YashSharma/MultivariateTimeSeries
Encoding Time Series as Images for Classification using CNNs
This tool helps medical professionals, specifically cardiologists and pulmonologists, analyze complex Cardiopulmonary exercise testing (CPX) data more thoroughly. It takes raw CPX time series measurements from patients and converts them into an image format. This allows for a deeper, more nuanced evaluation of exercise capacity and disease diagnosis than traditional simplified metrics, ultimately helping in the classification of conditions like heart failure and metabolic syndrome.
No commits in the last 6 months.
Use this if you need to analyze detailed cardiopulmonary exercise test (CPX) time series data to diagnose cardiovascular or pulmonary conditions and want to avoid over-simplifying complex trends.
Not ideal if you only need quick peak values or slopes from exercise tests, or if your primary focus isn't on detailed time-series pattern recognition for medical diagnosis.
Stars
15
Forks
4
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Aug 16, 2022
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/YashSharma/MultivariateTimeSeries"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
sktime/sktime
A unified framework for machine learning with time series
aeon-toolkit/aeon
A toolkit for time series machine learning and deep learning
Nixtla/neuralforecast
Scalable and user friendly neural :brain: forecasting algorithms.
tslearn-team/tslearn
The machine learning toolkit for time series analysis in Python
Nixtla/mlforecast
Scalable machine 🤖 learning for time series forecasting.