On the effects of signal processing on sample entropy for postural control

Abstract

Sample entropy, a measure of time series regularity, has become increasingly popular in postural control research. We are developing a virtual reality assessment of sensory integration for postural control in people with vestibular dysfunction and wished to apply sample entropy as an outcome measure. However, despite the common use of sample entropy to quantify postural sway, we found lack of consistency in the literature regarding center-of-pressure signal manipulations prior to the computation of sample entropy. We therefore wished to investigate the effect of parameters choice and signal processing on participants’ sample entropy outcome. For that purpose, we compared center-of-pressure sample entropy data between patients with vestibular dysfunction and age-matched controls. Within our assessment, participants observed virtual reality scenes, while standing on floor or a compliant surface. We then analyzed the effect of: modification of the radius of similarity (r) and the embedding dimension (m); down-sampling or filtering and differencing or detrending. When analyzing the raw center-of-pressure data, we found a significant main effect of surface in medio-lateral and anterior-posterior directions across r’s and m’s. We also found a significant interaction group × surface in the medio-lateral direction when r was 0.05 or 0.1 with a monotonic increase in p value with increasing r in both m’s. These effects were maintained with down-sampling by 2, 3, and 4 and with detrending but not with filtering and differencing. Based on these findings, we suggest that for sample entropy to be compared across postural control studies, there needs to be increased consistency, particularly of signal handling prior to the calculation of sample entropy. Procedures such as filtering, differencing or detrending affect sample entropy values and could artificially alter the time series pattern. Therefore, if such procedures are performed they should be well justified.

Control Mechanisms of Static and Dynamic Balance in Adults With and Without Vestibular Dysfunction in Oculus Virtual Environments

“…Then we measured the length of the position curve in either direction (AP or ML) as the sum of the absolute value of the position change at every time stamp [24]. In contrast, SampEn is a measure of postural sway regularity [25], and a filter or other smoothing procedures can remove unique features of a time series [26,27]. SampEn is defined as the negative natural logarithm of the conditional probability that a subsequence of length m, which matches pointwise within a tolerance r, also matches at the next point.…”

Development of a Virtual Reality-Based Game Approach for Supporting Sensory Processing Disorders Treatment

“…Postural balance problems that affect vestibular, visual and proprioceptive senses may be caused by SPD [2]. Many works [9][10][11][12]14] used VR to help patients suffering from postural balance control. In these works, VR was used to stimulate vestibular and proprioceptive senses, through the use of games and virtual reality equipment for body movement capture.…”

“…Lubetzky et al [11] used the Oculus Rift TM to capture head position and orientation of patients while executing postural control exercises. Later on, Lubetzky et al [9] applied a similar VR approach to test different parameter choices (embedding dimension and radius similarity) for the analyses of sample entropy, a measure used to study postural control. Rodrigues et al [12] used force feedback devices with Xbox Kinect TM so that patients could position themselves in a correct posture (either sitting or standing) by visualizing their body virtually and feeling force feedback in their hands.…”

“…A literature review indicated that Virtual Reality (VR) has been used to support treatment for various health conditions, including disorders that have SPD as a symptom, such as Vestibular Disorders [9][10][11][12][13][14], Autism Spectrum Disorder (ASD) [15][16][17][18][19][20], Attention Deficit Hyperactivity Disorder (ADHD) [21][22][23][24][25] and Speech Disorder [26,27]. All of these VR solutions have been used for rehabilitation and treatment of patients with different conditions, many of which are closely related to SPD.…”

Measuring Complexity of Biomedical Signals

“…However, in practice, many challenges emerge when these complexity measures are applied, such as the influence of the noise, the quantization effects, the lengths of the available data, or the parameter tuning. Many of these issues are still unsolved [6][7][8]. How to cope with these difficulties and how to obtain tools that can be employed in clinical practice are the subjects of this special issue.…”