Quantifying Spasticity: A Review

*Kristjana Ósk Kristinsdóttir, Samuel Ruipérez-Campillo and Þórður Helgason*

#### **Abstract**

A precise method to measure spasticity is fundamental in improving the quality of life of spastic patients. The measurement methods that exist for spasticity have long been considered scarce and inadequate, which can partly be explained by a lack of consensus in the definition of spasticity. Spasticity quantification methods can be roughly classified according to whether they are based on neurophysiological or biomechanical mechanisms, clinical scales, or imaging techniques. This article reviews methods from all classes and further discusses instrumentation, dimensionality, and EMG onset detection methods. The objective of this article is to provide a review on spasticity measurement methods used to this day in an effort to contribute to the advancement of both the quantification and treatment of spasticity.

**Keywords:** spasticity, Ashworth scale, Tardieu scale, Wartenberg pendulum test, stretch reflex, electromyography

#### **1. Introduction**

Spasticity is a motor impairment present in patients with various neurological disorders, including stroke [1], spinal cord injury (SCI) [2], cerebral palsy (CP) [3], and multiple sclerosis (MS) [4]. It is characterized by the hypersensitivity of the stretch reflex, but its complete mechanisms are poorly understood [5]. Spasticity affects the mobility, and therefore quality of life of those living with it. A precise method to quantify spasticity is, thus, fundamental for early intervention and correct treatment to optimize recovery outcomes and to evaluate other conditions and diseases.

In 1980, Lance proposed a definition of spasticity that, to this day, has been the most prominent one. Lance's definition states that spasticity is "a motor disorder characterized by a velocity-dependent increase in tonic stretch reflexes with exaggerated tendon jerks, resulting from hyperexcitability of the stretch reflex, as one component of the upper motor neuron syndrome" [6]. However, Lance's definition only describes spasticity during passive movement and does not take its effects during voluntary activity into account. As a result, Young proposed a refined definition of spasticity that is independent of the type of movement. Young's definition states that spasticity is "a motor disorder characterized by velocity-dependent increase in tonic stretch reflexes that result from abnormal intraspinal processing of primary afferent input" [7]. More definitions of spasticity have been proposed [8, 9], but there is a lack of consensus regarding which one to use [10–12], which highlights the complexity of the pathology.

Mechanisms that contribute to the development of spasticity include changes in reflex arcs that affect motor neurons' excitability and the changes of motor neurons' internal features [13]. These changes cause a loss of control between the brain and spinal cord, which results in a lack of inhibition of the stretch reflex. In a healthy individual, a stretch of the muscle will activate muscle spindles, which send sensory inputs to the spinal cord through Ia afferent fibers. There, a-motoneurons are activated and send signals to the muscle from which the sensory input arose, causing it to contract. In spasticity, a reduction or a complete loss of the inhibitory effects of the dorsal reticulospinal tract on the amotoneurons in the spinal cord leads to excessive muscle activation [1, 14], but spasticity can also be caused by increased excitation in motor tracts originating in the brain stem and increased action potentials in sensory neurons [15]. As previously mentioned, the velocity dependence of spasticity has been contributed to changes in the Ia afferent pathway or a change in the gain or threshold of the stretch reflex. Additionally, some studies have suggested a position dependence of spasticity, which might be a result of changes in the gain or threshold of the Ia and group II muscle spindle afferent fibers [10], as well as the viscoelastic properties of the passive tissue of a joint [16].

Since the onset of the neural lesion in spastic CP occurs in an underdeveloped nervous system, there is a differentiation between stroke and SCI patients in the mechanism of spasticity. Carr et al. [17] presented evidence that the descending pathways in patients with perinatal brain injury are subject to reorganization, which leads to a persistence of abnormal reflexes and automatic responses that are not visible in stroke and SCI patients [18]. Further, Willerslev-Olsen et al. [19] reported that passive muscle properties are changed in children as young as three years of age.

Spasticity does not only consist of neurogenic resistance but can also involve complex changes in muscular systems, leading to non-neurogenic resistance [1, 12, 20, 21]. These changes, which may include alterations in muscle fiber size and length along with modifications in fiber type distributions, are not accounted for in Lance's definition [6, 22]. The concentrations of fatigue-resistant muscle fibers in spastic muscle have been reported to exceed that of healthy muscle and changes in mechanical and morphological properties of intra- and extracellular materials may also contribute to spasticity [23–27]. Consequently, it is logical that spasticity assessment consists of both neurophysiological and biomechanical measurements [28]. Moreover, it is important to be able to clinically distinguish neurogenic and non-neurogenic resistance [19] and individually identify which one has a greater contribution to spasticity. Thereby, treatment plans can be constructed based on the profile of components, those with greater neurogenic components may be better suited for a therapy reducing the spinal stretch reflex, while those with a greater non-neurogenic component might benefit from stretch and exercise [20].

As suggested by the preceding discussion, the measurement of spasticity can be extremely complicated. Spasticity has intrinsic fluctuations, whereas it is both time [29, 30] and context-dependent [22]. Therefore, a measurement of the severity of spasticity of an individual can yield completely different results based on, for example, the time of day and physical and emotional state of the subject. The ideal spasticity measurement method should be sensitive to spasticity, clinically feasible, and have high reliability and reproducibility. Further, the elicitation of spasticity has to be standardized. The clinical feasibility of a measurement tool depends on the time required to administer the test, interpret the results, and analyze them. Other factors that come into play include its portability, cost and the need for specialized equipment and training [31]. The objective of this article is to review articles on spasticity measurement methods, and thus provide a platform to advance both the quantification and treatment of spasticity.

