Covid knocked me out quite good. I contracted the virus right at the start of summer and spent more than a week in bed – with aching limbs, a sore throat and body temperature almost reaching 40°C. Immediately after the first positive antigen test I decided to monitor my body more closely and generate some data to play with later. This post summarizes what I have learned through making a data project out of my Covid infection.
I love Streamlit. It is an amazing tool, to quickly create interactive data apps. In data science, it is often beneficial to get first results early and then improve iteratively. Making data available and accessible to domain experts is an important step in that journey. With Streamlit, it is straightforward to build custom applications. Apps can easily be tailored to specific data science projects. But with a few tricks, they can also be made more generally applicable.
Looking to find peaks in ECG? There is no need to reinvent the wheel. A number of great libraries may provide what you need. Check out my comparison of ECG peak detection libraries in Python. In many signal processing applications, finding peaks is an important part of the pipeline. Peak detection can be a very challenging endeavor, even more so when there is a lot of noise. In this post, I am investigating different ways to find peaks in noisy signals.