**5. How to extract clinically important information**

Suitably detected EMG signals can contain information that can be used to assist with the diagnosis of neuromuscular disorders. Specific characteristics of a detected EMG signal can be related to the type of neuromuscular disorder present (i.e. myopathic or neurogenic) as well as the degree to which the muscle may be affected by a disorder. As described above, the changes in MU morphology and activation created by a disease process lead to expected changes in MUP shapes and stability as well as the level of EMG signal complexity. However, in order to use EMG signals to support clinical decisions, the EMG signals must be acquired from a contracting muscle during specific activation protocols and using specific detection electrode configurations. The activation protocol and detection electrode configuration used should provide EMG signals in which the effect of the changes in MU morphology and activation created by a disease process are emphasized. Specific aspects of the detected EMG signals can then be analyzed to determine if they were most likely detected in a myopathic, normal or neurogenic muscle. It this last step qualitative or quantitative analysis can be applied.

considered incomplete, while if individual MUPs can be recognized, the IP is considered discrete [6]. One border of the EMG envelope is defined by connecting the negative peaks while the other is defined by connecting the positive peaks. The voltage difference between these lines (borders) is the envelope amplitude [6]. The criteria are as follows [14]: if the IP is full and the envelope amplitude is small, this is a myopathic pattern, and if the IP is discrete and the

Clinical Quantitative Electromyography http://dx.doi.org/10.5772/56033 101

The objectives of this qualitative analysis and characterization of the needle-detected EMG signals is to extract information regarding the morphology of a representative sample of MUs of the muscle being examined. Experienced and skilled clinicians can use these qualitative analyses to assist with the diagnosis of an examined muscle with respect to which, if any,

One of the main disadvantages of qualitative EMG analysis is inter and intra-rater variability. Specific assessments made and the consistency with which they are made depend on the training, experience and skill of the examiner. In addition, no more than a few MUPs can be qualitatively analyzed at a time [4]. Therefore, qualitative analysis is restricted to low levels of muscle activation where only a few MUs are recruited and consequently the EMG signals

Quantitative electromyography (QEMG) is an objective assessment of several aspects of detected EMG signals to assist with the diagnosis of a muscle under examination and also to assess the severity of an existing disorder if one is detected. QEMG is also sometimes used to assess the status of a detected disorder (active or not) and its time course (chronic or acute) [6]. Quantitative analysis is an automated process, unlike qualitative analysis, which typically requires comparing measured feature values of an EMG signal detected in a muscle under examination to standard or training set values from EMG signals detected in muscles of know clinical state (i.e. myopathic, normal or neurogenic). As a result, standardization or the collection of training data needs to be completed properly; for instance, data should be grouped based on age, gender, and specific muscle. In addition, the EMG signals need to be acquired using a standardized and consistent technique regarding the electrode used, the detection protocol, etc [4]. QEMG aims at increasing diagnostic sensitivity and specificity. Unlike qualitative analysis, quantitative analysis is not limited to studying just the first few recruited

MUs and EMG signals detected at higher levels of muscle activation can be analyzed.

Accuracy and transparency are two important factors that must be taken into consideration when selecting or designing a QEMG technique for clinical use. Accuracy is a major issue for any supervised learning problem as the ultimate purpose is to correctly categorize, a muscle being examined so that its condition can be correctly weighted in determining the overall patient diagnosis. Transparency is essential here as well because a clinician should be able to clearly understand the rationale behind the characterization process. A clinician is expected to have moderate knowledge of statistics and if the complexity of a certain technique is beyond that, it is considered as a non-transparent characterization technique. Rule-based classifiers

envelope amplitude is larger than its normal value, this is a neurogenic pattern.

specific disease processes may be present and if present, to what extent.

detected are the aggregation of only a few MUPTs.

**5.2. Clinical Quantitative EMG (QEMG)**