## **2. Clinical scales**

As of today, various proposals have been made on how to quantify spasticity. Rating scales, such as the Ashworth and Tardieu scales, are the most prevalent methods for clinical assessment of spasticity, but they are not impeccable. In this section, measurement of spasticity using clinical scales will be discussed. For a clear understanding of this section, a summary of the clinical scales is depicted in **Table 1**. The testing procedure is based on initiating a brisk dorsiflexion movement of the relaxed ankle, activating the monosynaptic stretch reflex pathway [32]. The original Ashworth scale (AS) has been refined [33] to the modified Ashworth Scale (MAS) [34] and the modified modified Ashworth scale (MMAS) [35] to ensure better sensitivity. In fact, those methods have faced controversy, whereas they are dependent upon the subjective interpretation of the examiner and thus suffer from poor inter-rater reliability [39]. The Ashworth scales measure the resistance of a spastic limb during passive soft-tissue stretching and do not take in to account different stretch velocities. Therefore, the AS and MAS cannot indicate whether the resistance is due to a hyperactive stretch reflex or an increase in the viscoelastic properties of other tissues surrounding the joint [12, 36, 40], although MAS has been shown to correlate with surface electromyography (sEMG) stretch response activity [41]. Moreover, a clustering of the values in the mid-range of the MAS has been reported, that is the extreme values are scarce [42] and the MAS has been demonstrated to be more closely related to the passive stiffness of the joint than to joint spasticity [40, 43]. The stiffness of a joint is defined as its resistance to passive movement and indicates the increment in force of the muscles in response to a change in length [44]. In fact, spasticity is only one of many factors that can alter the resistance to passive movement of a joint [45].


**Table 1.**

*Summary of the clinical scales and their date and main description for the procedure.*

On the other hand, the TS and MTS compare the response of the muscle to passive movement at both slow and fast speeds allowing them to address the velocitydependent aspect of spasticity and differentiate between neural and soft tissue components of muscle resistance [36, 37]. MTS has been demonstrated to have higher reliability than MAS [32, 39] and is recommended by two international consensus statements as it coordinates with Lance's definition of spasticity [46, 47]. Several parameters obtained from the TS and MTS are used to grade the severity of spasticity. These parameters include the angle of catch (AOC), the type of muscle reaction (X value), and the spasticity angle, which is the difference in the range of motion (ROM) of the joint at different speeds [32].

Although the MTS has been widely used and accepted, there is still a demand for a simple and portable clinical tool that has high levels of validity and reliability. Also, the application of the most frequently used clinical scales can be lengthy, making their usefulness in clinical, and especially pediatric settings insufficient. In this respect, Love et al. [38] extracted the best features from the TS and MTS to create a new clinical scale, the Australian Spasticity Assessment Scale (ASAS). The ASAS has a simple test procedure. Where the slow passive movement is assessed prior to the fast passive movement, which aids in excluding the nonneural components. The scoring criteria of the ASAS are mutually exclusive, ensuring that each muscle group only fits into one category. The inter-rater reliability of the ASAS proved to be greater than that of the most prevalent clinical scales, but it faces a limitation in the distribution of scores, whereas there are many scores of the lowest level but few scores of the highest level [38]. An illustrative summary of the clinical scales is depicted in **Table 1** for clarity. Scholtes et al. [48] created the spasticity test (SPAT) for the same purpose. However, the SPAT is merely a simplification of the TS and does not adopt features from the MTS. The SPAT is clinically feasible considering that its implementation takes a maximum of 15 minutes, and it has excellent intra-rater reliability [48].

#### **3. Instrumental assessment methods**

Obtaining a reliable outcome from the MTS is dependent upon the accuracy of the evaluation of the joint angle and angular velocity (see **Table 2**). Therefore, the addition of an instrumented or sensor-based assessment to the clinical scales should significantly improve their accuracy. Banky et al. utilized a Microsoft Kinect 3D camera and a smartphone in order to determine the joint start angle, end angle, total range of motion (ROM), and peak angle velocity while carrying out the MTS. Their results demonstrated a good accuracy compared to a criterion-standard 3D motion analysis system, which establishes that low-cost, accessible technology can be successfully used in instrumented spasticity tests [49]. In a study that investigated the accuracy of AOC measurements using goniometry, van den Noort et al. concluded that the inevitable repositioning of joints after the event of a catch decreases the precision and accuracy of the measurement. Instead of using goniometers, they suggested the use of inertial sensors [16]. Subsequently, Paulis et al. compared the reliability of TS measurements when using goniometers and inertial sensors. They confirmed the findings of van den Noort et al., recommending inertial sensors instead of goniometers to obtain more accurate results [37].

Instrumented tests can be classified according to whether the limb is moved around the joint by an operator [50, 51] or a mechatronic device [52] and the type of biological signals being measured. The added value of instrumented measurements is

*Quantifying Spasticity: A Review DOI: http://dx.doi.org/10.5772/intechopen.112794*


#### **Table 2.**

*Overview of evaluation criteria for clinical scales in spasticity quantification.*

#### **Figure 1.**

*Illustration of setups and mechanisms in the context of instrumental assessment methods for spasticity. A: Spasticity assessment test designed for a child with cerebral palsy to assess spasticity using inertial sensors. The knee joint is in ref. position, with the inertial sensors in the proximal and distant segment. The goniometer does not appear in the figure (extracted over modified figures in [16]) B: Assessment of elbow spasticity during passive stretch reflect through the use of a wearable sensor system. (B.a) illustration of the placement of the electrodes on the triceps and bices with two-channel EGM system. (B.b) fiber-optic goniometer. (B.c) wearable sensory system in the arm. (B.d) stretch-reflex test perform by a therapist with limb in extension and (B.e) limb in flexion (extracted over modified figures in [50]). C: Six degrees-of-freedom force-sensor load-cell to measure spasticity. (C.a) the white arrows indicate the direction of the stretch for the medial hamstrings, ensuring that the upper leg is maintained at 90o hip flexion. (C.b) white arrows measure the stretch of the gastrocnemius with a predefined knee angle measured with a calibration trial. Note that for both (C.a) and (C.b), the muscle activity was measured with surface EGM, jointrange characteristics with inertial measurement units, and torque with a force-sensor attached to either a shank orthosis on the posterior aspect of the lower leg or a foot (extracted over modified figures in [51]). D: Study for the evaluation of motorized and manual assessments of spasticity. Participants were seated with 70° hip flexion to limit posture-dependent reflex activity, and 20° knee flexion to allow for small knee contractures and to measure spasticity simultaneously in both the gastrocnemius medialis and soleus muscles. Participants were seated in an adjustable chair for the assessment with motorized (D.a) and on an examination table with a semi-inclined back and the lower leg on a stand for manual assessment (D.b) (extracted over modified figures in [53]).*

