*5.2.1.3. Clouds analysis*

usually provide this required transparency when they are used in QEMG but their accuracy

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

As mentioned earlier, the term "interference pattern" is used to refer to the complete EMG signal detected from a contracting muscle. The term "interference pattern" is sometimes used to describe the EMG signal detected during a maximal contraction only but the former definition is more common. The characteristics of an interference pattern (IP) depend on the level of muscle activation maintained during its detection and the type of electrode used. The level of activation determines the number of recruited MUs and their firing rates. The type of electrode used determines the shape characteristics of the MUPs (duration, area, amplitude,

The term "interference pattern analysis" (IPA) refers to those techniques that analyze an IP. IPA is used when a global analysis of an EMG signal is desired. IPA is a quantitative analysis which can be completed using either a frequency or time domain representation of the IP [6].

Any signal of time can be represented by a summation of sinusoidal functions of several frequency values, phase shifts and magnitudes. Therefore, a frequency domain representation of an IP can be obtained if these frequency values, phase shifts and magnitudes of the signal are identified. A frequency domain representation reveals information about MUP ampli‐ tudes, and durations as well MU firing patterns. For instance, high frequency components are representative of MUPs with short durations and short rise times, while low frequency components are representative of to MUPs with long durations and long rise times [8].

Time domain analysis basically depends on detecting the main characteristics of the time domain representation of an IP. Detecting changes in the sign and slope of an IP was how it was initially performed [8]. Later, more specific characteristics of the time domain signals were found to be important. For instance, the number of turns and their amplitude are important features for discriminating between IPs detected in myopathic, normal and neurogenic muscles. A peak is identified to be a turn if the change in amplitude between this peak and the previous peak exceeds a prespecified threshold, while the amplitude is the difference in

methods based on interference pattern and individual MUPT analysis, respectively.

etc.) that are created by the active MUs and which in turn comprise the IP.

Following are brief descriptions of common IPA techniques.

voltage values between successive peaks of opposite polarity [8].

levels are not as high as support vector machines or neural networks.

*5.2.1. Interference Pattern (IP) analysis*

102 Electrodiagnosis in New Frontiers of Clinical Research

*5.2.1.1. Frequency domain analysis*

*5.2.1.2. Time domain analysis*

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 are outside of the cloud.

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 by a majority of normal MUPTs.
