Learning similarities between biomedical signals with Deep Siamese Network

Today, I will walk you through the electrocardiogram (ECG) biomedical signal data with the aim of learning similarity representations between the two recorded signal data events. ECG is one of the most commonly heard types of signal data in context to human medical recordings. So, let's first simply understand what exactly is "Signal" in layman terms, what is an ECG signal, why is it needed, what exactly is Siamese Neural Network, how it can be useful towards comparing two vectors, and finally we will see an use-case starting with the ECG data analysis including uni/multivariate plotting, rolling window sum plots, data profiling, filtering outliers, detecting r-signal-to-signal peaks, and finally identifying the ECG signal similarities with Siamese Network model. The fundamental quantity of representing some information is called a "signal" in simple engineering terms. While in context to mathematical world, a signal is just a function that simply conveys some information, where the information could be a function of time [y y (t) ] or it could be function of spatial coordinates [ y y (x, y) ] or it could be a function of distance from source [ y y (r) ], etc. as an example.