![]() You will have to find a compromise for your FFT length between enough frequency resolution and enough time resolution. This represents a certain length of time (1024 samples at 1kHz sampling rate is 1024 milliseconds.) If the time period of your FFT is longer than your signal features then they will all be smeared together - you won't be able to identify the features. An FFT will be calculated for a specific number of samples. ![]() Your FFT should be shorter than the features you are trying to detect.Normal heart sound: Atrial spatial defect heart sound: Late aortic stenosis: The samples themselves can be found here: Washington University: Heart sound samples FFT magnitude graphs are showing somewhat recognizable results, but i think there is too much unneeded information there and they are not so distinct. I have very limited knowledge in this field and not sure about what options are available to me. Also normally S1 has a larger amplitude spike than S2 as i understand. two large fluctuations normally, with slight delay between them and if person is sick it may be either something before S1 or between S1 and S2 or S1 may double a bit. My question is, what kind of processing should i perform to surely extract and recognize those features, i.e. The length of the whole cycle (S1, S2) may vary depending on the heart rate. Depending on a given heart condition S1 may have doubling and there may be some "noisy" sounds (clicks also) between S1 and S2. These two fluctuations are named S1 and S2 respectively and they are my main subject of interest. Normal one and one of the person sick with atrial spatial defect, third one is of person with late aortic stenosis. These sounds are only samples i've found, but the final signal will be probably a bit noisier (maybe not, i don't know yet). I need to identify certain features of the audio signal recorded from microphone in stethoscope.
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