A novel broadband forcecardiography sensor for simultaneous monitoring of respiration, infrasonic cardiac vibrations and heart sounds

The precordial mechanical vibrations generated by cardiac contractions have a rich frequency spectrum. While the lowest frequencies can be palpated, the higher infrasonic frequencies are usually captured by the seismocardiogram (SCG) signal and the audible ones correspond to heart sounds. Forcecardiography (FCG) is a non-invasive technique that measures these vibrations via force sensing resistors (FSR). This study presents a new piezoelectric sensor able to record all heart vibrations simultaneously, as well as a respiration signal. The new sensor was compared to the FSR-based one to assess its suitability for FCG. An electrocardiogram (ECG) lead and a signal from an electro-resistive respiration band (ERB) were synchronously acquired as references on six healthy volunteers (4 males, 2 females) at rest. The raw signals from the piezoelectric and the FSR-based sensors turned out to be very similar. The raw signals were divided into four components: Forcerespirogram (FRG), Low-Frequency FCG (LF-FCG), High- Frequency FCG (HF-FCG) and heart sounds (HS-FCG). A beat-by-beat comparison of FCG and ECG signals was carried out by means of regression, correlation and Bland–Altman analyses, and similarly for respiration signals (FRG and ERB). The results showed that the infrasonic FCG components are strongly related to the cardiac cycle (R2 > 0.999, null bias and Limits of Agreement (LoA) of ± 4.9 ms for HF-FCG; R2 > 0.99, null bias and LoA of ± 26.9 ms for LF-FCG) and the FRG inter-breath intervals are consistent with ERB ones (R2 > 0.99, non-significant bias and LoA of ± 0.46 s). Furthermore, the piezoelectric sensor was tested against an accelerometer and an electronic stethoscope: synchronous acquisitions were performed to quantify the similarity between the signals. ECG-triggered ensemble averages (synchronized with R-peaks) of HF-FCG and SCG showed a correlation greater than 0.81, while those of HS-FCG and PCG scored a correlation greater than 0.85. The piezoelectric sensor demonstrated superior performances as compared to the FSR, providing more accurate, beat-by-beat measurements. This is the first time that a single piezoelectric sensor demonstrated the ability to simultaneously capture respiration, heart sounds, an SCG-like signal (i.e., HF-FCG) and the LF-FCG signal, which may provide information on ventricular emptying and filling events. According to these preliminary results the novel piezoelectric FCG sensor stands as a promising device for accurate, unobtrusive, long-term monitoring of cardiorespiratory functions and paves the way for a wide range of potential applications, both in the research and clinical fields. However, these results should be confirmed by further analyses on a larger cohort of subjects, possibly including also pathological patients

A Novel Broadband Forcecardiography Sensor for Simultaneous Monitoring of Respiration, Infrasonic Cardiac Vibrations and Heart Sounds

The precordial mechanical vibrations generated by cardiac contractions have a rich frequency spectrum. While the lowest frequencies can be palpated, the higher infrasonic frequencies are usually captured by the seismocardiogram (SCG) signal and the audible ones correspond to heart sounds. Forcecardiography (FCG) is a non-invasive technique that measures these vibrations via force sensing resistors (FSR). This study presents a new piezoelectric sensor able to record all heart vibrations simultaneously, as well as a respiration signal. The new sensor was compared to the FSR-based one to assess its suitability for FCG. An electrocardiogram (ECG) lead and a signal from an electro-resistive respiration band (ERB) were synchronously acquired as references on six healthy volunteers (4 males, 2 females) at rest. The raw signals from the piezoelectric and the FSR-based sensors turned out to be very similar. The raw signals were divided into four components: Forcerespirogram (FRG), Low-Frequency FCG (LF-FCG), High-Frequency FCG (HF-FCG) and heart sounds (HS-FCG). A beat-by-beat comparison of FCG and ECG signals was carried out by means of regression, correlation and Bland–Altman analyses, and similarly for respiration signals (FRG and ERB). The results showed that the infrasonic FCG components are strongly related to the cardiac cycle (R2 > 0.999, null bias and Limits of Agreement (LoA) of ± 4.9 ms for HF-FCG; R2 > 0.99, null bias and LoA of ± 26.9 ms for LF-FCG) and the FRG inter-breath intervals are consistent with ERB ones (R2 > 0.99, non-significant bias and LoA of ± 0.46 s). Furthermore, the piezoelectric sensor was tested against an accelerometer and an electronic stethoscope: synchronous acquisitions were performed to quantify the similarity between the signals. ECG-triggered ensemble averages (synchronized with R-peaks) of HF-FCG and SCG showed a correlation greater than 0.81, while those of HS-FCG and PCG scored a correlation greater than 0.85. The piezoelectric sensor demonstrated superior performances as compared to the FSR, providing more accurate, beat-by-beat measurements. This is the first time that a single piezoelectric sensor demonstrated the ability to simultaneously capture respiration, heart sounds, an SCG-like signal (i.e., HF-FCG) and the LF-FCG signal, which may provide information on ventricular emptying and filling events. According to these preliminary results the novel piezoelectric FCG sensor stands as a promising device for accurate, unobtrusive, long-term monitoring of cardiorespiratory functions and paves the way for a wide range of potential applications, both in the research and clinical fields. However, these results should be confirmed by further analyses on a larger cohort of subjects, possibly including also pathological patients

