IMC Phase Variability Biomarker for ALS Diagnosis

The patent replaces complex clinical assessment procedures with automated computational analysis of EMG signals. By using algorithms to calculate IMC phase and variability from raw EMG data, the system substitutes manual neurological examinations with objective, quantifiable metrics that can be processed computationally, thereby improving measurement precision while managing device complexity through software-based solutions.

The patent transforms raw EMG signals into meaningful clinical metrics by changing parameters through mathematical processing. Specifically, it converts time-domain EMG signals into frequency-domain representations and calculates coherence metrics across different frequency bands, transforming unprocessed electrical signals into quantifiable biomarkers that precisely reflect upper motor neuron function.

The system performs self-service by automatically calculating IMC metrics and generating diagnostic interpretations from raw EMG data without requiring manual intervention. The automated processing pipeline computes coherence values, determines phase variability, and provides objective disease progression assessments, reducing the operational burden on clinicians while maintaining high reliability through consistent, reproducible measurements.

The patent implements feedback mechanisms by continuously monitoring IMC metrics over time and comparing them against established norms or previous measurements. This feedback loop enables automatic detection of disease progression patterns, providing clinicians with actionable insights that simplify monitoring while enhancing reliability through objective, data-driven assessments rather than subjective clinical judgment.