yumi_popo's starred repositories
Sparse-ECG-denoising
Sparsity based ECG denoising
adaptive_sg_filter
Code for the article "Adaptive Savitzky-Golay Filtering in Non-Gaussian Noise"
Savitzky-Golay-Filter
Consists of Matlab Algorithms necessary for denoising ECG signals.
ecg-denoising-matlab
This code is can be used for ECG signal denoising. The data that being used is from MIT-BIH Arrhythmia database. The ECG signal is denoised using 3 filters: 2 median filters with 600ms and 200ms sliding window, and 12-order FIR filter with 35 Hz cut-off frequency. Reference: https://www.sciencedirect.com/science/article/abs/pii/S0167739X17324548
Real-Time-P-QRS-and-T-Wave-Detection-by-QRS-Matched-Filter-Method
This was my thesis topic in undergrad when I tried to track the heart beats from raw ECG signals. The intention was to build a model that can perform faster, very lit and very simple and also real time. I had done this on MATLAB.
wavelet-ica
Wavelet-enhanced ICA for EEG artifact removal
easy_ica
Python Module for performing Independent Component Analysis (ICA) using maximum likelihood, square mixing matrix and no noise (Infomax), based on a implementation in Matlab of Thomas Kolenda in their version 1.5. Implemented in the Group of Biomedical Data Processing at the University of Holguin.
Robust-DL-pipeline-for-PVC-localization
Premature ventricular contraction(PVC) is among the most frequently occurring types of arrhythmias. Along with other cardiovascular diseases, it may easily cause hazardous health conditions, making PVC detection task extremely important in cardiac care. However, the long-term nature of monitoring, sophisticated morphological features, and patient variability makes the manual observation of PVC an impractical task. Existing approaches for automated PVC identification suffer from a range of disadvantages. These include domain-specific handcrafted features, usage of manually delineated R peaks locations, tested on a tiny sample of PVC beats(usually a small subset of MIT-BIH database). We address some of these drawbacks in proposed framework, which takes a raw ECG signal as an input and localizes R peaks of the PVC beats. It consists of two neural networks. The first one is an encoder-decoder architecture that localizes the R peak of both Normal and anomalous heartbeats. Provided R peaks positions, our CardioIncNet model, adopted for ECG signal data, does the delineation of healthy versus PVC bits. We have performed the extensive evaluation of our pipeline with both single- and cross-dataset paradigms on three public datasets. Our approach results in over 0.99 and 0.979 F1-measure on both single- and cross-dataset paradigms for R peaks localization task and above 0.96 and 0.85 F1 score for the PVC beats classification task.
Medical-Management-System
C#+ SQL Database Project
ECG-acquisition-classification
Single Lead ECG signal Acquisition and Arrhythmia Classification using Deep Learning
Sensor_ECG_PPG_MAX86150_Dev-Board
Arduino based sensor learning project | MAX86150 | PPG | ECG | ESP8266
SA-ECG_AD8233_Single-Arm_ECG
Sensor learning project | AD8233 | ECG | ESP8266
LearningNote
C++和Linux学习笔记
SignalProcess
C++ 实现的信号处理测试用的程序
ECG-analyzer
Main ECG-analyzer repository
arduino-ecg
Event based processing for ECG waveforms - peak detection and heart rate calculation
audioSignalProcess
Some DSP algorithm implementation