A processing method and system of clinical female pelvic floor data
By simultaneously acquiring respiratory abdominal pressure and pelvic floor electromyography (EMG) signals, using a machine learning model to invert the visceral filling intensity and perform spatial compensation, and removing deep interference in the pelvic floor EMG signals, the problem of bladder filling misjudgment in pelvic floor EMG assessment is solved, and accurate assessment of pelvic floor muscle function is achieved.
Patent Information
- Application Number
- CN202610740743.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-05-27
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies struggle to effectively isolate deep physiological interference caused by bladder fullness during pelvic floor muscle electromyography (PEMG) signal acquisition, leading to inaccurate PEMG function assessment results.
By simultaneously acquiring respiratory abdominal pressure fluctuation signals and pelvic floor electromyographic signals, the visceral filling intensity is inverted using a machine learning model to generate defensive reflex background potential increments. Through spatial asymmetric attenuation compensation and a circuit breaker mechanism, deep interference in the pelvic floor electromyographic signals is removed to obtain pure pelvic floor muscle active function signals.
It significantly improves the accuracy and stability of pelvic floor electromyography assessment, avoids misjudgment of pelvic floor muscle spasm under bladder fullness, and ensures the medical reliability of assessment reports.
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Abstract
Citation Information
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