Preterm babies in the Neonatal Intensive Care Unit (NICU) have to undergo continuous monitoring of their cardiac health. Conventional monitoring approaches are contact-based, making the neonates prone to various nosocomial infections. Video-based monitoring approaches have opened up potential avenues for contactless measurement. This work presents a pipeline for remote estimation of cardiopulmonary signals from videos in NICU setup. We have proposed an end-to-end deep learning (DL) model that integrates a non-learning-based approach to generate surrogate ground truth (SGT) labels for supervision, thus refraining from direct dependency on true ground truth labels. We have performed an extended qualitative and quantitative analysis to examine the efficacy of our proposed DL-based pipeline and achieved an overall average mean absolute error of 4.6 beats per minute (bpm) and root mean square error of 6.2 bpm in the estimated heart rate.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1109/EMBC48229.2022.9871025 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Artificial Intelligence in Detecting and Segmenting Vertical Misfit of Prosthesis in Radiographic Images of Dental Implants: A Cross-Sectional Analysis.
Clin Oral Implants Res
January 2025
Department of Oral and Maxillofacial Radiology, School of Dentistry, Kashan University of Medical Sciences, Kashan, Iran.
Objective: This study evaluated ResNet-50 and U-Net models for detecting and segmenting vertical misfit in dental implant crowns using periapical radiographic images.
Methods: Periapical radiographs of dental implant crowns were classified by two experts based on the presence of vertical misfit (reference group). The misfit area was manually annotated in images exhibiting vertical misfit.
Improved Efficacy of Triple-Negative Breast Cancer Immunotherapy via Hydrogel-Based Co-Delivery of CAR-T Cells and Mitophagy Agonist.
Adv Sci (Weinh)
January 2025
Department of General Surgery, Tangdu Hospital, Air Force Medical University, Xi'an, 710038, P. R. China.
Leaky and structurally abnormal blood vessels and increased pressure in the tumor interstitium reduce the infiltration of CAR-T cells in solid tumors, including triple-negative breast cancer (TNBC). Furthermore, high burden of tumor cells may cause reduction of infiltrating CAR-T cells and their functional exhaustion. In this study, various effector-to-target (E:T) ratio experiments are established to model the treatment using CAR-T cells in leukemia (high E:T ratio) and solid tumor (low E:T ratio).
View Article and Find Full Text PDFYOLOv7-DWS: tea bud recognition and detection network in multi-density environment via improved YOLOv7.
Front Plant Sci
January 2025
College of Engineering, South China Agricultural University, Guangzhou, China.
Introduction: Accurate detection and recognition of tea bud images can drive advances in intelligent harvesting machinery for tea gardens and technology for tea bud pests and diseases. In order to realize the recognition and grading of tea buds in a complex multi-density tea garden environment.
Methods: This paper proposes an improved YOLOv7 object detection algorithm, called YOLOv7-DWS, which focuses on improving the accuracy of tea recognition.
A customized convolutional neural network-based approach for weeds identification in cotton crops.
Front Plant Sci
January 2025
Information and Communication Engineering, Yeungnam University, Gyeongsan, Republic of Korea.
Smart farming is a hot research area for experts globally to fulfill the soaring demand for food. Automated approaches, based on convolutional neural networks (CNN), for crop disease identification, weed classification, and monitoring have substantially helped increase crop yields. Plant diseases and pests are posing a significant danger to the health of plants, thus causing a reduction in crop production.
View Article and Find Full Text PDFRecent advances in deep learning and language models for studying the microbiome.
Front Genet
January 2025
Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, United States.
Recent advancements in deep learning, particularly large language models (LLMs), made a significant impact on how researchers study microbiome and metagenomics data. Microbial protein and genomic sequences, like natural languages, form a , enabling the adoption of LLMs to extract useful insights from complex microbial ecologies. In this paper, we review applications of deep learning and language models in analyzing microbiome and metagenomics data.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!