#### *Quantifying Spasticity: A Review DOI: http://dx.doi.org/10.5772/intechopen.112794*

their facilitation in quantifying the muscle response, but they also aid in standardizing the imposed movement of a limb by providing feedback [53]. Isokinetic dynamometers have frequently been used in laboratory conditions, but their high cost and limited clinical feasibility decrease their value as spasticity measurement devices [31, 54]. Sloot et al. compared the use of manual and motorized instrumented measurements when assessing the joint resistance in children with CP. The additional value of motorized instrumented tests is that they permit more control in the imposed movement. Their results demonstrated different muscle responses for manual and motorized tests, although the maximum velocity was equal. It was hypothesized that the different muscle responses were due to different movement profiles and that the profile of the manual instrumented test was more similar to that of walking. Furthermore, Sloot et al. suggested that movement profiles of instrumented measurements should match functional tasks, such as walking (**Figure 1**) [53].

## **4. Biomechanical methods**

Biomechanical methods to measure spasticity are concentrated on the resistance to passive movement (RTPM) of a joint. Generally, an effort is made to quantify the forces and torque generated during passive movement using dynamometers [16]. The system designed by Pandyan et al. [55] is a good illustration of the class of biomechanical spasticity measurement methods. The system consists of a force transducer and a flexible electrogoniometer and is designed in order to take simultaneous measurements of applied force and passive range of movement. The RTPM is then calculated as the slope of the graph of these two parameters, yielding a measure with units of Newtons per degrees (N/deg). Kumar et al. used the system developed by Pandyan et al. to assess the validity of MAS by comparing it to the RTPM, applied force, angular displacement, mean velocity, passive range of motion (PROM), and time required for the passive movement. Their results suggested that the MAS is not a valid measure of RTPM or spasticity [56]. A great advantage of the system developed by Pandyan et al. is that it can both be used in bed-bound and fully mobile patients. On the other hand, the system may be susceptible to artifacts as a result of motion at the interface of the apparatus and participant's forearm, which is due to the fact that the clinician is moving the apparatus and not the forearm directly [55]. Lindberg et al. produced a mechanical measurement device and a model that can differentiate the neural component of spasticity from the mechanical components by only monitoring the force during slow and fast passive movements [20]. They found that the neural component was dominating in the majority of the subjects included, who were all chronic stroke patients. They also divided the nonneural components into three categories based on whether they were viscous, elastic, or inertial. The device designed by Lindberg et al., which is devoted to measuring finger and wrist spasticity, has been shown to have higher reliability than the MAS [22]. Active or voluntary movement can also be used to biomechanically quantify spasticity. Wang et al. [57] used a few biomechanical parameters derived from the maximal isometric voluntary contraction of the elbow flexors to quantify spasticity. These parameters included the peak reflex torque (Tp [Nm]), which represented the muscle strength and the keep time of the peak torque (Tk [s]) which indicated the muscle endurance and was defined as the duration for which the muscle strength was maintained above 80% of the maximum torque. Furthermore, the rise time of the peak torque (Tr [s]) which signified the muscle power was defined as the time span between 10% and 80% of the maximum torque. Their results demonstrated that Tk had the best correlation to the severity of spasticity [57].

The Wartenberg pendulum test is based on oscillatory resistance characteristics of the lower limbs and can, therefore, be classified as a biomechanical measurement method. The pendulum test was introduced in the 1950s as a method of assessing spasticity in the clinical setting [58] and has proven to be sensitive to the presence and severity of spasticity [59, 60]. The test itself is based on letting the lower leg swing freely under the influence of gravity while recording joint kinematics. The pendulum test is most commonly used to quantify extensor spasticity, but the presence of flexor spasticity does not affect the results [61]. Initially, six or seven swings around the knee joint were considered normal. A decrease in the time of the leg swing or the number of oscillations was considered to be an indication of upper motor neuron involvement, whereas a prolonged swinging of the knee was contributed to lower motor neuron involvement [58]. However, in 1984, Bajd and Vodovnik [30] instrumented the pendulum test and derived the relaxation indices (R1 and R2) from it. The relaxation indices, which are defined as ratios of the amplitudes of different parts of the leg swing, have been shown to have high variability and low repeatability [60]. Therefore, various new parameters, such as the maximum velocity and acceleration [59, 60, 62], area under the pendulum curve [63], and frequency [62], have been extracted from the pendulum test and used to quantify spasticity. White et al. performed three-dimensional pendulum test analysis on children with CP, as well as able-bodied children. Overall, they extracted 13 parameters from the pendulum test and concluded that the integral of the sagittal plane motion curve [°s] is a better measure of spasticity than the relaxation indices. The integral of the sagittal plane is defined as area under the kinematic curve as a sum of degrees of knee motion by time component and a smaller number indicates more severe spasticity. In fact, White et al. also reported that three-dimensional analysis may be unnecessary since the motion in the frontal and transverse planes is relatively small during the test. They concluded that the pendulum test measures the combined effect of the reflex component of spasticity, chronic changes in musculotendinous tissues, and the muscle tone [62]. Cutting-edge technology has also proved to be useful in the implementation of the pendulum test. Bohannon et al. [63] used a magnetic position tracking system to better characterize the joint kinematics during the testing procedure, and Yeh et al. [64] used empirical mode decomposition along with phase-amplitude coupling analysis between sEMG and joint movement to quantify spasticity. A prominent challenge in the application of the pendulum test is insufficient data on the interaction between muscle activity and joint kinematics due to limited swinging of severely spastic patients. However, the nonlinear parameters described by Yeh et al. [64] overcome that challenge.

