Why do biomedical signals need processing?
Bioengineering is important for medicine because it provides tools and methodologies that can support the doctor in diagnosing, monitoring particular parameters, and understanding certain problems, through the use of mathematical models. In general, it is that sector that integrates the knowledge and methods of engineering with the problems of medicine, that is, the application of mathematical models and technologies to the medical-biological model. And biomedical signal is the important part of it.
A signal describes the variation of a given quantity as a function of other variables, which can be time, space, or both. We talk about the biomedical signal when the source that generates these quantities is the living organism.
The measurement of these quantities, the acquisition, and analysis of these signals is vital in the field of medicine because it provides useful information for the diagnosis, rather than for monitoring therapeutic treatment. It can provide additional information to the specialist for a better understanding of what the physical, chemical, and biological processes under examination are.
Classification of biomedical signals
A first distinction had been made between:
- Signals spontaneously generated by the body
- Evoked signals, which instead are signals that our organism does not spontaneously generate but either if it receives a stimulus from the outside or if a certain external agent interacts with it.
Another classification is related to the dependence of our greatness. We talk about:
- Varying time signal when our signal, our magnitude varies as a function of time.
- Spatial signal if instead there is a variation of the observed quantity as a function of space.
- Spatiotemporal signal if there is a double dependence, both in time and in space.
Signals can be classified according to their origin. We talk about:
- Electrical bio-potentials (ECG, EEG, and EMG): They are all potentials generated by the onset of an action potential, depolarization, and repolarization of the cell membrane.
- Chemical signals
- Mechanical signals
- Thermal signals
Beyond their origin, it is possible to classify the properties of the signals based on their mathematical characteristics. For example, a first distinction concerns:
- Time-continuous signals: in which the magnitude we are measuring varies continuously with the passage of time; that is, for each instant of time, a measurement is made, and the value of that quantity is known.
- Discrete-time signals: In this case, the measurement is carried out only in specific instants of time, interspersed with each other; it is not a time-continuous measurement. Generally, these are quantities that vary slowly or are quantities for which it is not necessary to carry out that type of measurement.
- Deterministic signals: a signal is said to be deterministic when the value that the signal assumes in a certain time interval can be calculated through, for example, a mathematical equation. Or it can also be extrapolated based on the measurements we have made up to that moment. So deterministic because it is almost "predictable."
- Periodic signals: A particular class of deterministic signals. Periodic signals are signals whose waveform repeats after a certain period of time. This time interval is called a period, and the inverse of the period is called a signal frequency. In medicine, some signals have deterministic characteristics; in some cases, even periodic, for example, the voice has a behavior that in physiological conditions, we can define almost periodic.
In any case, most of the signals in the biomedical field are not deterministic, even not periodic, but are random or, in any case, have an almost random behavior.
- Random signals: random signals are those signals for which it is difficult to predict what the value will be that the signal will assume in the following instants of time up to those we have observed. So, in fact, there is no mathematical equation that describes the trend of the signals. Although taking measurements, while going to acquire the signal for a certain period of time, from the knowledge of the samples that we have acquired, we cannot reconstruct the signal in the following instants; so if we want to share information, we must necessarily acquire the signal and study the signal equation that we have acquired.
For example, the electroencephalographic signals, the electromyographic signal, are non-deterministic signals and therefore fall into the class of random signals.
In general, the concept of frequency is associated with the rapidity of variation of the signal, a signal that varies slowly has a lower frequency, a signal that varies rapidly has a higher frequency. And they have different frequency values within the same group of variables the signals. It can be higher frequencies and lower frequencies. The unit of measurement for frequency is the hertz.
So why is it important to know the measurement range and frequency of a signal?
For example, if a diagnosis has to be made, if there are normal ranges, one understands whether or not it actually falls. Furthermore, the frequency and the amplitude are the information characterizing the nature of the signal. Therefore it is essential to know it not only to derive the information the specialist needs from the acquired signals but also before making a measurement. As it is true, that has a ready-made tool, prepared ad hoc to carry out that measurement, but in fact, in the field of measurements, generally, those who must measure a certain size must have an idea of what is going to be measured.
To have these ideas, he must know what the variation range is in terms of the amplitude of the signal magnitude, rather than the frequency value. Because there are instruments that can acquire only quantities with certain variation ranges rather than certain frequencies and those instruments, do not appreciate other values , which instead could be the values of interest.
Therefore it is also useful in choosing the instrumentation to understand if the measuring instrument available can monitor the quantity of interest. However, it would help if you always had an initial idea of the characteristics of what you are measuring. Then this information is necessary to make appropriate considerations on the variations associated with diseases or physiological conditions.