https://github.com/arrhythmia-detection/authorprovidedfeaturescombineddtoptimized
Deploys an optimized Decision Tree for Arrhythmia classification using Chapman ECG dataset on Arduino UNO board
https://github.com/arrhythmia-detection/authorprovidedfeaturescombineddtoptimized
arduino-uno arrhythmia-classification atmega328p chapman-ecg decision-tree-classifier eloquent scikit-learn
Last synced: about 2 months ago
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Deploys an optimized Decision Tree for Arrhythmia classification using Chapman ECG dataset on Arduino UNO board
- Host: GitHub
- URL: https://github.com/arrhythmia-detection/authorprovidedfeaturescombineddtoptimized
- Owner: arrhythmia-detection
- Created: 2025-01-09T11:42:53.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-01-11T16:54:17.000Z (4 months ago)
- Last Synced: 2025-01-21T22:14:53.320Z (4 months ago)
- Topics: arduino-uno, arrhythmia-classification, atmega328p, chapman-ecg, decision-tree-classifier, eloquent, scikit-learn
- Language: C++
- Homepage:
- Size: 15.6 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
Optimized DecisionTree Deployment on Arduino UNO
Tools
This is a standard [platformio](https://platformio.org/) project for `uno`
board which deploys a vanilla [Decision Tree](include/optimized_author_provided_feat_dt_v1.h) (*optimized*) to ATmega328P chip
and collects necessary performance metrics (see [Collected Metrics](CollectedMetrics.md)).
For model training etc., please refer to
[this repository](https://github.com/arrhythmia-detection/ArrhythmiaDetectionModels).