Myotonometry is a new technique that measures the stiffness of muscle tissue, and therefore provides an objective assessment of spasticity [65]. The stiffness is measured by pushing a probe onto a muscle and the underlying tissue and quantifying tissue displacement with respect to perpendicular compression force [66, 67]. Measurements can both be performed at rest and during muscle contraction and have been shown to correlate with the RMS of surface electromyography (sEMG) data [67]. Myotonometry benefits from fast data acquisition, easy analysis procedures, and high intra- and inter-rater reliabilities [68].

The concept of spastic catch or the angle of catch (AOC), which was initially described by Tardieu, Shentoub and Delaru [69], has been further researched and refined over the years. The AOC is nowadays most commonly assessed using the MTS

#### *Quantifying Spasticity: A Review DOI: http://dx.doi.org/10.5772/intechopen.112794*

and is defined as the angle at which a sudden increase of resistance is felt as a reaction to fast passive stretch. Wu et al. [70] developed an evaluation device consisting of a torque sensor to measure the joint torque and a potentiometer to measure the joint angle. Consequently, they were able to analyze four parameters, namely the position, torque, velocity and torque rate, and investigate their relationship to the AOC. They determined that the AOC is related to the velocity of the joint and further hypothesized that this velocity dependence might be due to a position dependence. Moreover, they discovered that the peak of torque change rate should be used as an indicator of the catch angle [42, 70]. Bar-On et al. [71] investigated the role of joint velocity and torque signals in the measurement of the catch angle. The torque was measured using force sensors at the medial hamstrings and gastrocnemius. They constructed three different definitions of the AOC, the first one based on the position of the maximum deceleration, the second one based on the position of the maximum rate of change of the torque, and the third one based on the angular position corresponding to the first local minimum of power after a local maximum of power. The power was defined as the product of the angular velocity and torque, and consequently, the third definition combines both signals. They found that all the AOC definitions were reliable, although the third definition was the best one. This is due to the fact that the individual signals of velocity and torque were lacking correlation so their integration proved to be the most advantageous. A summary of the most relevant biomechanical methods described in this section is displayed in **Table 3**, organized by relevant author/s, brief description, and main advantages and limitations in a nutshell.



**Table 3.**

*Summary of the biomechanical methods to assess spasticity and their description, advantages, and limitations in a nutshell.*

### **5. Neurophysiological methods**

The AOC can be regarded as a biomechanical method, whereas it is focused on the resistance felt in a joint. However, with the addition of sEMG, the similar mechanisms of the stretch reflex can be analyzed [16]. The stretch reflex (SR) is defined as a sudden increase in muscle activity during a fast passive stretch of a joint and is of a neurophysiological origin. Based on the fact that the muscle activity onset of the stretch reflex precedes the resistance felt in the AOC measurements, van den Noort et al. [16] contemplated that the muscle activity due to a hyperactive stretch reflex is the cause of the catch felt in fast passive stretching. The stretch reflex can be

#### *Quantifying Spasticity: A Review DOI: http://dx.doi.org/10.5772/intechopen.112794*

characterized by the threshold at which it is elicited or the reflex gain [72], which is defined as the change in number of motoneurons recruited per change in muscle length. The stretch reflex threshold has been demonstrated to provide a better measure of spasticity than the gain [18, 73–75], and Lance's definition [6] indeed suggests that the stretch reflex threshold should be the core of spasticity measurements. The concept of the SR threshold is based on the lambda model of motor control, which was formulated by Feldman et al. [76]. The lambda model, also called the equilibrium point hypothesis states that within a certain threshold (defined as l- to l+) the stretch reflex can be regulated, whereas in that range the central nervous system can control the joint angle and muscle torque appropriately. In non-spastic subjects, these thresholds lie outside the biomechanical range of joints but with the onset of neurological damage to the descending pathways, one limit of the threshold range might be shifted so that it is located within the biomechanical range and the patient has no ability to shift it. This leads to the hypersensitivity of the stretch reflex and the premature muscle contractions that are characteristic for spasticity [18, 73, 74].

Various different mechanisms, definitions, and methods to measure this threshold have surfaced. The quantification of the stretch reflex threshold is often carried out using either force coordinates or the latency at which muscle activity is elicited following a stretch [77]. However, following the development of the lambda model of motor control, the thresholds are generally expressed in velocity and angular coordinates [52, 78]. As a consequence of the velocity-dependent aspect of spasticity, dissimilar SR thresholds are encountered at different velocities. These are the dynamic SR thresholds (DSRT), and the slope of their regression line is defined as the sensitivity of the stretch reflex (). A positive value is indicative of a damping response to passive stretch dependent on velocity, while a negative value indicates an antidamping response. Increased sensitivity to the stretch reflex can be explained by reduced presynaptic inhibition of Ia primary fiber afferent inputs but might also be the result of deficits in dynamic fusimotor control of muscle spindle afferent discharges [79]. The tonic SR threshold (TSRT), which is generally defined as the muscle length at which motoneuronal recruitment begins, can be determined by the extrapolation of the regression line of the dynamic SR thresholds to a zero velocity but can also be estimated using quasi-static stretches of the muscle. The two methods have been shown to yield similar results [80]. The TSRT can be viewed as the angle below which the joint can be statically positioned without interference from unwanted muscle contraction [18, 52] or the excitability of motoneurons at zero velocity. When the TSRT lies within the biomechanical range of motion of a joint, spasticity is considered to be present. Additionally, the TSRT is inversely proportional to the severity of spasticity, namely the lower the TSRT, the more severe the spasticity is considered [52]. The sensitivity of the stretch reflex () has been shown to positively correlate with the TSRT in spastic muscles [18, 52].