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

Seismocardiography - Genesis, and Utilization of Machine Learning for Variability Reduction and Improved Cardiac Health Monitoring

Seismocardiography (SCG) is the measured chest surface vibrations resulting from cardiac activity. Although SCG can contain information that correlate with cardiac health, its utility may be limited by lack of understanding of the signal genesis and a variability that can mask subtle SCG changes. The current research utilized medical imaging reconstruction and finite element method (FEM) to simulate SCG by modeling the propagation of myocardial movements to the chest surface. FEM analysis provided a link between myocardial movements and the SCG signals measured at the chest surface and suggested that myocardial movement is a primary source of SCG. Increased understanding of the sources and propagation of SCG may help increase the utility of SCG as a cardiac monitoring tool. To reduce the variability of SCG measured in human subjects, unsupervised machine learning (ML) was implemented to group SCG beats into clusters with minimal intra-cluster heterogeneity. The clustering helped reduce the SCG variability and unveiled consistent relations with the respiratory phases and SCG morphology. This clustering reduced noise in calculating signal features and provided additional useful features. The study also analyzed longitudinal SCG from heart failure (HF) patients in order to predict HF readmission. Here, many time- and frequency-domain SCG features were extracted. Certain features showed good correlations with readmission. Using supervised ML algorithms, high classification accuracies (up to 100%) were achieved suggesting high SCG utility for monitoring HF patients and possibly other heart conditions. Effective monitoring followed by timely intervention can lead to improved patient management and reduced mortality

Contactless Electrocardiogram Monitoring with Millimeter Wave Radar

The electrocardiogram (ECG) has always been an important biomedical test to diagnose cardiovascular diseases. Current approaches for ECG monitoring are based on body attached electrodes leading to uncomfortable user experience. Therefore, contactless ECG monitoring has drawn tremendous attention, which however remains unsolved. In fact, cardiac electrical-mechanical activities are coupling in a well-coordinated pattern. In this paper, we achieve contactless ECG monitoring by breaking the boundary between the cardiac mechanical and electrical activity. Specifically, we develop a millimeter-wave radar system to contactlessly measure cardiac mechanical activity and reconstruct ECG without any contact in. To measure the cardiac mechanical activity comprehensively, we propose a series of signal processing algorithms to extract 4D cardiac motions from radio frequency (RF) signals. Furthermore, we design a deep neural network to solve the cardiac related domain transformation problem and achieve end-to-end reconstruction mapping from RF input to the ECG output. The experimental results show that our contactless ECG measurements achieve timing accuracy of cardiac electrical events with median error below 14ms and morphology accuracy with median Pearson-Correlation of 90% and median Root-Mean-Square-Error of 0.081mv compared to the groudtruth ECG. These results indicate that the system enables the potential of contactless, continuous and accurate ECG monitoring

High-Performance Accelerometer Based On Asymmetric Gapped Cantilevers For Physiological Acoustic Sensing

Continuous or mobile monitoring of physiological sounds is expected to play important role in the emerging mobile healthcare field. Because of the miniature size, low cost, and easy installation, accelerometer is an excellent choice for continuous physiological acoustic signal monitoring. However, in order to capture the detailed information in the physiological signals for clinical diagnostic purpose, there are more demanding requirements on the sensitivity/noise performance of accelerometers. In this thesis, a unique piezoelectric accelerometer based on the asymmetric gapped cantilever which exhibits significantly improved sensitivity is extensively studied. A meso-scale prototype is developed for capturing the high quality cardio and respiratory sounds on healthy people as well as on heart failure patients. A cascaded gapped cantilever based accelerometer is also explored for low frequency vibration sensing applications such as ballistocardiogram monitoring. Finally, to address the power issues of wireless sensors such as wireless wearable health monitors, a wide band vibration energy harvester based on a folded gapped cantilever is developed and demonstrated on a ceiling air condition unit

