37 research outputs found
MAMAF-Net: Motion-Aware and Multi-Attention Fusion Network for Stroke Diagnosis
Stroke is a major cause of mortality and disability worldwide from which one
in four people are in danger of incurring in their lifetime. The pre-hospital
stroke assessment plays a vital role in identifying stroke patients accurately
to accelerate further examination and treatment in hospitals. Accordingly, the
National Institutes of Health Stroke Scale (NIHSS), Cincinnati Pre-hospital
Stroke Scale (CPSS) and Face Arm Speed Time (F.A.S.T.) are globally known tests
for stroke assessment. However, the validity of these tests is skeptical in the
absence of neurologists. Therefore, in this study, we propose a motion-aware
and multi-attention fusion network (MAMAF-Net) that can detect stroke from
multimodal examination videos. Contrary to other studies on stroke detection
from video analysis, our study for the first time proposes an end-to-end
solution from multiple video recordings of each subject with a dataset
encapsulating stroke, transient ischemic attack (TIA), and healthy controls.
The proposed MAMAF-Net consists of motion-aware modules to sense the mobility
of patients, attention modules to fuse the multi-input video data, and 3D
convolutional layers to perform diagnosis from the attention-based extracted
features. Experimental results over the collected StrokeDATA dataset show that
the proposed MAMAF-Net achieves a successful detection of stroke with 93.62%
sensitivity and 95.33% AUC score
Face2PPG:An Unsupervised Pipeline for Blood Volume Pulse Extraction From Faces
Photoplethysmography (PPG) signals have become a key technology in many fields, such as medicine, well-being, or sports. Our work proposes a set of pipelines to extract remote PPG signals (rPPG) from the face robustly, reliably, and configurably. We identify and evaluate the possible choices in the critical steps of unsupervised rPPG methodologies. We assess a state-of-the-art processing pipeline in six different datasets, incorporating important corrections in the methodology that ensure reproducible and fair comparisons. In addition, we extend the pipeline by proposing three novel ideas; 1) a new method to stabilize the detected face based on a rigid mesh normalization; 2) a new method to dynamically select the different regions in the face that provide the best raw signals, and 3) a new RGB to rPPG transformation method, called Orthogonal Matrix Image Transformation (OMIT) based on QR decomposition, that increases robustness against compression artifacts. We show that all three changes introduce noticeable improvements in retrieving rPPG signals from faces, obtaining state-of-the-art results compared with unsupervised, non-learning-based methodologies and, in some databases, very close to supervised, learning-based methods. We perform a comparative study to quantify the contribution of each proposed idea. In addition, we depict a series of observations that could help in future implementations.</p