Musampa et al. [81] used the concept of the SR threshold to establish the approach of spatial spasticity zones, which are the configurations of a joint in which spasticity is present. They then characterized each threshold borderline by its position in joint space and its shape and confirmed a reduction in the range of regulation of the stretch reflex in spastic patients. At last, they found abnormal muscle activation patterns of agonist and antagonist muscles of the whole shoulder joint space during stretch. This finding was also described by Jobin and Levin [18] who studied the SR threshold in children with CP. Germanotta et al. [52] further investigated the stretch reflex in children with CP using a robotic device to impose muscle responses at controlled velocities and compared it to that of typically developing children. Their results

demonstrate that using the TSRT approach to measure spasticity of the plantarflexors of the ankle joint in children with CP is indeed feasible and that the addition of a mechatronic instrument is advantageous. Moreover, they confirmed the findings of Pisano et al. who hypothesized that involuntary muscle responses could also be elicited in healthy individuals [44, 82].

Calota et al. [52] developed the montreal spasticity measure (MSM), a portable device that exploits the concept of the stretch reflex. The MSM, which consists of a single-channel EMG, an electrogoniometer, and a laptop computer, measures the DSRT, while a clinician induces a passive stretch at different velocities. This enables a greater variability of input stretch velocities, thereby achieving a more reliable estimate of the TSRT. A moderate reliability for the MSM was reported, but there was no correlation between the TSRT values reported and the MAS scores. This finding, which is relatively prevalent for neurophysiological parameters [75], is in line with the observation of Pandyan et al. that the MAS is inadequate in characterizing the stretch reflex [40, 45]. Furthermore, Calota et al. highlighted the limitation of repetitive stretching in measurements of the DSRT and TSRT. Several studies have demonstrated an attenuation of the muscular resistance with repetitive stretching in both stroke and spinal cord injury patients [83, 84]. Further, repetitive stretching has been proven to result in elongation of muscle fascicles and an increase in sarcomere numbers [85]. Whereas time-dependent changes in motoneuronal excitability in healthy nervous systems have been reported to occur when the interval between muscle contractions is smaller than 6 seconds [86], the MSM protocol was designed with an inter-stretch interval of 10 seconds. In fact, repetitive stretching is also a problem when measuring the resistance to passive movement, whereas evidence suggests that the torque response to passive stretch decreases up to 50% after 20 to 30 cycles. This decrease has been attributed to changes in the viscoelastic properties of muscles [75]. The TSRT is believed to be influenced by both central and peripheral inputs, but the mechanical changes in motoneurons are considered to have less effect on it [78].

Although the stretch reflex seems to be the most used neurophysiological measurement method, the tendon reflex and H-reflex, which are also EMG-based, have gained momentum and established their advantage in spasticity measurement. These methods are concentrated around the same neuronal pathway but differ in the way that the reflex is elicited. As discussed, the stretch reflex is stimulated with a passive movement. However, the tendon reflex is based on a mechanical stimulus, while the H-reflex is the response to electrical stimulation.

The Hoffmann reflex or H-reflex was first described by Paul Hoffmann in 1910 and is presently used to characterize the excitability of the alpha-motoneurons. The H-reflex differs from the mechanically induced stretch reflex, whereas it bypasses the muscle spindles. It can, therefore, give valuable information on the modulation of the monosynaptic reflex activity in the spinal cord in spastic patients. The H-reflex, which is either a compound action potential or a group of essentially concurrent action potentials from neighboring muscle fibers, is elicited using a short duration and lowintensity electrical stimulus to excite sensory Ia afferent fibers. When the intensity of the stimulus is increased, motor axons are activated and send action potentials directly to the neuromuscular junction. This evokes another EMG response, which is termed the M-wave [87]. Either the H-reflex latency or the Hmax/Mmax ratio, which is the ratio of the maximum amplitudes (in V) of the waves and is generally considered an index of peripheral reflex excitability [88], are used to quantify the H-reflex [44]. Additionally, the Hslp/Mslp ratio was developed to evaluate the motoneuron excitability while eliminating the effect of changes in the peripheral region of the

monosynaptic reflex arc [89]. The H-reflex parameters have been confirmed to be sensitive to the presence of spasticity and an increase in the Hmax/Mmax ratio and a decrease in the H-reflex latency have been shown to be correlated with increases in spasticity [11, 88, 90]. However, a lot of factors, including muscle activity, sensory input, state of consciousness, and age, produce a large variability in the measurement of the H-reflex [91], and the Hmax/Mmax ratio has been shown to have a large intersubject variability [92]. Furthermore, several studies on the H-reflex have demonstrated no or poor correlation to clinical scales [44, 90, 93, 94].

The F-wave is also frequently analyzed in neurophysiological examination of spasticity. The Fwave is evoked due to backfiring of the alpha motor neurons in the anterior horn of the spinal cord. When a distal nerve is simulated an antidromic impulse travels to the spinal cord, where a few of the motor neurons backfire. This backfiring generates an orthodromic impulse that elicits a small muscle contraction [95]. In order to prevent a contamination of the F-wave with the overlapping of the H-reflex, a supramaximal stimulation of the nerve is performed whereas the H-reflex is only evoked with low-intensity stimulations [96]. Abnormalities in the latency and amplitude of the Fwave have been suggested to be more sensitive to spasticity than the H-reflex [97].

