Announcing the Final Examination of Rajkumar Dhar for the degree of Doctor of Philosophy
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. This study focused on the application
of SCG signal in predicting hospital readmissions of heart failure (HF) patients. Conventional machine learning and deep
learning models have been built using SCG signal acquired from the HF patients. These models have been found to predict
early HF readmissions with decent accuracies. This may 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 is its variability which may mask the subtle changes in the SCG waveform. To
investigate SCG variability, an exercise protocol has been developed. SCG signal was acquired from the healthy subjects
when they went through the protocol. It was found that respiratory phases 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.
Another limitation of SCG signal is that it is sensitive to the ambient and locomotion-induced noises. This can distort the
SCG signal. 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 building an algorithm in future that is
capable of autodetecting noises and suppress them.
Committee in Charge:
Hansen Mansy, Chair, Mechanical and Aerospace Engineering
Alain Kassab, Department of Mechanical and Aerospace Engineering
Luigi Perotti, Department of Mechanical and Aerospace Engineering
Ethan Hill, School of Kinesiology and Physical Therapy
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