Koldim2001/time_series_theory
Решение задач по анализу временных рядов: детекция пиков QRS на сигналах ЭКГ с помощью ML, прогнозирование заболеваемости COVID с помощью LSTM и др.
This project helps medical professionals or researchers analyze electrocardiogram (ECG) signals by detecting QRS complexes, which are crucial for heart rhythm analysis. It takes raw ECG data and outputs classifications indicating the presence of a QRS peak. It also offers tools for forecasting disease incidence like COVID-19, filtering signals, and analyzing relationships between different time-series datasets, such as vaccination rates and social media activity.
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Use this if you need to precisely detect heart activity from ECG signals, forecast trends for public health, or understand causal relationships between different time-series data like economic or social metrics.
Not ideal if you are looking for a complete, production-ready application rather than a collection of problem-solving approaches and examples.
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May 02, 2023
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