Alternative methods of normalising EMG during cycling

Abstract

We evaluated possible methods of normalisation for EMG measured during cycling. The MVC method, Sprint method and 70% Peak Power Output Method were investigated and their repeatability, reliability and sensitivity to change in workload were compared.

Thirteen cyclists performed the same experimental protocol on three separate occasions. Each day, subjects firstly performed MVCs, followed by a 10 s maximal sprint on a cycle ergometer. Subjects then performed a Peak Power Output (PPO) test until exhaustion. After which they cycled at 70% of PPO for 5 min at 90 rpm. Results indicated that normalising EMG data to 70% PPO is more repeatable, the intra-class correlation (ICC) of 70% PPO (0.87) was significantly higher than for MVC (0.66) (p = 0.03) and 10 s sprint (0.65) (p = 0.04). The 70% PPO method also demonstrated the least intra-subject variability for five out of the six muscles. The Sprint and 70% PPO method highlighted greater sensitivity to changes in muscle activity than the MVC method. The MVC method showed the highest intra-subject variability for most muscles except VM.

The data suggests that normalising EMG to dynamic methods is the most appropriate for examining muscle activity during cycling over different days and for once-off measurements.

Introduction

Measuring biological variation in the EMG signal is important in studies where surface EMG is used to gain understanding of physiological regulation, it is therefore necessary to minimise the variation caused by intrinsic and extrinsic factors that affect the raw signal (Burden and Bartlett, 1999). The extrinsic or experimenter-related factors can be controlled through the correct application and placement of electrodes supported by a thorough understanding of electrophysiology and anatomy. Whereas the intrinsic factors including muscle fiber type, muscle fiber diameter and length and the amount of tissue between muscle vary between subjects and cannot be controlled. A means in reducing the variation in the EMG signal is in part achieved through the appropriate method of normalisation. Normalisation involves the comparison of the EMG signal to a reference value obtained during standardised reproducible conditions, thus allowing comparison of activity between different muscles, across time, and between individuals.

Methods of normalisation for EMG activity measured during both static and dynamic exercise have been reviewed in detail (Bolgla and Uhl, 2007, Knutson et al., 1994, Burden and Bartlett, 1999, Kollmitzer et al., 1999). Over the past few months there have been a high number of publications investigating alternative normalisation methods to the MVC method including, single leg stance (Norcross et al., 2010), isokinetic squat jumps and 20 m sprints (Ball and Scurr, 2010), maximal and sub-maximal contractions (Murley et al., 2010, Chapman et al., 2010). These publications have all attempted to find alternative methods to normalising EMG during dynamic activity. However, normalisation of EMG data during dynamic activities such as cycling is relatively poorly understood, and there are presently few alternatives to the use of the isometric maximal voluntary contraction (MVC) method (Marsh and Martin, 1995, MacIntosh et al., 2000, Ricard et al., 2006).

The use of the MVC as a method of normalisation has theoretical constraints, since, during cycling there are variations in actions of the muscles involved. For example, changes in joint angle, joint angle velocity, and muscle lengthening and shortening all occur during cycling (Farina et al., 2004a), raising questions about the efficacy of the MVC method as a normalisation tool for EMG data measured during cycling (Farina et al., 2004a, Hunter et al., 2002). This is particularly the case when the EMG activity is being used in an attempt to quantify how much skeletal muscle may be active. Researchers have in the past made direct comparisons between the EMG signal measured during cycling and the EMG activity measured during an isometric MVC, expressing the resultant value as a percentage of maximum (for example, 45% of the muscle was active at volitional exhaustion). Quite clearly, this comparison is questionable on the grounds that the two types of muscle contractions are very different in nature. This creates the need to evaluate other possible means for normalising EMG activity during cycling.

The MVC method has proved popular amongst researchers (Ricard et al., 2006, Potvin, 1997, Marsh and Martin, 1993, Ericson et al., 1986, Arsenault et al., 1986, Dubo et al., 1976) but has certain limitations. For example, it is assumed that subjects provide maximum effort during testing and that the maximal contraction achieved represents 100% of muscle activity (Burden and Bartlett, 1999). Although researchers can encourage the subject to generate a MVC, the force generated ultimately depends on the level of motivation of the subject.

