Classification of segments of acoustic physiological signal captured during sleep using phase-locked loop array
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.
1. A sleep monitoring method, comprising the steps of:
detecting by a sleep monitoring device, using a sound transducer, respiration sound;
generating by the device, using the sound transducer, an acoustic physiological signal representing the respiration sound;
selecting by the device a master phase-locked loop to process the physiological signal from a plurality of phase-locked loops based at least in part on rhythm indicators computed by the plurality of phase-locked loops in response to the physiological signal;
classifying by the device as a snore segment a first segment of the physiological signal while a master rhythm indicator computed by the master phase-locked loop exceeds a snore rhythm threshold;
generating by the device sleep profile data based at least in part on the snore segment; and
outputting by the device information based at least in part on the sleep profile data.
2. The monitoring method of claim 1 , further comprising the step of classifying by the device as an apnea segment a second segment of the physiological signal in response to determining that the physiological signal exceeds a gasp threshold more than a predetermined time after the master rhythm indicator falls below the snore rhythm threshold,
wherein the sleep profile data are generated based further on the apnea segment.
3. The monitoring method of claim 2 , further comprising the step of determining that the physiological signal exceeds a snore loudness threshold.
4. The monitoring method of claim 3 , wherein the gasp threshold is higher than the snore loudness threshold.
5. The monitoring method of claim 1 , wherein the master phase-locked loop is selected based on a determination that the master rhythm indicator has a highest confidence value among the rhythm indicators.
6. The monitoring method of claim 1 , wherein the plurality of phase-locked loops have different free run frequencies.
7. The monitoring method of claim 1 , further comprising the step of applying to the physiological signal a plurality of bandpass filters having different passbands optimized for the plurality of phase-locked loops.
8. The monitoring method of claim 1 , further comprising the step of computing an energy envelope of the physiological signal.
9. The monitoring method of claim 1 , further comprising the step of normalizing the physiological signal.
10. A sleep monitoring method, comprising the steps of:
detecting by a sleep monitoring device respiration sound;
generating by the device an acoustic physiological signal representing the respiration sound;
selecting by the device a master phase-locked loop to process the physiological signal from a plurality of phase-locked loops using rhythm indicators computed by the plurality of phase-locked loops in response to the physiological signal;
classifying by the device as a snore segment a first segment of the physiological signal while a master rhythm indicator computed by the master phase-locked loop exceeds a snore rhythm threshold;
classifying by the device as an apnea segment a second segment of the physiological signal after the snore segment in response to determining that the physiological signal exceeds a gasp threshold more than a predetermined time after the master rhythm indicator falls below the snore rhythm threshold;
generating by the device sleep profile data using the snore segment and the apnea segment; and
outputting by the device information using the sleep profile data.