The objective of this work is to improve the diagnostic quality and reduce the radiation dose of computed tomography coronary angiography (CTCA) imaging by developing gating techniques based on signals derived from cardiac motion, rather than the currently used electrocardiogram (ECG), to more reliably trigger data acquisition during periods of cardiac quiescence. Because the ECG is an indication of electrical activity, it is a surrogate marker of the mechanical state of the heart. Therefore, gating based on a signal derived directly from cardiac motion using either echocardiography or seismocardiography (SCG) should prove better at detecting and predicting periods of cardiac quiescence. Improved gating would permit the use of CTCA in more instances to either replace or determine the necessity of invasive and expensive CCAs. This work presents novel methods for detecting and predicting cardiac quiescence. Quiescence is detected as periods of minimal velocity from echocardiography, computed tomography (CT), and SCG. Identified quiescent periods are used to develop and evaluate techniques for predicting cardiac quiescence using echocardiography and SCG. Both echocardiography and SCG are shown to be more accurate for predicting quiescent periods than ECG. Additionally, the average motion during quiescent periods predicted by echocardiography and SCG is shown to be lower than those predicted using only ECG. Lastly, cardiac CT reconstructions from quiescent phases predicted by a commercial CT scanner were compared to the optimal quiescent phases calculated using the CT quiescence detection methods presented in this work. The diagnostic quality of the reconstructions from the optimal phases was found to be higher than that of the phases predicted by the CT scanner, suggesting that there is the potential for marked improvement in CTCA performance through more accurate cardiac gating.

Non-invasive remote detection of cardiac and blood displacements is an important topic in cardiac telemedicine. Here we propose kinocardiography (KCG), a non-invasive technique based onmeasurement of body vibrations produced by myocardial contraction and blood flow through thecardiac chambers and major vessels. KCG is based on ballistocardiography and seismocardiographyand measures 12 degrees-of-freedom (DOF) of body motion. The integral of kinetic energy (iK)and maximum Power (Pmax) obtained from the linear and rotational SCG/BCG signals, was computedover the cardiac cycle, and used as a marker of cardiac mechanical function. We showedthat KCG metrics show high repeatability, can be computed on 50 Hz and 1 kHz SCG/BCG signalsindifferently, that most of the metrics were highly similar when computed on different sensors,and with less than 5% of error when computed on record length longer than 60 s. Finally, weshow that KCG metrics allow detecting dobutamine-induced haemodynamic changes with a highaccuracy and present a major improvement over single axis ballistocardiography or seismocardiography.These results suggest that KCG may be a robust and non-invasive method to monitorcardiac inotropic activity. / La détection à distance et non invasive des déplacements cardiaques et sanguins est un sujet important en télémédecine. Nous proposons ici la kinocardiographie (KCG), une technique non invasive basée sur mesure des vibrations corporelles produites par la contraction du myocarde et par le flux sanguin au travers des cavités cardiaques et des principaux vaisseaux sanguins. La KCG est basée sur la balistocardiographie et la seismocardiographie et mesure 12 degrés de liberté (DOF) de mouvement corporel. L'intégrale de l'énergie cinétique (iK) et la puissance maximale (Pmax) obtenue à partir des signaux SCG / BCG linéaire et rotationnel, a été calculée au cours du cycle cardiaque, et sont utilisées comme marqueur de la fonction mécanique cardiaque. Ce travail montre que les métriques KCG sont caractérisées par une répétabilité élevée, peuvent être calculées sur des signaux SCG / BCG à 50 Hz et à 1 kHz indifféremment, que la plupart des métriques étaient très similaires lorsqu'elles étaient calculées sur différents capteurs, et avec moins de 5% d'erreur lors du calcul sur une longueur d'enregistrement supérieure à 60 s. Enfin ce travail montre que les métriques KCG permettent de détecter les changements hémodynamiques induits par la dobutamine avec précision et présentent une amélioration majeure par rapport à la balistocardiographie à un seul axe ou à la seismocardiographie. Ces résultats suggèrent que la KCG peut être une méthode robuste et non invasive pour surveiller l'activité inotrope du coeur. / Doctorat en Sciences de l'ingénieur et technologie / La défense publique a eu lieu le 05/05/2021. Cet upload remplace l'upload pécédent et contient les derniers commentaires du jury après la défense publique. / info:eu-repo/semantics/nonPublished

