https://github.com/arrhythmia-detection/authorfeatureextracteddecisiontreeesp32s3
Deploys a vanilla non-optimized Decision Tree for Arrhythmia classification using Chapman ECG dataset on ESP32-S3 dev kit
https://github.com/arrhythmia-detection/authorfeatureextracteddecisiontreeesp32s3
arrhythmia-classification decisiontreeclassifier eloquent esp32-arduino esp32-s3 scikit-learn
Last synced: about 1 month ago
JSON representation
Deploys a vanilla non-optimized Decision Tree for Arrhythmia classification using Chapman ECG dataset on ESP32-S3 dev kit
- Host: GitHub
- URL: https://github.com/arrhythmia-detection/authorfeatureextracteddecisiontreeesp32s3
- Owner: arrhythmia-detection
- Created: 2025-01-05T11:57:38.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-11T16:35:54.000Z (over 1 year ago)
- Last Synced: 2025-02-28T21:07:32.879Z (over 1 year ago)
- Topics: arrhythmia-classification, decisiontreeclassifier, eloquent, esp32-arduino, esp32-s3, scikit-learn
- Language: C++
- Homepage:
- Size: 21.5 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
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 vanilla [Decision Tree](include/author_provided_feat_dt_v1.h) (*non 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).