Pauri et al. [98] performed transcranial magnetic stimulation, generating muscle evoked potentials (MEP) to evaluate the effect of botulinum toxin-A injection in spastic muscles. Additionally, they analyzed the effect of the treatment on the H-reflex and the F-wave. They reported significant changes in the MEP latency and the central conduction time but a lack of modulation of the H-reflex and F-wave characteristics. They postulated that their results could be explained by perceiving spasticity as a tonic noise maintaining the potential of descending pathway neurons and their transmission at the spinal a-motoneuron levels at a condition closer to their excitability threshold.

Jang et al. investigated the relationship between neurophysiological measures and clinical scales in children with CP after a botulinum toxin-A treatment and found that the amplitude of the tendon reflex had the strongest correlation with the MTS [93]. The tendon reflex is not only dependent on the excitability of alpha motoneurons but also involves the fusimotor system. It can, therefore, be argued that it is more sensitive to spasticity than the H-reflex [91]. The tendon reflex is initiated by tapping a tendon, generally the patellar tendon [99] or the Achilles tendon [93]. The reflex is then characterized by an output measure such as the time interval to an EMG response, bounce-back forces, or joint torque response [100]. Consequently, measurements of the tendon reflex are not always EMG-based. In particular, Zhang et al. [101] found that parameters based on the torque response correlated better with clinical scales than an EMG-based parameter. Using a tendon hammer with a force sensor mounted at its head along with a torque sensor, they measured the tendon tapping force, quadriceps EMG signals, and knee joint extension torque when initiating a tendon reflex in spastic MS patients. The impulse response of the tendon reflex was then calculated with the tendon tapping force as the system input and the reflex torque as the system output. They then derived three parameters to characterize the shape and amplitude of the tendon reflex impulse response, namely the tendon reflex gain (Gtr [cm]), the contraction rate (Rc [m/s]), and the reflex loop delay (td [ms]). Gtr was defined as the system gain, Rc as Gtr divided by the contraction time, and td as the interval from the onset of the tapping force to the onset of a torque response. In fact, those parameters were found to correlate better with clinical scales, such as the Ashworth scale, than the peak EMG reflex signal.

Electromechanical delay (EMD) is defined as the time between the onset of EMG activity to the onset of biomechanical force or movement, and therefore describes the time needed for electrochemical muscle activation, crossbridge formation, and elastic stretch [102]. Granata et al. [99] used the concept of the EMD to quantify the tendon reflex using the tendon tap as a stimulus to elicit muscle activation. They reported a decreased EMD in spastic muscle and attributed it to increased musculotendinous stiffness. In fact, EMD has been shown to be inversely proportional to stiffness [103]. Further, a reduced EMD in spastic patients suggests that the frequency of the peak response of muscles introduced to sinusoidal perturbations must be higher in spastic patients than normal subjects. This is in line with the results of Gottlieb et al. [104] who applied sinusoidal torques at different frequencies to the ankle joint and recorded the joint angle, torque, and EMG. Their results demonstrated a tendency toward higher resonance frequencies in spastic muscles than in normal muscles.

## **6. Onset detection**

Most of the neurophysiological methods are dependent upon the onset of muscle activity, which is determined from the EMG data. The onset detection can be a challenging task, especially when the EMG response or the signal-to-noise ratio (SNR) is low. Most studies have exploited the amplitude of the EMG signal to detect the onset [105], either by visual inspection or by setting a threshold [106]. A common threshold used is two standard deviations above the mean baseline value of the EMG signal, which has been reported to be inadequate in low spasticity subjects [52]. In fact, onset detection methods based on the amplitude of the signal are sensitive to noise and their performance reduces as the SNR of the surface EMG signal is decreased [105]. Several methods have been developed to compensate for this, such as the double threshold detector, which was especially designed for gait analysis [107] and wavelet template matching [108] along with methods utilizing statistical criterion determination [109, 110]. However, these methods are computationally intense, which can be problematic in the clinical setting. The Teager-Kaiser energy operator (TKE), which computes the instantaneous energy changes of signals made up of a single frequency varying in time, was formulated to address these problems [111]. The TKE operator is nonlinear and is sensitive to the instantaneous amplitude and frequency of the signal. Since increases in both frequency and amplitude accompany the firing of a motor unit, the difference between the EMG signal and background noise becomes clearer in the Teager-Kaiser domain [112]. Consequently, onset detection methods, such as visual detection, threshold algorithms, and statistical approaches, will perform better when the TKE operator is included in the signal conditioning [111]. However, the TKE operator is primarily effective against noise with Gaussian distribution [105]. Another type of noise that can affect the signal quality is spurious background spikes. These spikes can develop due to motion artifacts at the skinelectrode surface or because of interference from a radio transmission and electrical wires. Zhang et al. [105] developed an onset detection method based on sample entropy that can highlight bursts of EMG activity but has low sensitivity to spurious spikes. Sample entropy is a measure of the complexity and randomness of a system. When muscle activity is elicited an increase in the complexity of the EMG signal follows, which is not true for spurious background spikes. Conditioning the signal with the sample entropy algorithm, therefore, facilitates muscle onset detection. At last, the use of the Hilbert-Huang transform (HHT) in onset detection will be briefly

discussed. The HHT, which is applicable to nonstationary and nonlinear signals, is based on the concept of empirical mode decomposition that can break down complex signals into finite intrinsic mode functions [113]. The transform combines both nonlinear dynamics and time-frequency analysis and is therefore strongly suitable for EMG signal processing [114]. Furthermore, the HHT and entropy analysis have been combined into a method termed the Hilbert-Huang transform marginal spectrum entropy (HMSEN), which has shown effectiveness in seizure detection of electroencephalography (EEG) signals [115]. Hu et al. [114] utilized the HMSEN and the rootmean-square (RMS) of sEMG signals to develop a novel clinical assessment method for spasticity. The method identifies the stretch reflex onset from the sEMG signal using the HMSEN and then compares the RMS of the baseline of the signal to the RMS of a fixed length of signal obtained directly after the detected onset. The difference is then used to quantify spasticity.

