**3. EMG onset detection**

EMG onset parameters define the duration for the muscles to stay active [2]. Onset estimation is useful to diagnose abnormality in muscle coordination. To detect the EMG onset, visual inspection or measurement of nerve conduction velocity may be used [22]. The basic thresholding method for onset detection is sensitive to the type of trials, EMG amplifiers and noise level in the signal. The thresholding based on SD baseline noise can be improved with local peak value. In a study [23], integrated EMG provided more information about early activation. During preconditioning, Teager-Kaiser Energy Operator (TKEO) also improved the onset detection accuracy by constricting the energy of the baseline noise [24, 25]. Staude et al. compared onset detection methods based on the statistical optimal decision threshold [26]. The simple threshold algorithm of Hodges and Bui [26] identifies the onset at a point where the mean of the samples within a fixed time window surpasses the baseline level by a defined multiple of standard deviation [27].

The basic framework of the threshold detection algorithm includes signal conditioning (rectification, filtering, whitening etc.), detection (Test Function and Decision rule), and postprocessing [26]. A block diagram is shown in **Figure 2**.

Double threshold methods are considered better in comparison to single threshold methods [7]. The Bonato algorithm [28] includes pre-whitening filter and data sample squaring in the conditioning unit. The test function is computed between two successive samples from the conditioned EMG signal. The onset point identification is based on the following rules: (1) x out of y samples must exceed the threshold and (2) activation state of the muscle after surpassing the threshold should last for a certain number of samples or duration of time [26].

In Lidierth [29] method, the signal conditioning unit performs full wave rectification. The test function and decision rule are based on Hodges [26]. Additional post-processing rules increase the efficiency of the algorithm. The test function unit detects the onset if the sEMG signal exceeds the threshold. Any decline in the activity below threshold within a defined duration, should not be longer than the defined range of samples [29]. The power spectral correlation coefficient method performs better than TKEO and utilizes the moving average method of Hodges and Bui [30]. The statistical estimation algorithm includes an optimal estimator and approximated generalized likelihood-ratio detector. The statistically optimized algorithms are more robust in terms of signal parameters [26]. Tenan et al. [25] reviewed three classes of standard EMG (linear envelope, entropy, TKEO) and

#### **Figure 2.**

*EMG onset estimation framework; xk is Gaussian noise signal, yk is the processed signal, σ*′*o and μ*′*o are standard deviation and mean of samples, respectively, gk* ≥ *Th (Threshold) is the value to trigger an alarm ta, and t*′*o is the change time estimation.*

**23**

*A Review of EMG Techniques for Detection of Gait Disorders*

results in inhibitory postsynaptic potentials [33].

**4. EMG decomposition into MUAP**

challenges to decomposing the sEMG.

six classes of statistical EMG onset detection (general time series/mean–variance, sequential change point detection with parametric and non-parametric methods, batch change point detection, and Bayesian change point analysis). The Bayesian Change Point analysis algorithm showed higher reliability and accuracy for the

Maximum voluntary contraction (MVC) is a common scaling technique for EMG onset detection. MVC is the largest RMS amplitude a muscle generates in maximum contraction [31]. MVC has a curvilinear relationship with the muscle force production, where less force production amount to muscle weakness. EMG onset on a normalized time series with MVC can help diagnose gait disorders associated with atrophy [2]. Muscle spasticity/co-contraction during tremors among patients with neurological gait disorder exhibited abnormality in EMG onset compared to healthy individuals [12, 32]. EMG envelope indicated alterations in EMG onset for patients with Parkinson's during freezing episodes [20]. A premature activation of TA and GM muscles before a freezing episode was observed. In gait impairment, due to cervical spondylotic myelopathy, delayed onset and prolonged activation were present [12]. In cerebral palsy earlier onset suppression of EMG within cutaneous muscular reflex is associated with motor dysfunction, which

Raw EMG signal consists of superpositioned motor unit activation potentials (MUAP) and noise components. Muscle crosstalk is a major issue during recording of the biological signals. The crosstalk is dependent on factors such as anatomical site for the placement of electrodes, type of movement, and skin thickness. Since it is harder for sEMG to detect the origin of muscle electrical activity, the chances of muscle crosstalk are higher in sEMG than needle EMG [13]. Besides, low spatial resolution, high movement artifact, and narrow frequency range makes needle EMG more promising as a diagnostic tool in nerve conduction studies for assessing neurological disorders [13]. Changes in the shape of MUAPs, large dynamic range of action potential among motor units and superposition of motor units pose major

