v3551G

driftDatasets

ArduPi-ECG

Full HRV analysis of Arduino pulse sensor, using Python signal processing and time series techniques. Chaotic, Fourier, Wavelet, Regression, Neural Net.

autoencoding_beyond_pixels

Generative image model with learned similarity measures

basic_NNs_in_frameworks

several basic neural networks[mlp, autoencoder, CNNs, recurrentNN, recursiveNN] implements under several NN frameworks[ tensorflow, pytorch, theano, keras]

Blood_Pressure_Prediction

BloodPressure

Allows to categorize blood pressure types (High, Pre-High, Ideal, and Low) with the use of systolic and diastolic measurements

CNN

Convolutional Neural Network using Eigen and C++

DataDrivenBP

repository sharing research code of paper "Data-Driven Estimation of Blood Pressure Using Photoplethysmographic Signals"

DeepLearing-EEG

Testing models with eeg data

Diabeteshelper

血压及脉搏监测和简单变化趋势预测 APP

ECG-preprocessing

Preprocessing MITBIH Arrhythmia to CinC2017 format

EEG-Classification

This project was a joint effort with the neurology labs at UNL and UCD Anschutz to use deep learning to classify EEG data.

ensembeTimeSeries

Ensemble of ARIMA, prophet and LSTMS RNN

Keras-Tutorials

Keras-Tutorials

mimic-iii-paper

Repository for the paper describing MIMIC-III

Online-learning

oscillometric-blood-pressure

2016 finnal project

PPG

Code to estimate HR from PPG signals using Subspace Decomposition and Kalman filter for the dataset of 22 PPG recordings provided for the 2015 IEEE Signal Processing Cup (SP Cup) competition. The traces are stored in folder 'DATABASE'. Please cite this publication when referencing this material: "Measuring Heart Rate During Physical Exercise by Subspace Decomposition and Kalman Smoothing", IEEE Transaction on Instrumentation & Measurement.

PPG-1

Photoplethysmogram-based Real-Time Cognitive Load Assessment Using Multi-Feature Fusion Model

ppgprocessing

Obtain heart rate and SpO2 level from PPG data

Python-for-EEG-analysis

Currently working on analysis of EEG (Electroencephalography) signals on the emotion recognition and sleep stage classification problems. This package/file is a summary/gathering of the most frequently used packages, functions and papers of EEG analysis in Python.

random-output-trees

Randomized output tree for multilabel / multi-output regression tasks

ResNet

Reproduce ResNet-v2(Identity Mappings in Deep Residual Networks) with MXNet

Roc

Everything ROC and Precision-Recall curves.

sleep_staging_shhs

Code for the paper "A Convolutional neural network for sleep stage scoring from raw single-channel EEG"

stm32_development_on_linux

stm32 development on linux

TCML-tensorflow

Tensorflow implementation of Meta-Learning with Temporal Convolutions

tf-dann

Domain-Adversarial Neural Network in Tensorflow

WFDB-utils-and-others

Scripts to work with WFDB format ECG data