Furthermore, the MVC typically involves an isometric knee extension movement at an angle of 60° from full extension, a movement which does not occur during cycling and can be argued to be non-functional. Despite these limitations few studies have addressed the appropriateness of the MVC method or proposed alternative methods of normalisation during exercise trials. One of the first studies that investigated alternative methods of normalisation was done by Hunter et al. (2002), investigating EMG methods of normalisation in cycling by using two angles (60° and 180°) with four types of pedal contractions. The investigation found that the isometric MVC produced a higher integrated EMG (iEMG) value than the protocols using knee angles at 60°, 180° or one dynamic maximal cycle pedal revolution. This finding suggests that muscle activity during cycling does not provide the highest EMG amplitude. This study however did not investigate the repeatability of the various normalisation protocols but rather only investigated which of the four protocols produced the greatest iEMG activity, and therefore has the same potential limitation with regards to the comparison between cycling exercise and static, isometric contractions. A more recent study by Rouffet and Hautier (2008) investigated the use of maximal cycling sprints compared to isometric MVC’s for EMG normalisation in cycling. The study found that the maximal sprint was as repeatable as the MVC method in measuring peak EMG amplitude. In addition they highlighted the fact that the sprint method was less time and energy consuming. This study however did not measure between day repeatability of the normalisation methods, only the within day repeatability.

Therefore the most appropriate method for normalising muscle activity during cycling has yet to be established, especially for studies conducted over a number of days. To address this challenge, the first requirement was to determine an appropriate method of normalisation that fulfils certain criteria namely.

• Repeatability

  An appropriate method of normalisation would need to be highly repeatable. Repeatability should consider two different and complementary aspects; (1) reliability, which addresses between-day variations of measured variables; and (2) constancy, which addresses within-day variations of measured variables. Repeatability would ensure precision during the measurement and lowest variation in repeated trials with altered electrode positioning (Burden et al., 2003, Rainoldi et al., 2001). Intra-class correlation (ICC) is the most commonly used statistical method to identify repeatability of the EMG signal (Bolgla and Uhl, 2007, Laplaud et al., 2006, Mathur et al., 2005, Rainoldi et al., 2001, Ng and Richardson, 1996). A high ICC (closest to 1) is associated with a small within-subject variance relative to the between-subjects variance (Hopkins, 2000).

• Reliability

  Reliability refers to the extent to which measurements are consistent, dependable, and free from error (Portney and Watkins, 1990). Reliability also refers to the stability and consistency of measures with respect to time so that changes between the measurements can be attributed to the intervention (Keskula et al., 1995). One of the main contributors to reliability is the within-subject variation which affects the precision of estimates of change in the variable of an experimental trial (Hopkins, 2000). The coefficient of variation (CV) is the statistical method often used to evaluate intra-subject variability.

• Sensitivity

  Sensitivity is defined as the ability to detect true biological variations. In the context of this study, the ability of the method to assist in the measurement of actual change in muscle activity rather than the measurement of the combination of the biological and experimental factors that affect the signal, is a requirement of a good method of normalisation. It is reasonable to expect muscle force output and EMG amplitude to have a linear relationship, as they both depend on the number of motor units recruited (Farina et al., 2004b). Thus an ideal method of normalisation should to some extent, be able to identify changes in muscle activity due to the change in workload or exercise intensity, if fatigue is not present.

It is important that the EMG measured during the normalisation procedure is consistent from 1 day to the next (repeatability), and also that when the EMG activity measured during dynamic activity is normalised against the chosen procedure, the value obtained must reflect changes in EMG that occur as the exercise workload increases (sensitivity).

Thus the aim of the study was to examine and evaluate possible methods for normalisation of EMG measured during cycling, according to the requirements for a good normalisation technique which are described above. Three methods for normalising EMG during cycling, which are based on previous research (Bolgla and Uhl, 2007, Yang and Winter, 1984, Yang and Winter, 1983), were evaluated and their variability and sensitivity to change was compared to the method of normalisation using isometric MVC.

• MVC method – maximal static normalisation and is the current widely-used “standard” method

• Sprint method – maximal dynamic normalisation

• 70% Peak Power Output Method – sub-maximal dynamic normalisation, this method was based on studies that found sub-maximal MVC’s to be more repeatable and reliable than 100% MVC’s (Kollmitzer et al., 1999, Mathur et al., 2005, Yang and Winter, 1983). We therefore aimed to investigate the repeatability and reliability of using a sub-maximal dynamic method.