Electrocardiogram | Chart Egnine 心电图 | Chart library

Sorry for such silly question. I have a df that looks like this:

I want to split tx with data.frame(do.call("rbind", strsplit(as.character(df$tx), "\\s{2,}" )), stringsAsFactors=FALSE) , how can I keep Form in the new df? Also, how can i avoid the split results auto fill if it is empty?

sample df can be build using:

The following code finds the local maxima of a signal, can i find out the local minimas using same code, if not possible then what method should i use?

I have doing some siganl processing and I am new to it. I am using scipy.signal to do the calculations. I am able to find the peak height, width, but I was wondering if I can also find the rise of peak time and decay time. That will be the distance from the left width point to the tallest peak point and then tallest peak point to right width point.

So, far I have this, which is from tutorial

I'm trying to build a 1D CNN with time series. The input is of length 500. There are (only) 2 labels. The architecture which I built so far is the following: there are 3 convolution layers each, of them followed by an activation layer. The first convolution layer takes 50 channels as input.

I am trying to do bit plane decomposition of a 16 bit two's complement signed integer signal data(electrocardiogram) in python so i will get 16 signal data bit plane. I know how to decompose an 8 bit unsigned integer image signal, and i reimplement the code in this problem. I thought that i am supposed to get bit plane data with its values containing negative because it is originaly a 16 bit signed integer, but i got resulting 16 bit unsigned integer signal not 16 bit signed integer signal.
Here's my code:

I am trying to visualize the trajectory of multiple participants in a virtual room using R. I have a participant entering from the right (black square) and moving toward the left, where there is an exit door (red square). Sometimes there is an obstacle right in the middle of the room (circle), and the participant goes around it. To visualize multiple participants’ trajectories on the same graph (i.e., multiple lines), I have used the function plot to set up the plot itself (and the first line) and then I have used the function lines to add other trajectories after that. Below you can see an example with two lines; in the experiment, I have many more (as now I have collected data from about 20 participants.)

I would like to use scipy.signal.find_peaks to find peaks for Value in df as shown below.

df:

I have a .txt file in the sample as below

I have generated a dataframe containing all the possible two combinations of electrocardiogram (ECG) leads using itertools using the code below