describe an e-health system for sleep measurement in the home environment. The system measures sleep automatically, by uploading measured force sensor signals to a web service. The sleep information is presented to the user in a web interface. Such easy-to-use sleep measurement may help individuals to tackle sleeping problems. The user can track important aspects of sleep such as sleep quantity and nocturnal heart rate and learn how different lifestyle choices affect sleep.Unen mittaaminen voimasensoreilla Noin joka kolmannella on ongelmia unen kanssa. Nukahtamisvaikeus, heräily, huono unen laatu sekä erilaiset unenaikaiset hengitysongelmat ovat yleisiä. Helppo ja mukava unen seuranta voisi auttaa unenlaadun parantamisessa. Nykyiset mittausmenetelmät ovat kuitenkin epämukavia ja suunniteltu lähinnä lääketieteellisten diagnoosien tekemiseen. Ne eivät siis sovellu unen mittaamiseen itsenäisesti kotona. Tämä väitöskirja esittelee uuden mittausmenetelmän, joka mahdollistaa unen määrän sekä laadun mittaamisen helposti omassa sängyssä. Lakanan alle laitetaan pehmeästä kalvosta tehty anturi, joka mittaa nukkujan liikkeitä, sydämen sykettä sekä hengitystä. Anturi tunnistaa näiden mittausten perusteella useita uneen liittyviä asioita, kuten unenmäärä, kuorsaaminen sekä yön aikana mitattu leposyke. Uni-informaatio näytetään laitteen käyttäjälle verkkopalvelun tai mobiililaitteen avulla. Väitöskirjassa esitellyn unenmittausmenetelmän etu on, että syke- ja hengitystieto saadaan mitattua siitä huolimatta että anturi ei ole suoraan kosketuksissa nukkujan kehon kanssa. Kehitetyt signaalinkäsittelymenetelmät pystyvät erottamaan signaalista sykkeen ja hengityksen, sillä erilaisten mittaushäiriöiden ilmaantuminen signaaliin on otettu huomioon. Uutta unimittausmenetelmää on ehditty jo soveltaa käytännössä. Kehitetty tuote toimii siten, että mittaus lähetetään sensorilta langattomasti mobiililaitteelle, jossa unitiedot näytetään käyttäjälle. Mobiilisovellus antaa ohjeita unen parantamiseksi mittausten sekä käyttäjän profiilin perusteella