IEEE EMBS presents

Biomedical Signal Processing

Our bodies are constantly communicating information about our health. This information can be captured through physiological instruments that measure heart rate, blood pressure, oxygen saturation levels, blood glucose, nerve conduction, brain activity and so forth. Traditionally, such measurements are taken at specific points in time and noted on a patient’s chart. Physicians actually see less than one percent of these values as they make their rounds—and treatment decisions are made based upon these isolated readings.
Biomedical signal processing involves the analysis of these measurements to provide useful information upon which clinicians can make decisions. Engineers are discovering new ways to process these signals using a variety of mathematical formulae and algorithms. Working with traditional bio-measurement tools, the signals can be computed by software to provide physicians with real-time data and greater insights to aid in clinical assessments. By using more sophisticated means to analyze what our bodies are saying, we can potentially determine the state of a patient’s health through more noninvasive measures.

Patient › Signals › Processing › Decision

Real-Time Monitoring

Real-time monitoring can lead to better management of chronic diseases, earlier detection of adverse events such as heart attacks and strokes and earlier diagnosis of disease. Biomedical signal processing is especially useful in the critical care setting, where patient data must be analyzed in real-time.
Researchers at the University of Ontario Institute of Technology, working in conjunction with IBM, have created an environment for sophisticated data analysis of every reading from every medical device to support clinical decision-making. A fully functioning pilot program targeting the neonatal intensive care unit (NICU) of The Hospital of Sick Children, Toronto, has been in place since 2009. There, doctors are researching the use of advanced stream computing software for use as an early warning system to alert NICU staff when one of their tiny charges is at risk of developing life-threatening complications. Currently, the streaming environment processes 1256 data points per patient each second, providing constant monitoring of any changes in an infant’s condition.

Cloud Computing

Taking real-time monitoring one step further, these same researchers are testing the application being used in Toronto using cloud computing to provide advanced specialist support for rural and remote communities. The cloud computing approach is currently being tested using data from the NICU of Women and Infants Hospital in Providence, Rhode Island.
Providing a remote database also has implications for telemedicine applications. Real-time embedded signal processing could be programmed onto chips that are part of small, lightweight devices integrated into cell phones or worn by patients (see Wearable & Implantable Technologies) who can be monitored from home.

A Closed System

The human body is comprised of several systems working together in a closed loop and programmed to preserve life. Set points in the brain work to continually monitor and respond to internal and external influences to regulate body temperature. The heart rate varies in response to the autonomic nervous system, which acts as a feedback system, directing the heart to make adjustments according to the body’s level of exertion. Likewise, as a person begins to become ill, the body reacts very subtly. Everything affects something else. And these effects can be measured and interpreted. By doing complex analyses of the body’s signals, we can discover early indicators for how various conditions manifest themselves.

Toward an Understanding of Alzheimer’s

Biomedical signal processing could lead to better and more timely diagnosis and treatment of a disease such as Alzheimer’s. Researchers are combining EEG readings with other testing parameters to try to detect patterns that will distinguish Alzheimer’s patients from those with other forms of dementia. They are focusing on the deteriorating synchronization between the left and right sides of the brain. At present, a definitive diagnosis can only truly be made after an Alzheimer’s patient has died. And most cases are suspected only after the disease has already advanced. Earlier detection could allow for earlier intervention with drugs that slow the progression of the disease.

Eliminating the Guesswork

Our body is our greatest asset. But the current limitations of science and medicine lead to guesswork on the part of physicians. Treatments are often employed in a trial-and-error fashion based upon each physician’s experiences with their own patients. Sometimes doctors do not know whether a patient has gotten better as a result of the body’s own ability to heal itself or through medical intervention.
While the best physician can consider four to seven variables simultaneously, some 20 or 30 different situations could be occurring in a patient’s body at once. So the physician makes an educated guess based on previous experience. By giving the physician better information, they can make better decisions. The more we understand the system, the more we can eliminate the guesswork.
Take the lungs, for example. Lung tissue is just 5 microns thick. If a patient is on a ventilator, a doctor palpating the patient’s chest through their rib cage cannot possibly determine the degree of stiffness of the patient’s lungs. So the extent of ventilation prescribed is estimated and subsequently adjusted based upon how that patient responds. Engineering can help to eliminate—or at least reduce—the amount of trial and error that occurs in millions of real-life patients every day.

Standards and Protocols

To acquire and interpret data from multiple sensors and generate meaningful information, specific protocols and standards need to be established and followed. What are the protocols for data acquisition? How will this data be transmitted and stored? How can it be processed to provide real-time information about a patient’s health? How can this data be mined to discover new information about a particular patient as well as the population at large? Here, again, the hand of the engineer comes into play. But oftentimes our hands are tied.
Many biomedical sensors operate using proprietary protocols that are guarded to protect financial interests. In order to advance our understanding of bodily and disease processes, this will have to change.
The BioSig project is an open source software library that provides software tools for the analysis of many different biosignals. These tools address issues such as data acquisition, artifact processing, quality control, data visualization and so forth.

Multi-Scale Signal Processing

By taking and analyzing measurements in vast quantities, engineers are working toward a better understanding of how physiological systems work. A lot of effort is currently focused on multi-scale signal processing; looking for features in the measurements that are taken at varying scales in order to make more reliable predictions about the whole patient.
Biomedical signal processing encompasses the entire spectrum of health and wellness. It is the basis of how engineering aids the field of medicine. Medicine is an empirical field. Doctors understand medicine based on what they know to be true through their study and practice. Engineers, on the other hand, focus on trying to fully understand a particular system. Once we truly know an answer, our work in that area is done.

You might also be interested in: