Bjørn-Jostein Singstad (Bsingstad)

Bsingstad

Geek Repo

Company:Akershus University Hospital

Location:Norge

Home Page:https://bsingstad.github.io/

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Bjørn-Jostein Singstad's repositories

PhysioNet-CinC-Challenge2020-TeamUIO

Public repository associated with: Convolutional Neural Network and Rule-Based Algorithms for Classifying 12-lead ECGs

Language:Jupyter NotebookLicense:Apache-2.0Stargazers:26Issues:2Issues:0

PhysioNetChallenge2021-CNN

Public repository associated with: "Multi-Label ECG Classification Using Convolutional Neural Networks in a Classifier Chain"

Language:PythonLicense:BSD-2-ClauseStargazers:7Issues:3Issues:0

Heart-murmur-detection-2022-Simulab

Public repository associated with: "honocardiogram classification using 1-dimensional inception time convolutional neural network"

Language:Jupyter NotebookLicense:BSD-2-ClauseStargazers:3Issues:2Issues:0

ECG-classification-using-open-data

ECG classification using public data and state-of-the-art 1D CNN models. This work is based on George Moody Challenge 2020

Language:Jupyter NotebookLicense:Apache-2.0Stargazers:2Issues:2Issues:0

Bland-Altman-analysis

BlandAltman plot related to: "Estimation of heart rate variability from finger photoplethysmography during rest, mild exercise and mild mental stress"

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ECG-age

Public repository associated with: "Using deep convolutional neural networks to predict patients age based on ECGs from an independent test cohort"

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ECG-featurizer

A python package for extracting features from ECGs

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ecg_plot

Plot standard multi lead ECG/EKG chart with Python

Language:Jupyter NotebookLicense:MITStargazers:1Issues:1Issues:0

EEG-predict-cardiac-arrest-recovery

Public repository associated with: "MemoryInception: Predicting Neurological Recovery from EEG Using Recurrent Inceptions"

Language:Jupyter NotebookLicense:BSD-2-ClauseStargazers:1Issues:2Issues:0

Pulse2Pulse

DeepFake ECG generator based on 1D Convoloutional Neural Networks

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Strodthoff-2021

[Re] Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL, N. Strodthoff, P. Wagner, T. Schaeffter, and W. Samek,

Language:Jupyter NotebookLicense:GPL-3.0Stargazers:1Issues:0Issues:0

VHDL-lab-using-Quartus

A data lab at UiO (FYS4220, Lab 1, 2019) in VHDL design using Quartus

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DMBA-ML

This repository contains code and course material for Data Mining and Business Analytics at USN

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Bsingstad

A special repository presenting my GitHub profile.

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bsingstad.github.io

A webpage containing information about me and my research projects

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coral-ordinal

Tensorflow Keras implementation of ordinal regression using consistent rank logits (CORAL) by Cao et al. (2019)

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ECG-Age-Bayesian

This work is the preliminary experiments leading to the publication: Towards Invariant Soft Biometrics from Electrocardiograms

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ECG-classification-using-classifier-chain

This repository is associated with Multi-Label ECG Classification Using Convolutional Neural Networks in a Classifier Chain

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ECG-interpretation-flask-application

A Flask application allowing the user to upload a ECG to a webserver and get a prediction back

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fastai

A forked repository - necessary for replication of model presented in: "Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL" and cloud hosted notebooks

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hiPSC-CMs-ionic-current-block-detecion

Identifying ionic channel block in a virtual cardiomyocyte population using machine learning classifiers

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IdrettsEKG

A repository related to a master thesis in electronics, informatics and technology. Title: "Comparing Cardiological and Algorithm-Based ECG Interpretation in Athletes: Can Artificial Intelligence Improve the Algorithms?"

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PhysioNet-Challenge-2024-example-code

Python example code for the PhysioNet Challenge 2024

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Post-George-Moody-challenge-2020-2021

Public repository associated with: Assessing The Effect of Various Sampling Frequency and Double Soft F1-loss in Multi-Label Classification of 12-Lead ECGs

Language:Jupyter NotebookLicense:NOASSERTIONStargazers:0Issues:1Issues:0

releasing-research-code

Tips for releasing research code in Machine Learning (with official NeurIPS 2020 recommendations)

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