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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 22 hours 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 (13 days ago)
Default Branch: master
Last Pushed: 2025-01-11T16:43:21.000Z (11 days ago)
Last Synced: 2025-01-11T17:40:41.432Z (11 days ago)
Topics: arrhythmia-classification, decision-tree-classifier, decision-trees, eloquent, esp32-arduino, esp32-s3, scikit-learn
Language: C++
Homepage:
Size: 8.79 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).