BioWatch: Estimation of Heart and Breathing Rates from Wrist Motions

Continued developments of sensor technology including hardware miniaturization and increased sensitivity have enabled the development of less intrusive methods to monitor physiological parameters during daily life. In this work, we present methods to recover cardiac and respiratory parameters using accelerometer and gyroscope sensors on the wrist. We demonstrate accurate measurements in a controlled laboratory study where participants (n = 12) held three different positions (standing up, sitting down and lying down) under relaxed and aroused conditions. In particular, we show it is possible to achieve a mean absolute error of 1.27 beats per minute (STD: 3.37) for heart rate and 0.38 breaths per minute (STD: 1.19) for breathing rate when comparing performance with FDA-cleared sensors. Furthermore, we show comparable performance with a state-of-the-art wrist-worn heart rate monitor, and when monitoring heart rate of three individuals during two consecutive nights of in-situ sleep measurements.National Science Foundation (U.S.) (CCF-1029585)Samsung (Firm). Think Tank TeamMIT Media Lab Consortiu

BCG Signal Processing

Bakalářská práce se zabývá návrhem a vývojem softwarového nástroje pro komplexní zpracování a následnou analýzu balistokardiografického signálu (BKG). Software byl vyvinut v interaktivním programové prostředí a skriptovacím programovacím jazyce Matlab v podobě grafické uživatelské rozhraní (GUI). Zpracováním signálu je myšlena primárně jeho filtrace a úprava pro následnou analýzu. Na základě literární rešerše byly implementovány lineární frekvenčně selektivní filtry a filtr využívající vlnkovou transformaci. Aplikace dále umožňuje frekvenční analýzu a úpravu signálu pro výpočet tepové frekvence. Jednotlivé použité metody jsou v práci testovány na syntetických i reálných datech. V poslední části jsou vybrané metody srovnání na základě objektivního hodnocení v podobě odstupu signálu od šumu (SNR).Bachelor thesis deals with design and development of software tool for complex processing and analysis of balistocardiographic signal (BCG). The software was developed in an interactive programming environment and the Matlab scripting programming language in the form of a graphical user interface (GUI). Signal processing means primarily its filtration and treatment for subsequent analysis. On the basis of literary research, linear frequency selective filters and a wavelet transform filter were implemented. The application also allows frequency analysis and signal processing to calculate pulse rate. The individual methods used are tested on synthetic and real data. In the last part, selected methods of comparison are based on objective evaluation in the form of signal-to-noise ratio (SNR).450 - Katedra kybernetiky a biomedicínského inženýrstvívýborn

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

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

Peer reviewe

Ballistocardiography : physically-based modeling to bridge physiology and technology

The ballistocardiogram (BCG) captures the motion of the center of mass (CoM) of the human body resulting from the blood motion within the circulatory system. The BCG signal reflects the status of the cardiovascular system as a whole and, for this reason, it offers a more holistic evaluation of cardiovascular performance than traditional markers, such as electrocardiography or echocardiography. In addition, the acquisition of BCG signals is not invasive, can be performed with several devices -such as accelerometers, chairs, hydraulic system- and does not require body contact. However, the utilization of the BCG as a clinical diagnosis tool and monitoring method is currently hindered by the absence of standardized methods to link the motion of the CoM of the human body, which constitutes the physiological BCG (pBCG), with the BCG signal acquired with sensing devices, which constitute the measured BCG (mBCG). To address this issue, in the first part of the present work we provide a formal definition of pBCG and mBCG, which will be then utilized to (i) define the physical connection between the mBCG obtained with two sensing devices, i.e. the suspended bed and the load cell system, and the pBCG signal and (ii) reconstruct the individual CoM motion. In the second part of the thesis, we focus on the synergistic combination between the physiology behind the BCG signal and the physics of the sensing devices, which may lead to novel clinical applications. In particular, we propose a cuff-less method for absolute pulse pressure assessment via the synergistic integration of two components, namely (i) theoretical simulations of cardiovascular physiology by means of a mathematical closed-loop model of the cardiovascular system, and (ii) synchronous ECG, SCG and BCG data acquired in our laboratory. Then, we present an evolutionary algorithm aimed at individualizing the closed-loop model of the cardiovascular system, with which we will also provide an estimate of the arterial pressure. Finally, in the last part of the thesis, we draw the conclusion of this study, showing how the integration of the mathematical modeling alongside with clinical studies can improve the understanding of the BCG signal and actively contributing to the development of new clinical monitoring solution.Includes bibliographical references (pages 80-84)

Extracting Cardiac Information From Medical Radar Using Locally Projective Adaptive Signal Separation

Electrocardiography is the gold standard for electrical heartbeat activity, but offers no direct measurement of mechanical activity. Mechanical cardiac activity can be assessed non-invasively using, e.g., ballistocardiography and recently, medical radar has emerged as a contactless alternative modality. However, all modalities for measuring the mechanical cardiac activity are affected by respiratory movements, requiring a signal separation step before higher-level analysis can be performed. This paper adapts a non-linear filter for separating the respiratory and cardiac signal components of radar recordings. In addition, we present an adaptive algorithm for estimating the parameters for the non-linear filter. The novelty of our method lies in the combination of the non-linear signal separation method with a novel, adaptive parameter estimation method specifically designed for the non-linear signal separation method, eliminating the need for manual intervention and resulting in a fully adaptive algorithm. Using the two benchmark applications of (i) cardiac template extraction from radar and (ii) peak timing analysis, we demonstrate that the non-linear filter combined with adaptive parameter estimation delivers superior results compared to linear filtering. The results show that using locally projective adaptive signal separation (LoPASS), we are able to reduce the mean standard deviation of the cardiac template by at least a factor of 2 across all subjects. In addition, using LoPASS, 9 out of 10 subjects show significant (at a confidence level of 2.5%) correlation between the R-T-interval and the R-radar-interval, while using linear filters this ratio drops to 6 out of 10. Our analysis suggests that the improvement is due to better preservation of the cardiac signal morphology by the non-linear signal separation method. Hence, we expect that the non-linear signal separation method introduced in this paper will mostly benefit analysis methods investigating the cardiac radar signal morphology on a beat-to-beat basis

A semi‐active human digital twin model for detecting severity of carotid stenoses from head vibration—A coupled computational mechanics and computer vision method

In this work we propose a methodology to detect the severity of carotid stenosis from a video of a human face with the help of a coupled blood flow and head vibration model. This semi‐active digital twin model is an attempt to link non‐invasive video of a patient face to the percentage of carotid occlusion. The pulsatile nature of blood flow through the carotid arteries induces a subtle head vibration. This vibration is a potential indicator of carotid stenosis severity and it is exploited in the present study. A head vibration model has been proposed in the present work that is linked to the forces generated by blood flow with or without occlusion. The model is used to generate a large number of virtual head vibration data for different degrees of occlusion. In order to determine the in vivo head vibration, a computer vision algorithm is adopted to use human face videos. The in vivo vibrations are compared against the virtual vibration data generated from the coupled computational blood flow/vibration model. A comparison of the in vivo vibration is made against the virtual data to find the best fit between in vivo and virtual data. The preliminary results on healthy subjects and a patient clearly indicate that the model is accurate and it possesses the potential for detecting approximate severity of carotid artery stenoses