For decades now, electrocardiography (ECG) has been a crucial tool in medicine. And with wearable ECG devices making their way into clinical settings, the amount of ECG data available will continue to increase1. Automatic detection of heart beats (R peaks, QRS complexes) is an important step in ECG analysis. In this post, we will compare some of the libraries you may come across when looking for a ready-to-use solution in Python.

What many datasets for machine learning tutorials are lacking is metadata. Example datasets are, with good reason, kept as simple as possible and only capture the core of the problem. In the real world however, a business goal very rarely comes as one neat pack of uniform data. You may have CT images obtained with different scanners, manufacturing data from different facilities, sales data including different marketing campaigns… When working on a particular task, you want to be aware of such information, as disregarding it may lead to unexpected model behavior.

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.