Sharp Laboratories of America, Inc

Te-Chung Isaac Yang

Yongji Fu

Yungkai Kyle Lai

The present invention provides adaptive lightweight acoustic signal classification for physiological monitoring applications. In an exemplary implementation, the total energy of a segment of an acoustic signal recording body sounds is first determined. For each of a plurality of signal classes (e.g., good, noisy, weak), the probability that the segment belongs to the signal class is then calculated using the total energy and profile data for the signal class. The segment is then assigned to one of the plurality of signal classes by reference to the probabilities. Physiological data are then selectively generated and outputted using the segment, depending on the assigned signal class, and the segment is selectively applied as feedback to update profile data for the assigned signal class.

Adaptive lightweight acoustic signal classification for physiological monitoring

A sleep monitoring method and device classify segments of an acoustic physiological signal captured during sleep as snore and apnea segments. The method and device employ a phase-locked loop array to rapidly detect snore segments for widely variant snoring rhythms exhibited by different people or the same person over time. The phase-locked loop array integrates seamlessly with an apnea timer-thresholding mechanism that detects apnea segments.

Classification of segments of acoustic physiological signal captured during sleep using phase-locked loop array

Physiological signal quality classification methods and systems designed to improve ambulatory monitoring. Physiological signals are classified as good, noisy or weak based on signal properties. Once classified, signals are processed differently depending on their classification in order to encourage reliance on reliable physiological data, discourage reliance on unreliable physiological data and induce action to improve signal quality. For example, for a good signal, physiological data may be extracted from the signal and displayed to a person being monitored. For a noisy signal, a noisy signal notification may be displayed to the person in lieu of extracted physiological data. For a weak signal, a weak signal notification may be displayed to the person in lieu of extracted physiological data. Moreover, a noisy or weak signal notification displayed to a person being monitored may be accompanied by a corrective action recommendation, such as 'move to quieter environment' for a noisy signal or 'check body placement of sensor' for a weak signal.

Physiological signal quality classification for ambulatory monitoring

A method and device for physiological parameter estimation, such as heart rate estimation, use a phase-locked loop to dynamically track a dominant frequency of an acoustic signal within a frequency band for the physiological parameter and estimate the physiological parameter using the dominant frequency. Because the phase-locked loop starts searching for the current dominant frequency near the most recently identified dominant frequency, the method and device rapidly incorporate slow changes in the dominant frequency that likely reflect real changes in the monitored physiological parameter while being slow to incorporate rapid changes in the dominant frequency that are likely caused by large noise. The hysteresis in phase-locked loop tracking allows the method and device to avoid physiological parameter estimation error induced by short-term large noise and quickly reacquire the dominant frequency once short-term large noise abates.

Physiological parameter estimation using phase-locked loop

Physiological signal quality classification methods and systems for ambulatory monitoring. Physiological signals are classified as good, noisy or weak based on signal properties. Once classified, signals are processed differently depending on their classification For example, for a good signal, physiological data may be extracted from the signal and displayed to a person being monitored. For a noisy signal, a noisy signal notification may be displayed to the person in lieu of extracted physiological data. For a weak signal, a weak signal notification may be displayed to the person in lieu of extracted physiological data. Moreover, a noisy or weak signal notification displayed to a person being monitored may be accompanied by a corrective action recommendation, such as 'move to quieter environment' for a noisy signal or 'check body placement of sensor' for a weak signal.

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Aliso Viejo, CA, United States of America

Te-Chung Isaac Yang