Needle Electromyography Signals for Three-Class Neuromuscular Diagnosis

376 needle electromyography (nEMG) signals were collected from 57 patients at Seoul National University Hospital. The dataset supports a three-class diagnostic classification task. Specific details on the collection date, authors, and file formats are not provided in the input.

Use Cases

• Classify the three diagnostic labels using raw nEMG signal features.
• Analyze signal patterns across different muscle types documented for each patient.
• Train a time-series classifier to distinguish between healthy and pathological nEMG signals.
• Develop feature extraction methods for the 376 nEMG recordings to aid in automated diagnosis.

Strengths

• Contains 376 individual nEMG signal recordings.
• Data originates from 57 distinct patients, providing multiple samples per subject.
• Includes diagnostic labels and muscle type information for supervised learning.

Limitations

• Sample size of 57 patients is relatively small for robust model generalization.
• Lack of column details prevents assessment of signal features or metadata completeness.
• Unknown collection time range and update recency limit temporal analysis.

Provenance

Source

Seoul National University Hospital (SNUH).

Collection Method

Clinical needle electromyography recordings from patients.

Geography

South Korea (inferred from SNUH).