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yumi_popo's starred repositories

Sparse-ECG-denoising

Sparsity based ECG denoising

Language:MATLAB100

adaptive_sg_filter

Code for the article "Adaptive Savitzky-Golay Filtering in Non-Gaussian Noise"

Language:MATLABGPL-2.0700

Savitzky-Golay-Filter

Consists of Matlab Algorithms necessary for denoising ECG signals.

Language:Matlab800

HEPLAB

Detection of ECG events (R wave, T wave) for the preprocessing of the heartbeat evoked potential

Language:MATLABGPL-3.02900

ECG_EKRLS

ECG Adaptive Filtering in Matlab

Language:MatlabMIT700

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

Language:MATLAB500

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.

Language:MATLABMIT1800

wavelet-ica

Wavelet-enhanced ICA for EEG artifact removal

Language:MATLAB300

ica

Independent Component Analysis (for blind source separation)

Language:Matlab8400

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.

700

xv6

Language:C24000

STM32F103

STM32F103C8 Examples

Language:C82000

workspace

workspace是基于C++11的轻量级异步执行框架，支持：通用任务异步并发执行、优先级任务调度、自适应动态线程池、高效静态线程池、异常处理机制等。

Language:C++Apache-2.097000

LKPA

Linux操作系统原理与应用第二版改编

11700

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.

Language:Python800