Involuntary muscle contractions (spasms) are a major secondary consequence of spinal cord injury. These spasms disrupt mobility and the ability to perform daily activities. The rhythmic repetitive muscle contractions of clonus are one kind of spasm. In this study an algorithm was developed to automatically detect the start and end times of EMG bursts during clonus. These measures were used to calculate the duration of EMG bursts, clonus frequency and the intensity (root mean square) of each EMG burst, parameters that characterize clonus.
This algorithm relied on the technique of intensity analysis (Von Tscharner 2000). Filters were created by non-linearly scaling a Mother (Morlet) wavelet to produce envelopes of the EMG in different frequency bands. The intermediate frequency band (80-190 Hz) enveloped the EMG best and was used to detect the EMG bursts during clonus.To detect the EMG bursts, an intensity threshold and time separation threshold were imposed on the algorithm to eliminate multiple peaks caused by the baseline EMG, motor units or EMG changes.
Window regions were extended between the midpoints of identified EMG peaks then resized to 50 ms on either side of each identified EMG peak. The start and end times of EMG bursts were at 5% and 95% of the energy contained in a window region, respectively. A motor unit threshold constraint was used to eliminate motor unit potentials at the beginning and end of clonus.
The algorithm output from 31 spasms in long term (24 hr) EMG data recorded from 8 paralyzed leg muscles of 7 subjects with a chronic cervical spinal cord injury were compared to that generated by two independent human operators. The algorithm was as good as a human operator at identifying EMG bursts (p = 0.946), clonus frequency (intra class correlation coefficient α = 0.949), contraction intensity (α = 0.997) and the durations of each burst of EMG during clonus (α = 0.852). On average the algorithm was 574 (SE 238) times faster than manual analysis by two people (p≤ 0.001).
Analysis of clonus in one 24 hour dataset from the right medial gastrocnemius muscle with the algorithm showed that clonus was more prevalent and stronger during awake versus sleep time. This algorithm can be used to analyze long term recordings accurately with limited user intervention. The algorithm may also be a prospective diagnostic tool to judge the effectiveness of interventions such as drugs like baclofen that are used to mitigate clonus.
Anatomy of the Spinal Cord:
The spinal cord is a crucial part of the central nervous system in humans extending from brain to the lower back (coccyx). It is protected by a column of 33 bones (vertebrae). These vertebrae are divided into 5 sections namely cervical (vertebrae; n = 7), thoracic (n = 12), lumbar (n = 5), sacral (n = 5) and coccyx (n = 4) depending on their location. Ventral and dorsal roots exit these vertebrae and are named according to their origin in the spinal column.
Spinal Cord Injury Classification:
The spinal cord links the brain and the body by signaling both motor and sensory information to coordinate activities of living. Any lesion to this conduit may result in disruption of motor and/or sensory information from brain to periphery and vice versa. The body functions that are compromised or disrupted depend on the segment of the cord that is injured, and the severity of the injury. As a sequel to this traumatic injury a wide spectrum of consequences are encountered.
Stretch Reflex Pathway:
When a brief sudden stretch is applied to a joint, the muscle spindles respond by sending afferent discharges along Ia nerve fibers to the spinal cord. These Ia afferent inputs activate the parent motoneurons located in the ventral horn of the spinal cord resulting in contraction of the stretched muscle. There is simultaneous inhibition of the antagonistic muscle(s) via inhibitory interneurons.
Data Collection and Processing:
The EMG data were collected from 7 subjects (5 male and 2 female, mean age: 39 yr, SE 4, range: 27-52 yr) with a chronic cervical spinal cord injury (mean time since injury: 18 yr (SE 4; range: 4-33 yr, Table 4.1). These injuries were caused by diving mishaps (n=4), motor vehicle accidents (n=2) and sports events (n=1).
The injuries were at C4 (n=1), C6 (n=5) or C7 (n=1) and were complete (AISA A) according to American Spinal Injury Association criteria (Maynard et al. 1997). The subjects had no voluntary control of any leg muscles, indicated by an inability to generate any voluntary EMG activity. Subjects took no medication to mitigate muscle spasms. Before participating in the study all subjects gave informed, written consent which was approved by the Institutional.
Algorithm to Detect the Onset and Offset of Each Burst of EMG During Clonus:
Three cycles of clonus, with the onset and offset of the EMG marked manually by lines. The process of using software to automatically determine the start and end of EMG during user defined spasms that include clonus are described in the subsequent sections of this chapter. The basis of this detection method is derived from the technique of “intensity analysis” proposed by Vincent Von Tscharner (2000).
Visual Comparison of Measurements:
The starts and ends of bursts of EMG marked by two different operators and the algorithm can be viewed simultaneously to evaluate the decisions. A MATLAB user interface was developed to display the EMG and the outputs in Dadisp.
Comparison of Outputs From the Algorithm and Human Operators:
To compare the measurements made by the two individual operators and the algorithm, a template was developed in Microsoft Excel. Person 1 was set as the standard and comparisons were made between Person 1 and Person 2 (P1 Vs P2), and Person 1 and the Program (P2 Vs Pr). Data from Person 2 and the Program were also compared but not reported in this study, because Person 2 developed the algorithm. Hence the comparisons were treated as biased.
Statistical analyses were performed using SPSS-17.0 software (SPSS Inc, Chicago, IL) and Sigmastat software (Systat Software, San Jose, CA). Two comparisons were made on the results: 1) between Person 1 and Person 2; 2) between Person 1 and the Program, to evaluate whether the algorithm was as good as a human operator in terms of its performance in analyzing clonus.
In this study, a total of 31 spasms involving clonus were analyzed by the program and manually by two different human operators. The algorithm developed in this study was evaluated for its performance in 6 ways by comparing the results of two people (Person 1, P1; Person 2; P2) to the data obtained from Person 1 and the Program (Pr).
Comparisons made were: 1) the number of common bursts of EMG identified as clonus; 2) differences in the start and end times of bursts of EMG; 3) agreement on On duration, 4) agreement for clonus frequency; 5) agreement on the RMS value of the EMG during the bursts of EMG; 6) the time taken to measure the start and the end times of EMG bursts during clonus.
The algorithm developed in this study using MATLAB aimed to automatically detect the start and end times of EMG bursts during clonus recorded from leg muscles paralyzed by SCI. The program was highly reliable and as accurate as two independent raters in identifying EMG bursts, measuring the duration of EMG bursts, clonus frequency, and the intensity of contractions. The algorithm was also significantly faster than people at measuring the start and end times of EMG during clonus.
The novel methods developed in this study were used to analyze EMG recorded from medial gastrocnemius over 24 hours from one subject who had a spinal cord injury at C4. This analysis showed that clonus was more common during awake time than during sleep. Clonus during awake hours also had much stronger contractions than those during sleep time.
In terms of potential applications of the algorithm, it may be useful to characterize clonus and its behavior across an entire day. The data gathered can answer questions about the prevalence of clonus after SCI and whether it can be dampened by medication, exercise or other interventions.
Source: University of Miami
Author: Chaithanya Krishna Mummidisetty