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Found 18 papers, 4 papers with code
Benchmarking changepoint detection algorithms on cardiac time series
This work represents the first time change point detection algorithms have been compared in a meaningful way and utilized in a classification task, which demonstrates the effect of changepoint algorithm choice on application performance.
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.
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.
A Feasibility Study on Indoor Localization and Multi-person Tracking Using Sparsely Distributed Camera Network with Edge Computing
To this end, we deployed an end-to-end edge computing pipeline that utilizes multiple cameras to achieve localization, body orientation estimation and tracking of multiple individuals within a large therapeutic space spanning $1700m^2$, all while maintaining a strong focus on preserving privacy.
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.
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.
Privacy-Preserving Eye-tracking Using Deep Learning
The expanding usage of complex machine learning methods like deep learning has led to an explosion in human activity recognition, particularly applied to health.
Late fusion of machine learning models using passively captured interpersonal social interactions and motion from smartphones predicts decompensation in heart failure
Objective: Worldwide, heart failure (HF) is a major cause of morbidity and mortality and one of the leading causes of hospitalization.
An Analysis Of Protected Health Information Leakage In Deep-Learning Based De-Identification Algorithms
Modern deep learning algorithms have the highest potential of such leakage due to complexity of the models.
Classification of 12-lead ECGs: the PhysioNet/ Computing in Cardiology Challenge 2020
Main results: A total of 217 teams submitted 1395 algorithms during the Challenge, representing a diversity of approaches for identifying cardiac abnormalities from both academia and industry.
Voting of predictive models for clinical outcomes: consensus of algorithms for the early prediction of sepsis from clinical data and an analysis of the PhysioNet/Computing in Cardiology Challenge 2019
Although there has been significant research in boosting of weak learners, there has been little work in the field of boosting from strong learners.
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.
Using Convolutional Variational Autoencoders to Predict Post-Trauma Health Outcomes from Actigraphy Data
The results indicate that the VAE model is a promising approach for actigraphy data analysis for mental health outcomes in long-term studies.
Importance Weighting with a Adversarial Network for Large-Scale Sleep Staging
The proposed method leverages transferable and discriminable knowledge from the training set to the test set.
Temporal-Framing Adaptive Network for Heart Sound Segmentation without Prior Knowledge of State Duration
Moreover, the TFAN-based method achieved an overall F1 score of 99. 2%, 94. 4%, 91. 4% on LEVEL-I, -II and -III data respectively, compared to 98. 4%, 88. 54% and 79. 80% for the current state-of-the-art method.