## **7. Imaging techniques**

Several medical imaging modalities have been used to aid in spasticity measurement. Positron emission tomography (PET) and magnetic resonance imaging (MRI) have been used to assess spasticity in stroke patients but are relatively unused in clinical practice [11]. Conventional B-mode ultrasonography has also been used to quantify spasticity by determining muscle architectural parameters, including muscle thickness, fascicle length, and pennation angle [116]. Evidence suggests that these architectural parameters have significantly lower values in spastic muscles [117, 118], although the decrease of fascicle length in children with CP has been disputed [119]. The parameters have been shown to have good reliability [120], but they do not supply information on the muscle stiffness. The muscle stiffness can, however, be quantified using elastography, which is a newly developed imaging technique. Elastography is based on applying stress to tissues and measuring the displacement [118] and has been used to measure the flexibility of muscles, tendons, and nerves, and thereby quantifying spasticity [121]. Several different elastography techniques have been introduced and are classified based on the method used to develop stress and measure the displacement. These methods include sonoelastography, shear wave elastography, transient elastography, and acoustic radiation force elastography, which are all based on ultrasound [119] along with magnetic resonance elastography [122]. Of those techniques, sonoelastography, which is based on applying compression to the target tissue by hand, is the most used [118, 123]. Sonoelastographic measurements have been proven to be able to differentiate between spastic and unaffected muscles but do not correlate with the MAS [113] or muscle architecture parameters obtained with B-mode ultrasonography [118]. Shear wave elastography is also frequently used in spasticity quantification and has been shown to be promising in monitoring the structural and viscoelastic properties of spastic muscles [124]. Shear wave elastography is based on using the ultrasound probe to generate transient shear waves in the muscle. The shear waves are then detected again with the probe as they travel along the muscle fibers, and their speed is used to quantify muscle stiffness [125]. Shear wave elastography has been shown to be feasible in measuring spasticity [126], but high sensitivity to the measurement conditions has also been reported, resulting in a low reliability [124]. Generally, the quantification of elastographic data is done visually by using color grading [118] or by calculating elastic moduli [124, 125]. Additionally, a new five-point scale called the muscle elastography multiple sclerosis score

(MEMS) has been developed to quantify the elastography results based on the elasticity and distribution of muscle fibers [121].

#### **8. Multidimensionality**

The majority of the spasticity measurement methods developed during the last decade take advantage of instrumented approaches, which has increased their reliability remarkably. However, the complexity of the spasticity phenomenon further requires that these methods incorporate multidimensional parameters. Evidence suggests that clinical scales, biomechanical, and neurophysiological parameters of spasticity measurement have poor correlation [10, 12, 19, 127], so combining these methods is beneficial to be able to distinguish every aspect of the spasticity. In a recent study, McGibbon et al. [50] integrated biomechanical and neurophysiological parameters relevant for spasticity quantification using a wearable sensor system. They proposed a kinematic model of spasticity, which the parameters were extracted from. The model is based on constructing a motion curve of elbow stretch and comparing it to a reference curve. In healthy subjects, a consistency can be found between the reference curve and the actual motion curve, which cannot be identified in spastic subjects. Using the kinematic model, they further managed to obtain the spastic muscle interference force without an external force transducer device. Kristinsdóttir et al. used results from the pendulum test and sEMG recordings to construct a spasticity quantification parameter called the reflex period [128]. Bar-On et al. [51] also combined biomechanical and neurophysiological methods, integrating several sEMG and torque-related parameters explored around the maximum velocity, and comparing them between velocity conditions in order to assess the spasticity in children with CP. They reported that their parameters were sensitive to spasticity [51] and provided a more comprehensive assessment than clinical scales [129]. Interestingly, Falisse et al. [130] utilized this assessment method along with 3D gait analysis to develop spasticity models and recognized that a model relying on feedback from muscle force, and its time derivative (dF/dt) was best suited in explaining muscle activity during passive stretches and gait. This is consistent with a recent theory suggesting that muscle spindle receptors encode information about muscle force instead of length [131].

Mirbagheri et al. [21] managed to separate the corresponding contributions of neural and muscular components to the overall joint stiffness by using the integration of multidimensional signals. They applied pseudorandom binary sequence perturbations to joints of spastic patients and used a parallel cascade system identification technique to process the signals and quantify the different components. Their results revealed no correlation between either component and the MAS score [12]. Wu et al. [43] developed a four-dimensional characterization of spasticity, which includes the joint angle, velocity, torque, and torque change rate, in order to systematically quantify catch angle and spasticity. Centen et al. [132] developed a robotic exoskeleton to identify kinematic characteristics of resistance to passive movement in spastic subjects. They extracted seven parameters that described the resistance to passive movement from their measurements and found that two of them were suited to differentiate patients from healthy subjects. These parameters were peak velocity, which was the most effective in identifying spasticity, and the between arm-peak velocity difference, which used the less affected side of the subject as a reference.

#### *Quantifying Spasticity: A Review DOI: http://dx.doi.org/10.5772/intechopen.112794*

Advances in statistical methodologies, such as the development of machine learning algorithms, have been beneficial in managing the large datasets that accompany the analysis of multiple parameters. Zhang et al. used a supervised regression learning algorithm to process biomarkers and yield evaluation scores from a simple examination procedure using wearable surface EMG and inertial sensors [133]. The biomarkers were extracted from the previously mentioned kinematic [50] and lambda [76] models, which were constructed from the recorded data.

