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cnn

Convolutional Neural Network using Eigen and C++

continuous-non-invasive-blood-pressure-research-platform---ecg-and-ppg-pulse-arrival-time-based-

This project is cuffless cNIBP research platform with ECG(two-electrode) and PPG

cuff_less_bp_prediction

Prediction of Blood Pressure from ECG and PPG signals using regression methods.

cufflessbp_dann

data-competition-topsolution

Data competition Top Solution 数据竞赛top解决方案开源整理

data_label_tools

收集整理开源的数据标注工具

data_streams

You will find (about) synthetic and real-world data streams in this repository.

datadrivenbp

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

dataminingalgorithmset

deep-learning-drizzle

Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

deep-learning-with-tensorflow-book

深度学习开源书，基于TensorFlow 2.0实战。Open source Deep Learning book, based on TensorFlow 2.0 framework.

deep_complex_networks

Implementation related to the Deep Complex Networks

deeplearing-eeg

Testing models with eeg data

deeplearning-500-questions

深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为15个章节，近20万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系[email protected] 版权所有，违权必究 Tan 2018.06

deeplearning-models

A collection of various deep learning architectures, models, and tips

deeplearning_ai_books

deeplearning.ai（吴恩达老师的深度学习课程笔记及资源）

deeprnn

long-term blood pressure prediction with deep recurrent neural networks

deepsparsekernel

Multi-output Gaussian process regression via multi-task neural network

diabeteshelper

血压及脉搏监测和简单变化趋势预测 APP

dl4j-examples

Deeplearning4j Examples (DL4J, DL4J Spark, DataVec)

driftdatasets

dsn

Deeply-supervised Nets

ecg-preprocessing

Preprocessing MITBIH Arrhythmia to CinC2017 format

ecg_ptbxl_benchmarking

Public repository associated with "Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL"

ecgppg2art

Predict blood pressure (systolic and diastolic) rythm from ECG and pulse oximeter signals

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.

eeg-rsenet

Motor Imagery EEG Signal Classification Using Random Subspace Ensemble Network

ekg_tda

G-Q-R-S-T indentify. This is an algorithm which detects electrocardiogram features using persistent homology and then measures the PR,QT,ST-interval, QRS-duration, P-wave duration, and T-wave duration based off of the upper and lower time axis bounds of the representative cycles of the detected features within the dataset. Essentially, P, Q, S, and T waves are classified as dimension one homology features with a persistence and center of mass of representative cycle within certain ranges depending on the specific waveform being identified. For more about intuition behind the topological invariants of the data along with how these invariants are used to measure intervals of interest and eventually characterize arrhythmias, see "ekg_feature_extraction_intro.docx"

enhancementing-method-for-stemi-detection

Official source code of Preprocessing Method for Performance Enhancement in CNN-based STEMI Detection from 12-lead ECG

ensembetimeseries

Ensemble of ARIMA, prophet and LSTMS RNN