The 21st century brought tremendous changes in technologies, sweeping away every aspect of daily life, from social to private life, from education to professional world, at a breakneck pace. Medical science has not been spared by this colossal wave of changes: e-health, e-patients, e-physicians and e-medical students have already made their first appearance in the routine medical practice. Surgical robots, 3D printing, Artificial-Intelligence based imaging, smartwatches, wearable sensors are already used to improve diagnosis, personalize treatments and monitor patients’ health. Ballistocardiography (BCG) and seismocardiography (SCG) are ancient techniques which estimate the mechanical performance of the heart through measuring myocardial contraction-induced vibrations transmitted to the skin surface. They made their first appearance into clinic at the beginning of the 20th century to help in the diagnosis of cardiovascular diseases, but their popularity drastically declined in the middle of the 70s and they never had their place in clinical practice: cumbersome and complex equipment, ambiguity in the signal interpretation, unclear understanding of the physiological genesis of the signal, the advent of high-performing technologies (cardiac MRI, echocardiography) are a few of the reasons for their clinical failure. Thanks to the tremendous improvements in technologies, these techniques of the past came back to the medical world as wearable biosensors, first holding the promise of a remote and continuous monitoring of the cardiovascular status. Displacement, velocity and acceleration of blood mass flowing into cardiac chambers and main extracardiac vessels are recorded for each heartbeat along three cardinal axes and in two dimensions, a linear and a rotational one, by the renewed BCG and SCG, with 6 degrees-of-freedom (6D-BCG and 6D-SCG). By applying the Newtonian principles to the recorded signals, signal processing algorithms automatically compute the kinetic energy (KE) and its temporal integral (iK) for each cardiac cycle. This work first analyzed the influence of normal and pathological respiration as well as the effects of sympathetic overactivity on the genesis of the 6D-BCG and 6D-SCG signals and the iK parameters; secondary, it tested the usefulness of 6D-BCG and 6D-SCG techniques in the detection of cardiac dysfunction and hemodynamic impairment during acute myocardial infarction and reperfusion in an animal model for acute coronary syndrome. While breathing normally mildly affects cardiac iK parameters, pathological respiration profoundly alters them. During a sustained end-expiratory apnea, as it happens in patients suffering from central sleep apnea, the iK generated within a contractile cycle acutely increases at the end of the apnea, strictly depending on the magnitude of sympathetic activity; inspiring against a resistance, as it happens in patients suffering from obstructive sleep apnea, acutely increases the cardiac iK and this surge is related to the acute external force afterloading the left ventricle. So, whether it is a central apnea or an obstructive one, myocardial mechanical function as expressed in terms of iK is profoundly impaired, suggesting the myocardium is enduring a sustain endeavor during these pathological respirations. During an experimental acute myocardial infarction, in a context of mechanical ventilation without major respiratory events, iK parameters drastically drop during coronary occlusion and does not improve during reperfusion, along with systemic blood pressures and cardiac output, thus holding the promise to continuously monitor the cardiac contractile function and the hemodynamic profile both during acute coronary occlusion and reperfusion. Renewed and wearable 6D-BCG and 6D-SCG may prove useful in the detection and continuous monitoring of cardiac dysfunction and hemodynamic impairment in patients suffering from sleep disordered breathing and may be used in the mid-long-term follow-up of patients with myocardial dysfunction of ischemic origin. There is still a lot of work to do before validating these renewed technologies in the practice of cardiovascular medicine, but evidences are there to consider them as next generation medical devices. / Doctorat en Sciences médicales (Médecine) / info:eu-repo/semantics/nonPublished

Seismocardiography (SCG) is the low frequency chest surface vibration generated by the mechanical activities of the heart. SCG has been found to have clinical utilities in diagnosis of different cardiac diseases. The first part of this study focused on the application of SCG signal in predicting hospital readmissions of the heart failure (HF) patients. Conventional machine learning and deep learning models have been developed using SCG signal acquired from the HF patients. Early HF readmissions was predicted with decent accuracies with these models. This may potentially help the clinicians to identify the patients who need special care and treatment and make timely targeted interventions. This will ensure better management of HF patients and reduce the mortality rate. One of the limitations of using SCG signal in clinical settings is its variability. To investigate SCG variability, an exercise protocol has been developed. SCG signal was acquired from the healthy subjects when they underwent the protocol. It was found that cardiopulmonary interactions may contribute to the variability in SCG signal. The study results help to better understand the source of variability which eventually may increase the clinical utility of SCG signal. Another limitation of SCG signal is that it is highly sensitive to the ambient and locomotion-induced noises. This can distort the SCG signal. Hence, removal of noises is a necessary step to use SCG in ambulatory assessment of HF patients. To encounter this problem, a healthy subject performed different maneuvers to induce few common types of noises in the SCG signal. Different signal processing techniques have been employed to remove the noises from the signal. A comparison among different techniques has been provided which may lead to developing an algorithm in the future that is capable of autodetecting noises and suppress them.

The clinical usage of Seismocardiography (SCG) is increasing as it is being shown to be an effective non-invasive measurement for heart monitoring. SCG measures the vibrational activity at the chest surface and applications include non-invasive assessment of myocardial contractility and systolic time intervals. Respiratory activity can also affect the SCG signal by changing the hemodynamic characteristics of cardiac activity and displacing the position of the heart. Other clinically significant information, such as systolic time intervals, can thus manifest themselves differently in an SCG signal during inspiration and expiration. Grouping SCG signals into their respective respiratory cycle can mitigate this issue. Prior research has focused on developing machine learning classification methods to classify SCG events as according to their respiration cycle. However, recent research at the Biomedical Acoustics Research Laboratory (BARL) at UCF suggests grouping SCG signals into high and low lung volume may be more effective. This research aimed at com- paring the efficiency of grouping SCG signals according to their respiration and lung volume phase and also developing a method to automatically identify the respiration and lung volume phase of SCG events.

Cardiac time intervals (CTIs) are vital indicators of cardiac health and can be estimated using a combination of electrocardiography (ECG) and seismocardiography (SCG). This study investigates the impact of SCG sensor location across the sternum on CTI estimations and heart rate variability parameters. Signal processing algorithms were developed to detect the opening and closure of heart valves on SCG for CTI calculation. A novel ECG-independent method was also developed based on template matching to determine similar parameters solely based on SCG. Comparative analysis with gold-standard methods were conducted on the SCG fiducial points, evaluating accuracy and performance. Results indicate a high overall average F1 score and correlation for all fiducial point detections. The p values revealed significant differences in SCG-derived CTI estimations across the sensor locations, highlighting the importance of sensor placement for accurate assessments. This finding underscores a fundamental step toward precise evaluation of cardiac health.