4 research outputs found
A Sequence Agnostic Multimodal Preprocessing for Clogged Blood Vessel Detection in Alzheimer's Diagnosis
Successful identification of blood vessel blockage is a crucial step for
Alzheimer's disease diagnosis. These blocks can be identified from the spatial
and time-depth variable Two-Photon Excitation Microscopy (TPEF) images of the
brain blood vessels using machine learning methods. In this study, we propose
several preprocessing schemes to improve the performance of these methods. Our
method includes 3D-point cloud data extraction from image modality and their
feature-space fusion to leverage complementary information inherent in
different modalities. We also enforce the learned representation to be
sequence-order invariant by utilizing bi-direction dataflow. Experimental
results on The Clog Loss dataset show that our proposed method consistently
outperforms the state-of-the-art preprocessing methods in stalled and
non-stalled vessel classification.Comment: 5 pages, 4 figure
A CNN based Multifaceted Signal Processing Framework for Heart Rate Proctoring Using Millimeter Wave Radar Ballistocardiography
The recent pandemic has refocused the medical world's attention on the
diagnostic techniques associated with cardiovascular disease. Heart rate
provides a real-time snapshot of cardiovascular health. A more precise heart
rate reading provides a better understanding of cardiac muscle activity.
Although many existing diagnostic techniques are approaching the limits of
perfection, there remains potential for further development. In this paper, we
propose MIBINET, a convolutional neural network for real-time proctoring of
heart rate via inter-beat-interval (IBI) from millimeter wave (mm-wave) radar
ballistocardiography signals. This network can be used in hospitals, homes, and
passenger vehicles due to its lightweight and contactless properties. It
employs classical signal processing prior to fitting the data into the network.
Although MIBINET is primarily designed to work on mm-wave signals, it is found
equally effective on signals of various modalities such as PCG, ECG, and PPG.
Extensive experimental results and a thorough comparison with the current
state-of-the-art on mm-wave signals demonstrate the viability and versatility
of the proposed methodology.
Keywords: Cardiovascular disease, contactless measurement, heart rate, IBI,
mm-wave radar, neural networkComment: 13 pages, 10 figures, Submitted to Elsevier's Array Journa