Apparatus and method for diagnosing sleep quality

A contactless method for determining levels of one or more sleep disorder factors, the method comprising:

• using a non-contact microphone, acquiring a real-time sleep sound signal representing sounds made by a person during sleep, and conveying the signal to a processor;
• segmenting by the processor the sleep sound signals into a plurality of epochs;
• generating by the processor at least one sleep sound feature vector for each of the epochs in the plurality of epochs, wherein for each of the epochs, the generation of the at least one sleep sound feature vector for the respective epoch includes determining an autocorrelation function Rτ as a function of time displacement τ of sleep sounds that occurred during the respective epoch, and specifying that the at least one sleep sound feature vector for the respective epoch is equal to a value at a time displacemen τ=τ1 for which the autocorrelation function reaches a first maximum after a maximum of the autocorrelation function at τ=0;
• for each of the epochs in the plurality of epochs, determining by the processor a set of first probabilities using a first model and based on the at least one sleep sound feature vector generated for the respective epoch, said set of first probabilities indicating probabilities as to whether the respective epoch is associated with respective sleep period states from a plurality of predefined sleep period states;
• associating each epoch in the plurality of epochs with one of the sleep period states from the plurality of predefined sleep period states based on the set of first probabilities;
• determining by the processor a second probability for each set of two adjacent epochs in the plurality of epochs, the second probability indicating a probability of transitioning from the sleep period state associated with the preceding epoch in the respective set to the sleep period state associated with the succeeding epoch in the respective set;
• wherein the processor is configured to determine the sleep period state of each of the epochs in the plurality of epochs based on the set of first probabilities and the second probability, wherein the sleep period state of each of the epochs in the plurality of epochs is determined during the respective epoch;
• and
• utilizing said determined sleep period states, determining levels of sleep disorder factors selected from:
• total sleep time (TST)—a sum of the durations of sleep states in a sleep period;
• sleep latency (SL)—an elapsed time to falling asleep from a time of lying down to go to sleep;
• sleep efficiency (SE)—a ratio between TST and total time spent lying down to sleep during the sleep period;
• wake-time after sleep onset (WASO)—a sum of the durations of awake states during the sleep period;
• and
• an awakening index (AI)—equal to an average number of times per hour a person awakes from sleep during the sleep period;
• wherein the first model comprises a Gaussian mixture model (GMM), and the second probability for each set of two adjacent epochs in the plurality of epochs is determined using a second model, and the second model comprises a hidden Markov model (HMM).