Automatic and Non-Invasive Delineation of the Seismocardiogram Signal for the Estimation of Cardiac Time Intervals with Applications in Diastolic Timed Vibration and Early Stage Hemorrhage Detection

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Seismocardiography, machine learning, cardiac time intervals, hemorrhage, diastolic timed vibration

Seismocardiography is the non-invasive measurement of the heart vibration by placing an accelerometer on the human chest. Due to its non-invasive nature, the seismocardiogram signal could be embedded inside portable devices for the purpose of health monitoring and remote diagnosis. With the combination of the electrocardiogram (ECG) signal, cardiac time intervals (CTI) could be extracted. CTIs are timing intervals that are associated with specific events of the cardiac cycle. The research community has explored the potential of CTI in the diagnosis of chronic myocardial disease, ischemic and coronary artery disease, arterial hypertension, cardiac resynchronization therapy, and implantable cardioverter de-fibrillator. For the extracted CTIs to be useful in a medical device, the seismocardiogram signal (SCG) has to be automatically delineated. Upon the automatic delineation of CTIs, the timing parameters could be either combined with other physiological signals to create new indices that have unique physiological interpretations or to be used as a complimentary technology. Hence, The present dissertation has three main objectives: (1) automatic SCG delineation algorithm, (2) application of cardiac time intervals (extracted from SCG) for generating aunique index for early stage hemorrhage detection, and (3) complementary technology for optimization of the diastolic timed vibration therapy. For the first objective, the proposed delineation algorithm had the capability to correctly estimate the CTIs while discarding low-quality cardiac cycles, which are the ones thatdon’t have identifiable fiducial points. For the second objective, the combination of the electrocardiogram, seismocardiogram, and photoplethysmogram signals was used to design a hemorrhage progression index, which ultimately was applied for early stage detection. For the last objective, the extracted CTIs were applied to the “diastolic timed vibration”, which is a potential therapy for patients with acute ischemia during the pre-hospitalization phase. A calibration methodology was proposed for diastole detection in real-time.

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This thesis may be printed or downloaded for non-commercial research and scholarly purposes. Copyright remains with the author.
Dr. Carlo Menon
Applied Sciences: School of Engineering Science
Thesis type: 
(Thesis) Ph.D.