About the lab
Biosignal Processing Lab
Featured research (2)
Vertical Ground Reaction Force (VGRF) is a force obtained during gait cycle beneath the feet and is used to screen the severity of Parkinson’s disease (PD) patient’s in clinical environment. This article investigates the VGRF signals (left and right) semblance nature among PD patients and control subjects as a function of time and possibility of reconstructing dual tasking VGRF signal from normal walking VGRF signals using radial basis function (RBF) based artificial intelligence (AI). There are many traditional methods for gait analysis and these methods are purely subjective and none made semblance analysis of same subjects gait pattern in different tasking. In order to overcome the difficulties faced by PD patients, RBF based AI is proposed in this research to reconstruct the dual tasking VGRF signal from normal walking VGRF signal. 93 PD patients with mean age: 66.3 years (63% men), and 73 healthy controls with mean age: 66.3 years (55% men) datasets are used in this work. Proposed RBF network is trained on VGRF signals obtained in normal walking and dual tasking conditions from control. The network was trained with 60% of VGRF data and tested on remaining 40% data. Semblance analysis results are encouraging, and it shows that semblance is high in PD patients than control subjects during dual tasking (P < 0.05). In order to test the findings of semblance analysis, we explicitly reconstruct VGRF signal of clinically significant dual tasking from VGRF signal of normal walking by the proposed RBF method. Findings proved that the proposed RBF network can reconstruct dual tasking VGRF signal of PD patients from their normal walking VGRF signal with high cross correlation (P < 0.0001). These findings pave way for a new adjunct tool to diagnose the gait dynamics of PD patients using the proposed reconstruction method.
In this paper, a numerical estimation of wall shear stress (WSS) in a compliant Thoracic Aorta (TA) with aneurysm is modeled and the hemodynamic pattern is studied using Computational Fluid Dynamics (CFD). Thoracic Aortic Aneurysm (TAA) is an excessively localized enlargement of TA caused by weakness in the arterial wall and it can rupture the inner wall intima and continue on to the outer wall adventitia. WSS is a tangential force exerted by blood flow on the vessel wall, and its estimation is clinically very important because any change in WSS is considered as a vital cue in the onset of aneurysm. In this work, a three-dimensional (3D) model of a TAA reconstructed from computed tomography (CT) images comprising of 600 slices with 1-mm resolution from neck to hip is considered and patient-specific simulations have been carried out in compliant TA under rest and exercise conditions. The findings show that the change in wall geometry was marginal due to variation in pressure forces inside and is not the primary source for expansion of an aneurysm. It was inferred that expansion was rather due to thinning of the wall, owing to damage caused to the inner lining of the tissues, at regions of high WSS. It was found that the geometry extraction is important as any change in length causes a corresponding variation in mass flow through it. Although mass conservation is maintained irrespective of the length, it does affect the rate of flow due to shifting in the pressure boundary conditions with the length as it varies the pressure inside the system. Modeling of the geometry is very important as the change in mass flow will affect the outlet velocity and strength of vortices. Surprisingly, the split-up of flow is consistent but the geometric change in the model has no effect on WSS values and flow pattern. The results of this study provide important information such as blood flow pattern and pressure drops in the compliant TA on WSS estimations with TAA diseases.