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

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

applied.

**5.1. Qualitative electromyography**

100 Electrodiagnosis in New Frontiers of Clinical Research

presented in a free running or triggered raster display.

The firing rates of MUs and the number of recruited MUs are also estimated.

the firing rate [6]; to obtain the number of discharges in one second.

The advantage of analyzing an IP detected during minimal muscle activation is that individual MUPs can be recognized. Therefore information about their recruitment information and their firing rates can be obtained [21]. In order to estimate MU firing rates, a 500 ms epoch is displayed [6]. This technique depends on visual inspection to identify individual MUP discharges. The number of discharges of an MU in this 500 ms epoch is multiplied by 2 to get

This is a semi-quantitative approach to implement IPA where an IP is detected during maximal force of contraction [14]. The IP is considered full when the signal baseline is completely obscured by MUP spikes [6]. If the baseline can be seen between MUP discharges, the IP is

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

The current status quo for assessing the clinical state of a muscle is to qualitatively analyze EMG signals detected using needle electrodes following abrupt movement of the electrode, while the muscle is at rest and during low levels of muscle activation. Characteristics of the detected signals are subjectively compared to those expected to be detected in normal muscle. The signals detected following abrupt needle movement and while the muscle is at rest are grouped into what is classified as spontaneous activity. Following abrupt needle movement, if the muscle remains active (i.e. significant signals are detected) for a prolonged period of time this is a sign of abnormality. Likewise, if while the muscle is at rest, potentials related to muscle fibre fibrillation or MU fasciculation are detected the muscle is considered abnormal. The degree of spontaneous muscle activity is often subjectively graded using a discrete 4 or 5 level scale. MUPs contained in EMG signals detected during low levels of muscle activation are visually and aurally analyzed to assess their shape, size and stability either as they are

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 usually provide this required transparency when they are used in QEMG but their accuracy levels are not as high as support vector machines or neural networks.

*5.2.1.3. Clouds analysis*

are outside of the cloud.

be higher.

*5.2.2.1. Signal detection and preprocessing*

**1.** Level and/or window triggering

by a majority of normal MUPTs.

*5.2.2. MUP template / MUPT characterization*

Clouds analysis uses the number of turns and the mean turn amplitude features of IPs detected during several contractions maintained at different levels of muscle activation ranging from slight effort to maximal. The number of turns and the mean turn amplitude for each IP define points in a two dimensional plot in which is overlaid a cloud or region [8]. The cloud defines the area in which 90% of data from IPs detected in normal muscle are expected to be. Accord‐ ingly, a muscle is considered diseased if more than 10% of its IPs provides data points which

Clinical Quantitative Electromyography http://dx.doi.org/10.5772/56033 103

One of the limitations of IPA is that because of superpositions of MUPTs it is difficult to detect marginal levels of disorders. Small numbers of abnormal MUPTs may be lost in IPs generated

A MUPT is composed of the MUPs created by a single MU. The typical MUP shape of a MUPT is represented by its MUP template. The stability of the MUPs with in a MUPT can be estimated as can the firing behavior of the MU that created the MUPT. MUPT characterization refers to performing supervised learning to determine if a MUPT was created by a normal or abnormal (disordered) MU, if just two categories are considered or by a myopathic, normal or neurogenic MU if three categories are considered. This characterization is based on a training stage that is performed using training data suitably representing each category. MUPT features used for MUPT characterization often consist of MUP template morphological features; features extracted from the time domain representation of the MUP template [4] as well as spectral features; those extracted from its frequency domain representation [4]. MU firing pattern features have not yet be effectively used. Typically, a feature selection step is performed to select the best feature subset. As is the case with any supervised learning problem, feature selection can be filter-based (quality metric of the feature subset depends on information content like interclass distance or correlation) or wrapper-based (quality metric of the feature subset depends on the accuracy of the characterization process using such feature set).

However, wrapper-based feature selection techniques are used more frequently [56].

In addition to the intrinsic MUP template features, like turns, duration, amplitude, etc, combinations of features can be used if they improve the characterization results. For instance, MUP template thickness (area/amplitude) can be added to the features used for characteriza‐ tion to improve classification performance as the discriminative power of the feature set would

Individual MUPTs can be extracted for quantitative analysis using level or window triggering methods. These methods allow the MUPs created by a single MU to be extracted, but only if their amplitudes are unique with respect to the amplitudes of MUPs created by other MUs.

To perform QEMG, the complete EMG signal, or interference pattern, can be analyzed or individual MUP activity can be isolated from an EMG signal using level or window triggering or EMG signal decomposition methods. If EMG signal decomposition methods are used, individual MUPT can be analyzed which allow information about typical MUP shape, MUP shape stability and MU activation patterns to be used. The next sections discuss QEMG methods based on interference pattern and individual MUPT analysis, respectively.
