Cardiac Parameters Estimation Using Seismocardiographic and Remote Photoplethysmographic Signals
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2025
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Abstract
Cardiovascular diseases (CVDs) are major risk factors contributing to the increasing death rate. Effective and regular monitoring of cardiac activities are useful for early detection and clinical management of the CVDs. Many vital parameters, such as heart rate (HR), heart rate variability (HRV), blood pressure (BP), oxygen saturation (SpO2), and respiratory rate (BR) provide insight to cardiac health and help in diagnosing and treating life-threatening diseases. In this study, two emerging cardiac modalities, such as seismocardiography (SCG) and remote photoplethysmography (rPPG) are considered for the estimation of cardiac vital parameters. The SCG is a non-invasive technique that captures the chest wall vibrations induced by cardiac mechanical activities. The acquired SCG signal needs precise delineation and feature extraction prior to the measurement of human vital parameters. The first part of the thesis involves the detection of the prominent peaks of SCG cycles and investigates their possible clinical applications. A data-adaptive modified variational mode decomposition (MVMD) method along with simple decision rules are incorporated to extract two fiducial points, AO and post-AC (pAC) peaks. Later, these points are utilized to derive systolic blood pressure (SBP), diastolic blood pressure (DBP) and HRV parameters. Another application is explored, which utilizes these feature points along with the demographical information of the volunteers to identify ventricular depolarization events using a deep feedforward neural network (DFN). The proposed methods are evaluated on publicly available CEBS database (at PhysioNet archive) and in-house recordings created using a small electronic circuit board consisting of a 3D MEMs-based accelerometer, pre-amplifier, and a filter.
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Supervisors: Bhuyan, M K and Sharma, L N