Publication Date

2009-01-01

Availability

Open access

Degree Type

Thesis

Degree Name

Master of Science (MS)

Department

Biomedical Engineering (Engineering)

Date of Defense

2009-04-02

First Committee Member

Dr. Jorge Bohorquez - Committee Chair

Second Committee Member

Dr. Christine K. Thomas - Committee Co-Chair

Third Committee Member

Dr. Weizhao Zhao - Committee Member

Abstract

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 (p = 0.949), contraction intensity (p = 0.997) and the durations of each burst of EMG during clonus (p = 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.

Keywords

Clonus; Wavelets; EMG

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