Surface Electromyography Features for ALS Bulbar Assessment

Panying Rong's study dataset includes 104 oral diadochokinetic recordings from 16 individuals with ALS and 10 healthy controls. The data was collected to translate surface electromyography into an objective clinical assessment tool. The dataset was last updated on April 10, 2026.

Use Cases

• Detecting subclinical bulbar involvement in ALS based on extracted sEMG features.
• Distinguishing ALS patients with and without bulbar symptoms from healthy controls using machine learning classification.
• Evaluating associations between neuromuscular signals and standardized functional outcomes via mediation analysis.
• Testing the robustness of diagnostic features against nonmotor confounders like age, sex, and cognitive-linguistic impairments.

Strengths

• Dataset includes recordings from 26 participants (16 ALS, 10 healthy controls).
• Framework extracts 60 features from six craniofacial muscle groups.
• Composite measures demonstrated high internal consistency (Cronbach's α=0.89±0.071).
• Machine learning classifications achieved discriminatory accuracy between 0.82 and 0.85.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Data is stored in a DOCX file format, which may require extraction of tabular data.

Provenance

Source

figshare

Collection Method

A clinically grounded sEMG framework with a standardized protocol and analytic pipeline.

Freshness

Last updated 2026-04-10 06:00:37