UW Gazette, March 12, 1997 by Bob Whitton for the UW news bureau Though he is an engineer, the research interests of Dr. Daniel Stashuk, assistant Professor, systems design engineering, are in the biomedical area. His broad field is signal detection and signal processing, and particularly detecting and analysing electrical signals within the human body that are associated with nerves and muscles. Such signals, in the form of minute electrical voltage variations, are known to travel from our brain through the spinal column, and then to various muscle groups (arms, legs, etc.), so we can move them; impulses called "action potentials" originating from motor neurons in the spinal cord stimulate groups of muscle fibres, causing contractions. When our muscles contract we move. These contractions are initiated by propagating action potentials that in turn give rise to electromyographic or EMG signals - signals that have to do with the electrical activity of muscles. Stashuk's aim is to detect and analyse these EMG signals and extract information that will be useful in diagnosing and treating a variety of neuromuscular disorders - muscular dystrophy (MD) for example, or amyotrophic lateral sclerosis (ALS). He is trying to find better ways to help physicians diagnose and monitor these diseases. A former UW electrical engineering student who completed his graduate degrees at McMaster where he received his PhD in 1985, Stashuk spent the next three years with Dr. Carlo De Luca at the NeuroMuscular Research Center, Boston University, returning to Waterloo to a tenure track position in SDE in 1988. Since then he has continued his work on EMG signals, which can be detected by inserting an extremely small device directly into muscle tissue; they can also be detected exter nally, by a somewhat larger sensor placed on the skin. The sensing is delicate because very minute voltages are involved, though the instruments are not what might be called leading-edge technology. The detecting surface of an electrode inserted into muscle tissue is appoximately 100 microns in diameter; when the electrode picks up EMG electrical activity this means muscle fibres are being contracted (in response to messages from the brain). The EMG signal detected during a specific muscle contraction can be digitized and displayed on a computer screen as a complex waveform, or represented by a mathematical model. His challenge is to break down these waveforms and analyse themÉ a complicated activity requiring an understanding of the electrical fields generated within and around muscle fibres. When he does this he finds that the waveforms from diseased tissue differ from the waveforms from healthy tissue; thus he can find out whether a patient is suffering from, for example, a disease that primarily affects muscle tissue. This could be very valuable to medical clinicians interested in diagnosing an illness, or determining how far it has progressed, or what kind of treatment would be best. "I take composite EMG signals and decompose them into constituent motor unit action potential trains (MUAPTs)," he says. "Signals are digitized, then manipulated using a computer and graphically displayed to illustrate what is happening. We can analyse each individual motor unit, and its behavior and characteristics, and we can do this for an entire group of active motor units to determine the character of these units and how their characteristics change with different diseases. From this we can help identify the disease, and we can also learn how far it has progressed." He says a disease such as MD changes the shape characteristics of the motor unit action potentials (MUAPs)É which are the electrical pulses that make up a MUAPT, or their firing pattern within the MUAPT. Stashuk, with the help of his students, has now developed most of the EMG signal decomposition algorithims that he feels are needed, and he has begun working with medi cal researchers, in Boston, Washington, Houston, Minneapolis and Chapel Hill; he is also attempting to reestablish relationships with interested medical scientists at McMas ter. "I want to build up a database that can be useful in evaluating the ability of a variety of EMG-based techniques to quantify neuromuscular disorders," he says. "These would include MD which affects the peripheral musculature and is characterized in part by the fact that while the number of motor units (groups of fibres connected to and controlled by a single motor neuron) a muscle has stays unchanged, the number of fibres in these groups becomes smaller and smaller; that is, the muscle fibres dieÉ which becomes an indicator of MD." He predicts that because of the loss of muscle fibresÉ that for similar levels of contaction the firing rates of the MUAPTs in MD patients will be increased, when compared to those in healthy subjects. With ALS on the other hand, motor neurons die off reducing the number of motor units and leaving muscle fibres that have lost their nerve supplyÉ so the number of MUAPTs decreases, and the sizes of their MUAPs increases. Also, it is predicted that firing rates will become lower and that these firing rate changes will be measurable. There are two primary types of diseases that concern Stashuk: (1) those that affect nervous tissue (such as ALS), and (2) those that affect the muscle tissue (such as MD). However, there are also diseases that affect the junction between muscle and nerve (such as myasthenias gravis) which result in muscle weakness. "I have been developing EMG-based techniques for diagnosing all three," Stashuk says. "Of course, physicians use a battery of other tests to confirm a diagnosis. But one of the significant things about this work is that it promises to permit EMG-based testing to be quantifiedÉ rather than carried out in a fundamentally subjective way." Clinical EMG physicians have traditionally had to rely heavily on subjective judgment. Stashuk's goal is to develop techniques that can reduce the amount of subjective judgement involved in diagnosis. He wants to be able to generate useful numbers that can be compared and that will suggest clearly whether a specific instance of a given disease is progressive, is in remission, how successful the treatment to date has beenÉ and so forth. While his new techniques are intended to be used clinically they could also be used for research applications, by someone seeking a new understanding of a disease. At the moment, however, he is still evaluating the tools that he is suggesting physicians use. "For the last eight years my focus has been on the decomposition of the EMG signals that we are able to detect in muscle tissue," he says. "We've been looking for the best way to take an EMG signal apart and we have evaluated how accurate our decompositions are. Now we are starting to develop and evaluate clinical applications of the information available following an EMG signal decomposition: What we need to know know is, will the information be clinically useful, and how reliable will it be?" One of his main challenges has involved dealing with interference or "noise," which can occur if a patient simply moves slightly while EMG signals are being detected. Stashuk is looking forward to the day when MD or ALS sufferers will have ready access to this technology, perhaps in their doctors' offices where they will be able to get helpful information almost immediately. If the technology proves as useful as he hopes, it should be possible for patients to learn of their condition, almost right away. Stashuk describes his work as very much "on the cusp" at the moment; it is still so new that an awareness of it has barely begun to move through the medical profession. It is still not widely appreciated. He has published journal articles on the technical aspects of his work but not on its clinical aspects. However, he expects word of the new developments will spread during the coming year as he confirms his data. The key piece of research equipment he has been using is manufactured by Advantage Medical, of London, Ontario, and it is essentially a computer specially programmed and equipped to process data from tests. As has been noted above, the technique utilizes data collected not only from internal sensors deep within muscle tissue, but also from sensors located on the subject's skin, on top of the muscle. These external sensors can be vastly larger than internal ones and can provide a "macro" picture of electrical phenomena. Both types provide valuable information, in different ways. "But this means there are two channels of information to be processed," he notes. "Both are digitized, then the 'noise' is removed and the signals are 'decomposed' using the algorithms we have developed. This results in a computer screen display that presents information relating to how the muscle group is working." The technology measures MUAPTs not only according to shape but also according to their firing patternÉ how many pulses per second occur, the time gap between the dis charges, how long each pulse lasts (it can be a matter of milliseconds). Initially, each MUAP of a MUAPT is plotted on top of previous MUAPs to form a shimmer plot on the computer screen. The tracings for each individual MUAP can then be completely separated (decomposed), so one can see what each is aboutÉ and take into account the meaning of variations in MUAP shapes as they occur. This is essentially a pattern- recognition task. "What we are trying to do is provide information to physicians about the state of a subject's peripheral neuromuscular system, and do so in a way that will be useful to them when making clinical decisions," Stashuk concludes.