Novel MS vital sign: multi-sensor captures upper and lower limb dysfunction

Abstract

Objective: To create a novel neurological vital sign and reliably capture MS-related limb disability in less than 5 min.

Methods: Consecutive patients meeting the 2010 MS diagnostic criteria and healthy controls were offered enrollment. Participants completed finger and foot taps wearing the MYO-band© (accelerometer, gyroscope, and surface electromyogram sensors). Signal processing was performed to extract spatiotemporal features from raw sensor data. Intraclass correlation coefficients (ICC) assessed intertest reproducibility. Spearman correlation and multivariable regression methods compared extracted features to physician- and patient-reported disability outcomes. Partial least squares regression identified the most informative extracted textural features.

Results: Baseline data for 117 participants with MS (EDSS 1.0-7.0) and 30 healthy controls were analyzed. ICCs for final selected features ranged from 0.80 to 0.87. Time-based features distinguished cases from controls (P = 0.002). The most informative combination of extracted features from all three sensors strongly correlated with physician EDSS (finger taps r_s = 0.77, P < 0.0001; foot taps r_s = 0.82, P < 0.0001) and had equally strong associations with patient-reported outcomes (WHODAS, finger taps r_s = 0.82, P < 0.0001; foot taps r_s = 0.82, P < 0.0001). Associations remained with multivariable modeling adjusted for age and sex.

Conclusions: Extracted features from the multi-sensor demonstrate striking correlations with gold standard outcomes. Ideal for future generalizability, the assessments take only a few minutes, can be performed by nonclinical personnel, and wearing the band is nondisruptive to routine practice. This novel paradigm holds promise as a new neurological vital sign.

Figure 1

Overview of signal processing approach. EMG, electromyogram, PLS, Partial least squares. Participants performed 20 finger and foot taps as fast as they could while wearing the MYO‐band. The surface EMG data were used to extract the times for completing 20 taps for each limb. All three sensors were used for textural feature extraction. A PLS method was used to determine which sensor(s) and textural features were most informative. Lastly, a final scalar statistic for the textural analysis (“T‐metric”) was derived from the PLS model.