well, in few words, rather I don't understand how LSTM could help you at all.

Let's talk in terms of second FFT transform: at what frequencies your learnable signal mostly is?

Here as i undestand you wanna say the data frequecy is too much i should downsample it and beside that instead of using only one time frame it might be better to merge several neighber time frame and then feed the network am i right?

Again, for a comparison with sounds, frequencies longer than 1 Hz are of no interest.

Uhu so i was understang wrong my sampling rate is 1khz

These are two alternatives to consider:
learning each second of data independently (like if there's no generalizable signal in frequencies longer than 1Hz),
and the second alternative -- to try how those frequencies are useful, making a coarse grain for your time data.

That's before the first FFT. And I believe you said that you have then frames of 40 ms, and that you have a label once per second or so.

aha, no, you have a label for each 40 ms and data for each 1 ms.

Would you suggest your preference model instead of lstm? Im not rigid for it and i can try other model as well im usi g rnn based because it's more match with sequence based data

CNN I think. And a FFT maybe before a CNN.

Yes its like (40sample x, 2 lables y)

actually i have used some calculated feature on this 40 ms time frame like FFT and used them instead of raw data and i have implied even Conv1d for them and eventually i saw when i mix it with lstm it gaves me a slightly better result

Also consider different preprocessing and find out the best. You can try a fixed model like CNN+FC layers and compare different preprocessing before it first of all.
For the sounds, scientists found the best preprocessing a long time ago and training NN to reproduce it as its part isn't rational because this makes the learning much slower and much more data is needed to learn it.

That sounds nice to check at first the local pattern matching and then maybe implying temporal model for temporal pattern👍👍

yeah, indeed

so a good research would look like finding a good combination of the parameters for the following:
0) choosing a baseline performance for your study (and optionally perform baseline analysis)
1) preprocessing (no FFT, FFT: hann/hamming, frame size, window size, overlap)
2) architecture: finding the best architecture.
3) learning the possible reasons of overfitting and measuring their impact into the final quliaty.
4) "theoretical maximum" quality, probably a kind of analysys of the data variance across people, data noise (maybe by trying to soften the data) and label noise (how often similar data leads to different labels).
You can take a small part of dataset for most of these studies, so the network would train very fast (in several minutes on modern GPUs).

I'd also suggest to take initial values for all parts from other people's works.

Thanks a lot yuri im thinking now how can i combine raw data and calculated feature at the same time i mean some cnn with several filters for raw data and some domain related fetures like heart rate at same time is it possible or do you recomend it?

yes, absolutely. you can approximate heart beats with a linear, a cosine or an exponential decaying function, I think.

if your heartbeat is exponentially decaying, you've got some problems