Brief Introduction to Biosignals

This tutorial introduces briefly the concept of biosignals that can be measured through your Arduino!

The term Biosignal refers to all the signals that are being generated in the human body or any other living organism or more specifically it is used to represent all those signals from living organisms that are monitored to obtain certain useful information. Primarily, the term refers to signals that are electrical in nature but, some non-electric signals are monitored as well. Typically, the changes in potential difference across a certain tissue in the body are measured in case of bio-electric signals.

A broad definition of a signal is a ‘measurable indication or representation of an actual phenomenon’, which in the field of biosignals, refers to observable facts or stimuli of biological systems or life forms. In order to extract and document the meaning or the cause of a signal, a physician may utilize simple examination procedures, such as measuring the temperature of a human body or have to resort to highly specialized and sometimes intrusive equipment, such as an endoscope. Following signal acquisition, physicians go on to a second step, that of interpreting its meaning, usually after some kind of signal enhancement or ‘pre-processing’, that separates the captured information from noise and prepares it for specialized processing, classification and decision support algorithms.

Biosignals require a digitization step in order to be converted into a digital form. This process begins with acquiring the raw signal in its analog form, which is then fed into an analog-to-digital (A/D) converter. Since computers cannot handle or store continuous data, the first step of the conversion procedure is to produce a discrete-time series from the analog form of the raw signal.

 (source Wikipedia)

This step is known as ‘sampling’ and is meant to create a sequence of values sampled from the original analog signals at predefined intervals, which can faithfully reconstruct the initial signal waveform. The second step of the digitization process is quantization, which works on the temporally sampled values of the initial signal and produces a signal, which is both temporally and quantitatively discrete; this means that the initial values are converted and encoded according to properties such as bit allocation and value range. Essentially, quantization maps the sampled signal into a range of values that is both compact and efficient for algorithms to work with.

The most popular biosignals used by physicians for medical assessment and also utilized by medical and health applications are the followin:

Biomedical Measurements (Broadly Used Biosignals) Voltage range(V) Number of sensors

Information rate

(b/s)

ECG 0.5-4 m 5-9

15000

Heart sound Extremely small 2-4

120000

Heart rate 0.5-4 m 2 600
EEG 2-200 μ 20 4200
EMG 0.1-5 m 2+ 600000
Respiratory rate Small 1 800
Temperature of body 0-100 m 1+ 80

In case of Electroencephalography, the activity of the human brain is monitored. Usually, at a synapse (junctions between the cells of the nervous system), flow of ions takes place. This results in the formation of various signals that are used by the body to transfer information. The voltage variations that are caused by these signals are recorded and thus, the activity of the brain is measured.
In Magnetoencephalography, the magnetic fields that that are produced by the variations of electric currents that form the brain activity are monitored. For this, devices such as SQUID (Superconducting Quantum Interface Devices) are used as they have high sensitivity. A large number of difficulties are faced one tries to measure the same as the ambient magnetic noise in urban areas is quite high. Even with the use of SQUID, about 50 thousand neurons need to be active at a given moment in order to measure the field.

Galvanic Skin Response technique is used to measure the electric resistance offered by the skin. In it first, a random area of the skin is selected to measure establish the base resistance and then, the area in concern is studied in reference to the data that has already been obtained. As emotional arousal increases skin activity, it is generally used in polygraph tests.

The Electrocardiograph is produced by recording the electrical waves that are produced by a person’s heart. The human heart produces these waves when it pumps blood into the body. These waves are measured by electrodes that are attached to the person’s skin. The rhythms that are produced by these heart beats are analysed in order to detect weaknesses in the heart muscles or some other abnormalities.

Electromyography is similar to Electrocardiography but, it is used for any muscles in the body and not the heart muscles in particular. Similar to the heart muscles, the muscles in other parts of the body also produce electric waves when they contract. Their abnormal activity can be detected by the fluctuations in the graph that are recorded.

In addition to the latter biosignals, patient physiological data (e,g., body movement information based on accelerometer values), and context-aware data (e.g., location, environment and age group information) have also been used by health applications for determining the patient status and identifying emergency cases (e.g., falls, etc.).
You can find more information about biosignal acquisition and processing for building pervasive healthcare systems in my book chapter: “Intelligent Pervasive Healthcare Systems“.

2 comments on “Brief Introduction to Biosignals

  1. RaveendrababuC on said:

    Dear Sir,
    I am Raveendrababu Chintalapudi doing M.tech project based bio-signals analysis. I need some information regarding bio-signals i.e what are sampling rates of all bio-signals and what are voltage range of PPG signals and biosignals?

    • Hi Raveendrababu,

      The sampling rate is not something fixed, it really depends on the signal itself and on the hardware you are using to acquire it. Let’s say that you use your Arduino and a custom 3-lead ECG circuit, the maximum Arduino sampling rate (aprox. 16MHz) is good enough to receive the full ECG waveform but that might not be enough for acquiring and EEG signal. In the case of the ECG, if you lower the sampling rate the less resolution on the signal you will get. E.g., sampling every 300msec will only give you the peaks of the signal, which more or less will be the heartbeats.

      By voltage range you mean the voltage the various sensors generate? That is usually between 3.3-5V.

      Hope to have answered your question!

      Ch.

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