Feature scaling is an essential part of any machine learning pipeline. In the process of designing our own neural networks designed to interpret EGMs, we have tested various scaling functions on real data, and we present the results below.
No Scaling — This retains the data in its raw format.
Min-Max Scaling — This function scales the maximum value to be 1, and the minimum value to be -1.
Max Abs Scaling — This function scales the maximum absolute value to be either 1 or -1.
Normalization —This function takes the L2 norm of the sample.
Power Transformation — Applies a monotonic transformation to the data. Monotonicity means that the function is always increasing, or always decreasing. In this case, the Yeo-Johnson method is used. …
Today, we had the privilege of observing the implant of one pacemaker and two ICDs at the Philadelphia VA Medical Center. The patients all had different underlying conditions, with each condition treated with a slightly different device and device setup. The patients were awake and anesthetized throughout the procedure.
The purpose of this series on Supraventricular Tachyarrhythmias is to share what I’ve learned so far in my study, in the hopes that it may help someone else, perhaps in getting up to speed on the topic. Quick disclaimer: I’m still a beginner to the world of cardiology and electrophysiologic testing, so this may contain errors. Unless otherwise cited, the content of this series comes from a book entitled “Fogoros’ Electrophysiologic Testing”, by Dr. Richard N. Fogoros and John M. Mandrola.
Supraventricular Tachyarrhythmias are a group of arrhythmias that are non-lethal, and that occur above the ventricles, usually in the atria. They have the effect of increasing the heart rate. These tachycardia can be broadly divided into two classes, automatic and reentrant. …