This educational repository focuses on working with three types of medical data: tabular data, ECG and EEG signals. It provides implementations of machine learning and deep learning models for processing and analyzing these medical data, with practical projects based on recent research articles.
Additionally, you can refer to the table below to access related instructional videos on YouTube.
| Section | Educational Video | Link | Description |
|---|---|---|---|
| Pandas, Matplotlib, Numpy & Scikit-learn | YouTube | Link | Covers the four main Python libraries in machine learning, along with a project on regression using tabular medical data. |
| Ensemble Classifier - ECG Arrhythmia Classification | YouTube | Link | ECG Arrhythmia classification in ECG signals using ensemble learning with SVM and Random Forest models. |
| EEGNet - Motor imagery Classification | YouTube | Link | Identification of five motor imagery tasks for hands, feet, and tongue using the convolutional model EEG-Net. |
| Title | DOI |
|---|---|
| Ensemble classifier fostered detection of arrhythmia using ECG data | 10.1007/s11517-023-02839-6 |
| Improving Motor Imagery EEG Classification Based on Channel Selection Using a Deep Learning Architecture | 10.3390/math10132302 |
| A large electroencephalographic motor imagery dataset for electroencephalographic brain computer interfaces | 10.1038/sdata.2018.211 |
| EEGNet: A Compact Convolutional Neural Network for EEG-based Brain-Computer Interfaces | 10.1088/1741-2552/aace8c |
| Title | Link |
|---|---|
| Diabetes | scikit-learn |
| MIT-BIH Arrhythmia | physionet |
| Kaya Motor Imagery | figshare |