### **9. Discussion and conclusions**

In this review, multiple aspects of spasticity measurement have been discussed. The review was focused on four essential classes of quantification methods, namely clinical scales, imaging techniques, biomechanical, and neurophysiological methods. Clinical scales, which are based on initiating a brisk dorsiflexion movement of the relaxed ankle and grading the resistance, are the most prevalent methods for clinical assessment of spasticity. The Ashworth and Tardieu scales have been most widely used but recently there has been development in the field, with new scales such as the ASAS [38] and SPAT [48] being created. Clinical scales that allow for two or more different velocities in the dorsiflexion movement have been shown to have higher reliability than those based on a single velocity [32, 39]. Clinical scales are advantageous in the sense that they are clinically feasible and do not require heavy equipment and instrumentation, but their main limitation is that they are based on a subjective assessment. Therefore, quantified comparison of the severity of spasticity of a muscle from time to time or between persons is limited.

Biomechanical methods are based on quantifying the resistance of a joint, most often by measuring force and torque using dynamometers [16]. Most often passive movement is used to elicit spasticity, but voluntary movement has also been shown to be correlated with the severity of spasticity [57]. The Wartenberg pendulum test, which was introduced in the 1950s, can be classified as a biomechanical measurement method, whereas it is based on the oscillatory resistance characteristics of the lower limbs [58]. The instrumentation of the pendulum test, for example, with sEMG, goniometers [64], and magnetic tracking systems [63], has made its results more reliable and turned it into a promising method to use in clinical settings. The quantification of the angle of catch, which is included in the MTS protocol [69], can also be perceived as a biomechanical measurement method.

Neurophysiological methods are based on quantifying muscle activity, and thereby predicting neural mechanisms. A big share of neurophysiological methods is based on quantifying the stretch reflex and its threshold of elicitation, which is usually defined using velocity or angular coordinates [52, 78]. Quantification of the H-reflex and tendon reflex are two other prevalent methods that belong to the neurophysiological class. In fact, these methods are all quantifying the same physical phenomenon, the stretch reflex, but differ in the way that the reflex is evoked. The stretch reflex, which is a monosynaptic spinal pathway, can be activated using passive movement, a mechanical stimulus, or electrical stimulation and measurement methods that make use of the stretch reflex can be classified accordingly. The H reflex and the F-wave are based on electrical stimulation, the tendon reflex is based on a mechanical stimulus (a tendon tap), and the stretch reflex is based on passive movement. Neurophysiological methods can be further classified into methods that are based on EMG recordings and those who are not. When EMG signals are brought into play the question of how to

detect the muscle onset arises. Visual detection is frequently used but methods based on the amplitude of the signal are sensitive to noise. Consequently, methods based on energy and sample entropy have demonstrated better results [105, 114].

Imaging techniques have also been used quantify spasticity. Conventional B-mode ultrasonography can determine architectural parameters of muscles, such as muscle thickness, fascicle length, and pennation angle, which have been shown to have lower values in spastic patients [116]. Elastography, which is based on applying stress to tissues and measuring the displacement, has also been used for spasticity quantification. Elastography is advantageous, whereas it can measure muscle stiffness, which has been shown to be a good indicator of spasticity [126].

Spasticity quantification methods can further be classified according to whether they are instrumented or not. A subclassification of instrumented approaches is based on whether the limb is moved by another individual or a mechatronic device during the measurement. The instrumentation of quantification methods generally improves their accuracy [53]. At last, measurement methods can be categorized based on the dimensionality of the signals obtained. Unidimensional methods are limited when it comes to measuring spasticity, whereas its complexity requires a combination of physiological parameters for accurate classification. It has been shown that methods from the four different classes mentioned have poor correlation internally, which emphasizes the use of multidimensional parameters [2, 10, 19, 127].

To conclude, this review has provided a general overview of the methods used to quantify spasticity hitherto. No conclusion was made on the most desirable method to measure spasticity, although the benefits of instrumentation and multidimensionality are emphasized. However, the review can hopefully aid in distinguishing which methods are the most promising and eventually pave the ground for establishing a gold standard for spasticity measurement.

In order to be able to accurately quantify spasticity, there has to be a consensus on its definition and fundamental mechanisms. According to Lance's definition [6], spasticity is a hyperexcitability of the stretch reflex. However, few of the methods are directly measuring this core of the pathology but rather its consequences. In particular, if the gamma motor neuron system can be directly stimulated, a better understanding of the sensitivity of the reflex mechanisms can be obtained. Also, a deeper understanding of the interplay between the muscle spindles and the spinal neural network could pave the way for a better quantification method. Ultimately, as spasticity research progresses, its quantification methods will improve. In the meantime, this review of current methodologies and knowledge will hopefully be of value for future research.

## **Abbreviations**


*Quantifying Spasticity: A Review DOI: http://dx.doi.org/10.5772/intechopen.112794*


## **Author details**

Kristjana Ósk Kristinsdóttir1,2, Samuel Ruipérez-Campillo2,3,4,5 and Þórður Helgason<sup>1</sup> \*

1 Health Technology Center, Reykjavik University – Landspitali-University Hospital, Reykjavik, Iceland

2 Department of Bioengineering, University of California, Berkeley, USA

3 School of Medicine, Stanford University, Palo Alto, USA

4 Department of Computer Science, ETH Zürich, Zürich, Switzerland

5 ITACA Institute, Universitat Politecnica de Valencia, Spain

\*Address all correspondence to: thordur@landspitali.is

© 2023 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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## *Edited by Amit Agrawal*

Adequate stroke care access not only requires the availability of comprehensive healthcare facilities but also needs affordability and knowledge of the availability of these services. Presentations of stroke syndromes depend on their location, size, and underlying comorbidities. These lesions can have clinical features of increased intracranial pressure, focal neurological deficits, or seizures (generalized or partial) with rapid progression. This book presents high-quality research work on recent advances in the management of stroke.

Published in London, UK © 2023 IntechOpen © vsijan

Stroke - Management Pearls

Stroke

Management Pearls

*Edited by Amit Agrawal*