https://github.com/arrhythmia-detection/authorfeatureextracteddecisiontreeoptimizedesp32s3
Deploys an optimized Decision Tree for Arrhythmia classification using Chapman ECG dataset on ESP32-S3 dev kit
https://github.com/arrhythmia-detection/authorfeatureextracteddecisiontreeoptimizedesp32s3
arrhythmia-classification decision-tree-classifier decision-trees eloquent esp32-arduino esp32-s3 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 ESP32-S3 dev kit
- Host: GitHub
- URL: https://github.com/arrhythmia-detection/authorfeatureextracteddecisiontreeoptimizedesp32s3
- Owner: arrhythmia-detection
- Created: 2025-01-09T15:18:42.000Z (4 months ago)
- Default Branch: master
- Last Pushed: 2025-01-11T16:43:21.000Z (4 months ago)
- Last Synced: 2025-01-21T22:14:53.367Z (4 months ago)
- Topics: arrhythmia-classification, decision-tree-classifier, decision-trees, eloquent, esp32-arduino, esp32-s3, scikit-learn
- Language: C++
- Homepage:
- Size: 12.7 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
Optimized Decision Tree Author Extracted Features Deployment on ESP32 S3
Tools
This is a standard [platformio](https://platformio.org/) project for `esp32-s3-devkitc-1`
board which deploys a [Decision Tree](include/optimized_author_provided_feat_dt_v1.h) (*optimized*) to ESP32-S3 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).