Characterization, Classification, and Genesis of Seismocardiographic Signals

Seismocardiographic (SCG) signals are the acoustic and vibration induced by cardiac activity measured non-invasively at the chest surface. These signals may offer a method for diagnosing and monitoring heart function. Successful classification of SCG signals in health and disease depends on accurate signal characterization and feature extraction. In this study, SCG signal features were extracted in the time, frequency, and time-frequency domains. Different methods for estimating time-frequency features of SCG were investigated. Results suggested that the polynomial chirplet transform outperformed wavelet and short time Fourier transforms. Many factors may contribute to increasing intrasubject SCG variability including subject posture and respiratory phase. In this study, the effect of respiration on SCG signal variability was investigated. Results suggested that SCG waveforms can vary with lung volume, respiratory flow direction, or a combination of these criteria. SCG events were classified into groups belonging to these different respiration phases using classifiers, including artificial neural networks, support vector machines, and random forest. Categorizing SCG events into different groups containing similar events allows more accurate estimation of SCG features. SCG feature points were also identified from simultaneous measurements of SCG and other well-known physiologic signals including electrocardiography, phonocardiography, and echocardiography. Future work may use this information to get more insights into the genesis of SCG

A low-cost system for seismocardiography-based cardiac triggering: A practical solution for cardiovascular magnetic resonance imaging at 3 tesla

This study describes a pilot clinical validation of a new low-cost system for the continuous monitoring of the human body's cardiorespiratory activities within the magnetic resonance examination area. This study primarily focuses on monitoring cardiac activity and the related cardiac triggering. The patented system tested by the authors is based on seismocardiography (SCG). The study was conducted on 18 subjects on a Siemens Prisma 3T MR scanner. Standard anatomical and diffusion sequences were used to test cardiac activity monitoring. A wide range of commonly used diagnostic sequences were used to test imaging of the heart by means of cardiac triggering. System functionality was verified against a commercially available electrocardiography (ECG) system. Monitoring of cardiac activity (detection of the R-R interval in ECG and the AO-AO interval in SCG) was objectively evaluated by determining the overall probability of correct detection (ACC), sensitivity (SE), positive predictive value (PPV), and harmonic mean between SE and PPV, i.e. F1. Imaging quality control using Cardiac Triggering was performed by subjective evaluation of images by the physicians. The study conducted clearly confirmed the functionality of the system in terms of continuous cardiac activity monitoring. In all 18 subjects, a mean PPV > 99% was achieved; F1 > 99 %; SE > 99 %; ACC > 98 %; 1.96 sigma < 3.5 bpm. Also, Cardiac Triggering functionality was confirmed by the physicians on the basis of analyzing cardiac images using the T1/T2 balanced echo sequences and the T1 flash sequence measured natively.Web of Science711862911860

An Investigation of the Relationship Between Respiration and Seismocardiographic Signals Using Signal Processing, Machine Learning and Finite Element Analysis

Cardiovascular disease (CVD) is one of the major causes of death worldwide. Disease management, as well as patient health, can be significantly improved by early detection of patient deterioration and proper intervention. Review of the patient\u27s medical history and physical examination including stethoscope auscultation and electrocardiograms (ECG), echocardiography imaging, numerous blood testing, and computed tomography are common means of evaluating cardiac function. Seismocardiographic (SCG) signals are the vibrations of the chest wall due to the mechanical activity of the heart. These signals can provide useful information about heart function and could be used to diagnose cardiac problems. The variability in SCG waveforms may make it difficult to obtain accurate waveforms, limiting SCG clinical value. Breathing is a well-known source of change in SCG morphology. In this dissertation, SCG variability due to respiration is described, related signal characteristics changes are measured, and the effects of breathing states and maneuvers are discussed. Increased SCG variability understanding can aid in accounting for variability in signal as well as more accurate characterization of significant features in SCG that could correlate with heart health. Direct airflow measurement is frequently used to assess respiration. When direct airflow access is difficult or unavailable, indirect ways to breathing monitoring might be used. The seismocardiographic signal is influenced by respiration. As a result, this signal can be utilized to noninvasively determine the respiratory phases. Hence, SCG may reduce the requirement for direct airflow measurements in situations where SCG signals are easily available. This dissertation extracts respiration derived from SCG in healthy adults using machine learning techniques and compares the results with direct respiration airflow measurements. Finite element method (FEM) was implemented to perform SCG simulation during different breathing states by modeling the myocardial movements propagation to the surface of the chest. SCG waveforms predicted by FEM analysis were comparable with SCG signals measured at the surface of the chest suggesting that myocardial activity is the SCG main source. The effects of increased soft tissue in the chest wall on SCG signal were investigated and were found to decrease SCG amplitude. The research led to an enhanced understanding of the SCG variability sources as well as respiratory phase-detection methods. These discoveries could lead to better non-invasive, low-cost approaches development for managing cardiovascular disorders, which can enhance patient quality of life

Wearable and Nearable Biosensors and Systems for Healthcare

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