Biosensor vital sign detects multiple sclerosis progression

Abstract

Objective: To determine whether a small, wearable multisensor device can discriminate between progressive versus relapsing multiple sclerosis (MS) and capture limb progression over a short interval, using finger and foot tap data.

Methods: Patients with MS were followed prospectively during routine clinic visits approximately every 6 months. At each visit, participants performed finger and foot taps wearing the MYO-band, which includes accelerometer, gyroscope, and surface electromyogram sensors. Metrics of within-patient limb progression were created by combining the change in signal waveform features over time. The resulting upper (UE) and lower (LE) extremity metrics' discrimination of progressive versus relapsing MS were evaluated with calculation of AUROC. Comparisons with Expanded Disability Status Scale (EDSS) scores were made with Pearson correlation.

Results: Participants included 53 relapsing and 15 progressive MS (72% female, baseline mean age 48 years, median disease duration 11 years, median EDSS 2.5, median 10 months follow-up). The final summary metrics differentiated relapsing from secondary progressive MS with AUROC UE 0.93 and LE 0.96. The metrics were associated with baseline EDSS (UE P = 0.0003, LE P = 0.0007). While most had no change in EDSS during the short follow-up, several had evidence of progression by the multisensor metrics.

Interpretation: Within a short follow-up interval, this novel multisensor algorithm distinguished progressive from relapsing MS and captured changes in limb function. Inexpensive, noninvasive and easy to use, this novel outcome is readily adaptable to clinical practice and trials as a MS vital sign. This approach also holds promise to monitor limb dysfunction in other neurological diseases.

Figure 1

Haralick energy heatmap for finger tap EMG data for a patient with primary progressive MS. Figure legend: Heatmap for Haralick energy textural feature for the right finger tap EMG data, with three follow‐up visits on the y‐axis, with visits about 6 months apart over a 1‐year period. This demonstrates the Haralick energy feature changes notably with change in heat map colors indicating more irregularity in tap movements over time in this individual with primary progressive MS, who developed inability to play piano with the right hand. EMG electromyogram; MS multiple sclerosis.

Figure 2

Final scalar metric for the upper extremity (A) and lower extremity (B) by MS subtype. Figure legend: The final scalar metric differentiated multiple sclerosis subtype in the upper extremity (P < 0.001) and lower extremity (P < 0.001), with higher scores in those with progressive than relapsing MS. MS multiple sclerosis; PPMS primary progressive MS; RRMS relapsing remitting MS; SPMS secondary progressive MS.

Figure 3

ROC curves discriminating MS subtype with upper and lower extremity metrics, and the overall metric. Figure legend: Discrimination of all progressive types versus relapsing MS with the upper extremity metric (A), lower extremity metric (B), and overall combined upper and lower extremity metric (C). Discrimination of secondary progressive from relapsing MS with the upper extremity metric (D), lower extremity metric (E) and overall combined upper and lower extremity metric (F). These were calculated with the standard trapezoidal approach and the GLM‐based fusion approach. GLM generalized linear model; MS multiple sclerosis; ROC receiver operating characteristic.

Figure 4

Association of the lower extremity metric with baseline (A) and change (B) in disability. Figure legend: The lower extremity metric was associated with baseline Expanded Disability Status Scale (EDSS) (r = 0.41, P = 0.0007) and with change in EDSS (r = 0.25, P = 0.048) although several individuals had no change in EDSS, but high values of the metric suggesting progression not detected by the EDSS. Positive values for change in EDSS indicate worsening disability.