Hello Archith,

The biosignals, say for example ECG, originate from the body potentials whereas the noise is mainly from the man-made electrical sources. 50-60Hz Power-line frequencies are the worst enemies in bio-potential amplification/processing so you need to filter out these first. Usually a notch filter is applied to remove these power-line components and a low pass filter is used to restrict other high frequency signals entering into your system. This is needed particular to avoid the aliasing effect (filter act as anti-aliasing) in sampled systems with digital processing of the signals.

The final algos you apply depend on what type of signals you are looking at and what data or information you need to extract from them.

Hope that I have tried to address your question in a proper way!

Thank You Sir,for the reply.An additional query:But apart  from extraneous sources aren't there sources of noise within the system(human body) itself?

Archith,

Yes, there are but I can only comment after having understood your exact setup or experimental requirement.

Thanks

Well I have to apologize Sir.The question posed was general.

Hello Archith,

There are some biosignals in which noise and signal can be differentiated easily, for example: ECG, PPG . These signals have a ideal or say definite waveform, others are noise, mainly power line interference which can be removed by notch filters.

These two signals can be easily filtered and separated from noise.

Hope this would help.

Thanks 

Hello Archith,

Based on your comment about sources of noise from within the human body itself, I wonder if you are referring to the predictability of biosignals.  For example,  ECGs are summations of synchronized individual muscle cell firings and you have a nice, repeatable ECG signal.  An EMG (electromyography) signal is also the summation of individual muscle cell firings, but the individual muscle cells are not synchronized to one another and the signal looks "noisy."   However, there is still much information that can be obtained from an EMG signal.  Is this what you are referring to?

Cheers

Alan

Thank You Sir.

Yes that was what I was referring to.Since the ECG signal is well studied over the years and  well understood,it can be very well correlated to the underlying physiological phenomenon.But how well have we been able to understand other signals,provided that  human body performs a variety of processes simultaneously.

An EE professor of mine defined noise as, essentially, everything picked up by a sensor that isn't the desired signal.  So to answer your general question: no, there is no clear cut distinction between signal and noise in most applications.  Event-related neural signals, for example, will generally be inundated by neural and other biological events - some independent of the signal of interest and some not - that will reduce the signal-to-noise ratio, even after filtering and averaging.

Thank U Sir

Hi Archith,

Other than mister Waichal and mister Oslan I would like to differentiate between noise and artefacts. Movement distortion, 50hz/60Hz powerline interference, EMG in ECG or any other physiological signal on top of the one you're interested in:  they are all artefacts for which you can shield,  compensate, filter or reduce (at least to a certain extent).

The inherent noise is partly in your body, but mostly in your measurement system (thermal resistor noise, amplifier noise, 1/f etc.)

In practice you cannot always make a distinction between noise and artefacts, but you can establish a baseline for your setup with a measurement on some dead meat, like a dead chicken. Everything on top of that is something physiological.

For what its worth: for a well designed measurement system powerline interference is almost only  Common Mode, a good Common Mode Rejection Ratio (CMRR) will yield you good clean signals.  

I'm a hardware development engineer for such measurement systems.

Thank you,Sir,for the reply.

The first thing to identify is the bioelectrical signal which will work, morphological features, plus the frequency features, has also to be clear the characteristics of the electronic device with which these signals were obtained and depending of this, will be the technique should be used to determine which part of the record is signal and what is noise. In the case of Evoked Potentials, which are the answers produced by the brain and are obtained from EEG recording, in this case the record EEG itself is considered noise and should be removed, also considered noise eye movement, the noise produced by the muscles of the body and elk provided by the line of 60 Hz. in the case of removing the EEG signal can not be performed by filtering because the spectral components of evoked potential overlap with the EEG register, then classical technique is recommended as the coherent averaging. This evidence example, you perform the analysis for a signal does not have to correspond exactly with which you make to another signal, you must be clear about its features

Thank You Ma'am,for the reply.

Dear Archith

Your question has different answers in different field of processing, However, I recommend you in first step study previous works related to your field of research, then guess the frequency band which is important to you then filter rest of bands.

for instance, I can point out on my work, which was related to Parkinson patients tremor, based on previous works their frequency is about 3-12 Hz, so I passed these frequency band and filtered rest of bands then I implemented my processor based on features which had been extracted in this frequency band.

Thank you,Sir,for the reply