Sniff detection and artifact discrimination from electromyography and accelerometer signals.

The method and system use EMG electrodes and accelerometers to preprocess signals, applying machine learning for accurate sniff detection, addressing the challenge of distinguishing sniffs from artifacts in EMG signals, enabling non-invasive and effective respiratory muscle effort quantification.

JP7884559B2Active Publication Date: 2026-07-03KONINKLIJKE PHILIPS NV
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Patent Information

Application Number
JP2023580768
Authority / Receiving Office
JP · JP
Patent Type
Patents
Current Assignee / Owner
Priority Date
2021-06-29
Filing Date
2022-06-27
Publication Date
2026-07-03
Estimated Expiration
2042-06-27

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Abstract

A non-invasive system and method for quantifying respiratory muscle effort (RME) (or respiratory effort, work of breathing) is provided. The system and method utilize simultaneously measured EMG and accelerometer signals. The measured EMG signals are pre-processed to generate both a signal that emphasizes normal respiratory activity and a signal that emphasizes sniff activity (deep, sharp inspiration). Candidate sniff time intervals are determined from the pre-processed EMG signals. The measured accelerometer signals are pre-processed to generate a number of signals that emphasize activity in upper or lower frequency bands. Features of the pre-processed EMG and accelerometer signals corresponding to the candidate sniff time intervals are analyzed to determine whether the candidate sniff constitutes an actual sniff or an artifact. The maximum sniff effort is then identified, and the RME is then quantified by the ratio of the average of the maximum normal respiratory activity to the value of the maximum sniff effort.
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Citation Information

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