Digital Biomedical Signal Processing

Digital Signal Processing (DSP) is an engineering discipline that enjoys many applications, including medicine. Biological signals are data registers that convey information about a biological process in a given period of time (i.e. a heartbeat).  The interpretation of these signals is of significant value for diagnosticians, clinicians and researchers.  DSP has been the basis on which many artificial intelligence and health-oriented technologies have found a way to establish a leading role in biomedical engineering’s evolution.

The human body is a living source of many kinds of signals. Nerve and muscle cells generate electrical signals like an EEG (Electro Encephalograph). Blood flow and breathing produce biomechanical signals that  provide information about respiratory and circulatory systems.  Acoustic signals from a heart valve or a breathing process are also very useful for determining the state of a patient.

When dealing with biomedical signals it is important to classify them as non-periodic (may have patterns, but never fully periodic) and finite signals (it is impossible to register a patient’s data for ever).  This means they must be studied as stochastic signals, with a seemingly aleatory behavior.  This forces biomedical engineers to make use of  advanced DSP theory and concepts in order to obtain the best frequency resolution and event recognition algorithms.

Bio-signals  in the human body are very electrically weak.  This makes them very susceptible to noise. Noise may come from the same electrical devices that are performing the signal acquisition and processing, and also from all the other signals that the human body is emitting.  Muscles, neurons and organs are simultaneously working, so previous knowledge of the signal under analysis  and it’s environment is mandatory.

Adequate hardware selection helps greatly to improve the quality of the signal.  Depending on the signal to be analyzed, the sampling frequency of the Digital Signal Processor must live up to the working frequency of the signal.  Sensor selection and sensitivity is important.  Flux sensors,  microphones, heat sensors, accelerometers and electrodes are commonly used in biomedical research and are available in the market.

It is also important to never underestimate solutions that an intelligent software design has to offer. DSP engineers, through software design, can greatly improve a signal’s quality as well, by using digital noise reduction methods and frequency domain algorithms to the point of even avoiding the need for invasive sensors.

DSP has proven to be so useful and reliable for monitoring patients that most biomedical devices today make good use of this technology to perform real-time and offline analysis of this information.  Patient monitors include automatic control systems based on the analysis of a real-time incoming signal acquired directly from the patient.

Researchers today are exploring new possibilities in the development of new technologies, for example EEG signals (produced by neural activity in the brain) are used for epileptic spikes detection algorithms and brain-machine interfaces for intelligent prosthetics control.  Also, advanced pattern recognition techniques are used for DNA sample analysis, biomedical image processing, and many more applications.

The ever growing and merging fields of science like medicine, biology and engineering, keep opening doors for many  innovations and solutions for today’s obstacles. DSP definitely embraces the possibilities that this merging of knowledge has to offer.

Inband Software can assist you with your biomedical signal processing project.  Our expert staff understands the unique challenges of processing biomedical signals, and can help you select sensors, processors, develop algorithms, and implement and optimize them for your specific hardware.

3 responses to “Digital Biomedical Signal Processing”

  1. Cristian Urena Hdz

    Hi Pablo, good article to describe the fascinating world we are involve with. Although many researchers have gotten promising results there is still a long way to go through.


  2. Pablo Deliyore

    Thanks for your comment Christian! Yes you are right, indeed all the ”futiristic” possibilities that DSP interacting with the human body are still under development and not yet ready for daily life appliance.

    However, remember that most biomedical devices interact with the human body through DSP, and some of those futuristic applications have been finding their way into the market for quite some time now, such is the case of DSP-based implantable pacemakers, which detects arrhythmias by realtime ECG analysis, through event recognition algorythms. Also apnea detection devices based on your breathing cycles. What I am trying to say is that many biosignals from your body are ‘’simple” enough to analyze with a robust algorythm in order to obtain reliable results.

    In the research fields, neural and moto-neural signals are been used to develop human machine interfaces, brain-machine interfaces and intelligent prosthetics. And you are right, there is still a long way to go before we start seeing people using these interfaces on the street on everyday life. As you already know, that is because neural and moto-neural signals are much more complex and challenging signals for engineers to analyze and work on, but that just makes it more exciting for researchers.

    Thank you for your very relevant comment!

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