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Found 21 papers, 7 papers with code
Learning from Two Decades of Blood Pressure Data: Demography-Specific Patterns Across 75 Million Patient Encounters
Hypertension remains a global health concern with a rising prevalence, necessitating effective monitoring and understanding of blood pressure (BP) dynamics.
Leveraging Large Language Models for Analyzing Blood Pressure Variations Across Biological Sex from Scientific Literature
Based on the automatically-extracted information from these articles, we conducted an analysis of the variations of BP values across biological sex.
Point-of-Care Real-Time Signal Quality for Fetal Doppler Ultrasound Using a Deep Learning Approach
In order to provide instant feedback to allow correction of the data at source, a signal quality metric is required that can run in real-time on the mobile phone.
Beyond Convergence: Identifiability of Machine Learning and Deep Learning Models
However, not all inverse problems in ML are ``identifiable,'' indicating that model parameters may not be uniquely determined from the available data and the data model's input-output relationship.
ECG-Image-Kit: A Synthetic Image Generation Toolbox to Facilitate Deep Learning-Based Electrocardiogram Digitization
We introduce ECG-Image-Kit, an open-source toolbox for generating synthetic multi-lead ECG images with realistic artifacts from time-series data.
A Survey on Blood Pressure Measurement Technologies: Addressing Potential Sources of Bias
Regular blood pressure (BP) monitoring in clinical and ambulatory settings plays a crucial role in the prevention, diagnosis, treatment, and management of cardiovascular diseases.
Electrode Selection for Noninvasive Fetal Electrocardiogram Extraction using Mutual Information Criteria
Based on this idea, in previous works array recording systems and sensor selection strategies based on the Mutual Information (MI) criterion have been developed.
A Data-Driven Gaussian Process Filter for Electrocardiogram Denoising
Objective: Gaussian Processes (GP)-based filters, which have been effectively used for various applications including electrocardiogram (ECG) filtering can be computationally demanding and the choice of their hyperparameters is typically ad hoc.
Beyond Heart Murmur Detection: Automatic Murmur Grading from Phonocardiogram
On the test set, the algorithm achieves an unweighted average of sensitivities of 80. 4% and an F1-score of 75. 8%.
Heart Murmur Detection from Phonocardiogram Recordings: The George B. Moody PhysioNet Challenge 2022
Objective Cardiac auscultation is an accessible diagnostic screening tool that can help to identify patients with heart murmurs for follow-up diagnostic screening and treatment, especially in resource-constrained environments.
Noisy Neonatal Chest Sound Separation for High-Quality Heart and Lung Sounds
The median processing time for the sound separation of a 10s recording was found to be 28. 3s for NMCF and 342ms for NMF.
Mythological Medical Machine Learning: Boosting the Performance of a Deep Learning Medical Data Classifier Using Realistic Physiological Models
Objective: To determine if a realistic, but computationally efficient model of the electrocardiogram can be used to pre-train a deep neural network (DNN) with a wide range of morphologies and abnormalities specific to a given condition - T-wave Alternans (TWA) as a result of Post-Traumatic Stress Disorder, or PTSD - and significantly boost performance on a small database of rare individuals.
Noninvasive Fetal Electrocardiography: Models, Technologies and Algorithms
The fetal electrocardiogram (fECG) was first recorded from the maternal abdominal surface in the early 1900s.
Real-Time Multi-Level Neonatal Heart and Lung Sound Quality Assessment for Telehealth Applications
In this study, a new method is proposed to objectively and automatically assess heart and lung signal quality on a 5-level scale in real-time and to assess the effect of signal quality on vital sign estimation.
Temporally Nonstationary Component Analysis; Application to Noninvasive Fetal Electrocardiogram Extraction
The nonstationarity of the source signals can be used as a discriminative property for signal separation.
The CirCor DigiScope Dataset: From Murmur Detection to Murmur Classification
This is mainly due to the lack of large publicly available datasets, where a more detailed description of such abnormal waves (e. g., cardiac murmurs) exists.
Hardware Synthesis of State-Space Equations; Application to FPGA Implementation of Shallow and Deep Neural Networks
As a case study, it is shown that state-space systems can be used for the systematic implementation and optimization of NNs (as nonlinear and time-varying dynamic systems).
A Hypothesis Testing Approach to Nonstationary Source Separation
The extraction of nonstationary signals from blind and semi-blind multivariate observations is a recurrent problem.
Model-based Prediction and Optimal Control of Pandemics by Non-pharmaceutical Interventions
The control objective is to prescribe quantifiable NPI strategies at different levels of stringency, which balance between human factors (such as new cases and death rates) and cost of intervention per region/country.
Optimization and Control Populations and Evolution
Deep Sequence Learning for Accurate Gestational Age Estimation from a $\$$25 Doppler Device
Assessing fetal development is usually carried out by techniques such as ultrasound imaging, which is generally unavailable in rural areas due to the high cost, maintenance, skills and training needed to operate the devices effectively.
Mathematical Modeling of Epidemic Diseases; A Case Study of the COVID-19 Coronavirus
The outbreak of the Coronavirus COVID-19 has taken the lives of several thousands worldwide and locked-out many countries and regions, with yet unpredictable global consequences.
