A Rapid and Unbiased Algorithm to Quantify Skeletal Muscle Twitch Kinetics

Abstract

2332 Muscle is a chemomechanical motor that converts chemical energy to mechanical work. Quantifying mechanical output is fundamental to understanding metabolism that fuels muscle contraction. Software for automated analysis of mechanical output is not readily available. PURPOSE: To develop software for automated quantification of skeletal muscle contraction kinetics. METHODS: An in situ mouse hindlimb stimulation protocol was used to generate mechanics data over a range of frequencies. Force measurements were recorded using a PC ADC board as well as a chart recorder. Spectral analysis of the noise components formed the basis for designing a smoothing Chebyshev filter. Peaks were identified using the stimulation interval. Baseline was estimated by linear interpolation of the minimum signal on the preceding and following peak-to-peak intervals. The time to peak force, peak force, tension time integral, and half relaxation time were calculated for each twitch after baseline correction. Results were compared to those obtained from planometric analysis of the chart recordings. RESULTS: A regression analysis of the digital results against planometric analysis is presented in Table 1. Each regression yielded a slope of unity except the half relaxation time, due to variance inherent in the planometric method and quantization error in the digital signal.Table 1: Regression analysis of the digital and planometric methods.CONCLUSIONS: This algorithm provides a rapid and reliable method that is readily amenable for skeletal muscle mechanics analysis and to other digitally acquired physiological data. Supported in part by MSU IRGP 41006, NSBRI MA00210, and the MSU Department of Radiology.