Fang et al. [34] decomposed EMG into MUAP by wavelet transform. The technique utilized spectrum matching in wavelet domain as opposed to waveform matching. De Luca et al. [35] proposed a method to decompose the sEMG into MUAP during cyclic dynamic contractions. The algorithm solved two main problems, the first associated with the displacement of the electrode on the surface of the skin leading to alteration in the shape of MUAPs, and second regarding lengthening and shortening of the muscle fibers while undergoing those contractions. The algorithm was an extension of the algorithm by Nawab et al. The process was followed as an extracting time-varying time template parameter, performing time-varying filter analysis, clustering on MUAP trains, shape refinement, test, and decomposition. If the test failed, the iterations were done again for shape refinement of MUAPs. Precision Decomposition I (PD I), which was earlier used to decompose needle EMG data was updated to decompose sEMG and referred as PD (III). An updated approach of PD III reported by Nawab et al. has PD-IPUS (Integrated Processing and Understanding) and PD-IGAT (Iterative Generate and Test) [36, 37]. Another method to decompose sEMG into MUAP trains included a hybrid approach of K-means clustering and convolution kernel compensation method. K-means clustering was performed to estimate the pulse trains, which were

later updated iteratively by convolution kernel compensation method [38].

*DOI: http://dx.doi.org/10.5772/intechopen.84403*

singular EMG onset detection.

*Artificial Intelligence - Applications in Medicine and Biology*

EMG onset parameters define the duration for the muscles to stay active [2]. Onset estimation is useful to diagnose abnormality in muscle coordination. To detect the EMG onset, visual inspection or measurement of nerve conduction velocity may be used [22]. The basic thresholding method for onset detection is sensitive to the type of trials, EMG amplifiers and noise level in the signal. The thresholding based on SD baseline noise can be improved with local peak value. In a study [23], integrated EMG provided more information about early activation. During preconditioning, Teager-Kaiser Energy Operator (TKEO) also improved the onset detection accuracy by constricting the energy of the baseline noise [24, 25]. Staude et al. compared onset detection methods based on the statistical optimal decision threshold [26]. The simple threshold algorithm of Hodges and Bui [26] identifies the onset at a point where the mean of the samples within a fixed time window surpasses the baseline level by a defined multiple of standard deviation [27]. The basic framework of the threshold detection algorithm includes signal conditioning (rectification, filtering, whitening etc.), detection (Test Function and Decision rule), and postprocessing [26]. A block diagram is shown in **Figure 2**.

Double threshold methods are considered better in comparison to single threshold methods [7]. The Bonato algorithm [28] includes pre-whitening filter and data sample squaring in the conditioning unit. The test function is computed between two successive samples from the conditioned EMG signal. The onset point identification is based on the following rules: (1) x out of y samples must exceed the threshold and (2) activation state of the muscle after surpassing the threshold should last

In Lidierth [29] method, the signal conditioning unit performs full wave rectification. The test function and decision rule are based on Hodges [26]. Additional post-processing rules increase the efficiency of the algorithm. The test function unit detects the onset if the sEMG signal exceeds the threshold. Any decline in the activity below threshold within a defined duration, should not be longer than the defined range of samples [29]. The power spectral correlation coefficient method performs better than TKEO and utilizes the moving average method of Hodges and Bui [30]. The statistical estimation algorithm includes an optimal estimator and approximated generalized likelihood-ratio detector. The statistically optimized algorithms are more robust in terms of signal parameters [26]. Tenan et al. [25] reviewed three classes of standard EMG (linear envelope, entropy, TKEO) and

*EMG onset estimation framework; xk is Gaussian noise signal, yk is the processed signal, σ*′*o and μ*′*o are standard deviation and mean of samples, respectively, gk* ≥ *Th (Threshold) is the value to trigger an alarm ta,* 

for a certain number of samples or duration of time [26].

**3. EMG onset detection**

**22**

**Figure 2.**

*and t*′*o is the change time estimation.*

six classes of statistical EMG onset detection (general time series/mean–variance, sequential change point detection with parametric and non-parametric methods, batch change point detection, and Bayesian change point analysis). The Bayesian Change Point analysis algorithm showed higher reliability and accuracy for the singular EMG onset detection.

Maximum voluntary contraction (MVC) is a common scaling technique for EMG onset detection. MVC is the largest RMS amplitude a muscle generates in maximum contraction [31]. MVC has a curvilinear relationship with the muscle force production, where less force production amount to muscle weakness. EMG onset on a normalized time series with MVC can help diagnose gait disorders associated with atrophy [2]. Muscle spasticity/co-contraction during tremors among patients with neurological gait disorder exhibited abnormality in EMG onset compared to healthy individuals [12, 32]. EMG envelope indicated alterations in EMG onset for patients with Parkinson's during freezing episodes [20]. A premature activation of TA and GM muscles before a freezing episode was observed. In gait impairment, due to cervical spondylotic myelopathy, delayed onset and prolonged activation were present [12]. In cerebral palsy earlier onset suppression of EMG within cutaneous muscular reflex is associated with motor dysfunction, which results in inhibitory postsynaptic potentials [33].
