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Uses LSTM-based autoencoders to detect abnormal resting heart rate during the coronavirus (SARS-CoV-2) infectious period using the wearables data.

Home Page: https://github.com/gireeshkbogu/LAAD

License: MIT License

Shell 3.85% R 22.29% Python 73.86%
wearables lstm autoencoder heart-rate anomaly-detection-algorithm covid-19 sars-cov-2 coronavirus deep-learning

laad's Introduction

LAAD

LSTM-based Autoencoder Anomaly Detection (LAAD) is primarily developed to detect abnormal resting heart rate (RHR) during the Coronavirus (SARS-CoV-2) infectious period. It uses heart rate and physical activity data from smartwatches like Fitbit, Apple and Garmin. Further, it splits data into training (baseline) and test based on the participant's self-reported symptom date and COVID-19 infectious period. It learns the structure of the baseline data for each user by reconstructing the temporal sequences and calculates a reconstruction error. Using this error it builds an anomaly threshold and detects if the RHR sequences in the test data are anomalous (abnormal) or not.

Link to the research paper - https://www.medrxiv.org/content/10.1101/2021.01.08.21249474v1

Installation

git clone https://github.com/gireeshkbogu/LAAD.git
cd LAAD
pip install -r dependencies.txt

Usage

python laad_covid19.py  --heart_rate data/ASFODQR_hr.csv --steps data/ASFODQR_steps.csv --myphd_id ASFODQR --symptom_date 2024-08-14

LAAD architecture

LAAD

Results

Reconstruction error (loss)

s_training s_pred_loss

Predictions

s_results

laad's People

Contributors

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laad's Issues

Regarding the labels (symptom and diagnosis dates)

Dear sir/madam,

thanks so much for your great work and I am doing some research in this domain as well at West Virginia University. I couldn't find the symptom and diagnosis dates for the subjects so that I can train models on them. Could you kindly help?

Best regards,
Omid

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