A method of distinguishing sleep period states that a person experiences during a sleep period, the method comprising: using a non-contact microphone to acquire a sleep sound signal representing sounds made by a person during sleep; segmenting the sleep sound signals into epochs; generating a sleep sound feature vector for each epoch; providing a first model that gives a probability that a given sleep period state experienced by the person in a given epoch exhibits a given sleep sound feature vector; providing a second model that gives a probability that a first sleep period state associated with a first epoch transitions to a second sleep period state associated with a subsequent second epoch; and processing the feature vectors using the first and second models to determine a sleep period state of the person from a plurality of possible sleep period states for each of the epochs.

An apparatus, comprising a processor and memory storing instructions that, when executed by the processor, cause the processor to: receive ventilator data obtained from continual mechanical ventilatory support of a patient over a period of time, analyze the ventilator data to identify a plurality of breathing cycles, classify the plurality of breathing cycles into normal breathing cycles and abnormal breathing cycles, using a machine learning algorithm, detect a change in the normal breathing cycles, compared to normal breathing cycles identified by the apparatus based on ventilator data obtained from continual mechanical ventilatory support of the patient over a previous period of time, and generate output indicative of the change in the normal breathing cycles.

The present invention is directed to a sensor device detecting movement of mouth or tongue associated with bruxism and/or sleep apnea. The present invention is also directed to a system for treating sleep apnea in a patient in need thereof, comprising a sensing component and a stimulation component, the sensing component comprising one or more wireless sensors for collecting one or more vital signs of the patient and/or tongue muscle movements of the patient, the sensing component being in wireless communication with a control system, and wherein the stimulation component comprises (i) an implantable body configured to deliver energy to one of a nerve or muscle, and (ii) a wearable appliance inductively coupled to the implanted body, the wearable portion configured to receive signals from the control system, wherein the sensing component comprises the sensor device of the present invention.

A method and system to manage a respiratory condition of a patient. An oxygen concentrator is configured to generate and deliver oxygen enriched air to the patient according to a selected dosage. The oxygen concentrator senses and collects physiological data of the patient and collects operational data during the generation and delivery of oxygen enriched air. The oxygen concentrator adjusts the dosage of oxygen enriched air based on the sensed physiological data. The oxygen concentrator transmits operational data and the physiological data to a health data analysis engine. The health data analysis engine collects the data transmitted by the oxygen concentrator. The health analysis engine detects a triggering event based on the collected data and determines an action to resolve the detected triggering event.

Apnea-hypopnea detection includes obtaining accelerometer data from an accelerometer configured to measure micro-movements that are due to respiration. Displacement values are obtained from the accelerometer data. Features are obtained using the accelerometer data. An apnea-hypopnea index (AHI) is obtained from a machine learning model that uses the features as inputs. The displacement values correspond to peaks in the accelerometer data.

An oxygen concentrator 100 apparatus and a method thereof implement operations control to efficiently release oxygen enriched gas to reduce potential waste. The control methodology may include generating a profile such as a minimum inhalation flow profile of the user. The profile may be based on a size parameter of the user. The method may determine one or more control parameters characterizing a bolus of oxygen enriched gas based on the generated flow profile. The control methodology may then generate a bolus release control signal, such as for a supply valve, according to the determined one or more control parameters. The oxygen concentrator may then, with the control signal, release and deliver a bolus of oxygen enriched gas for a user such as for reducing waste.

The present invention may provide a device and a method for detecting and diagnosing sleep apnea, which can accurately determine an apnea state by: extracting a respiration signal of an examinee from a received signal which is a reflection of an impulse signal emitted from at least one IR-UWB radar; setting a threshold value for determining an apnea state from changes in the deviation and interval between peaks of the extracted respiration signal; and comparing the set threshold value with the deviation between the peaks. Therefore, the sleep apnea can be accurately detected and diagnosed using a non-contact/noninvasive method, and thus inconvenience felt by the examinee can be reduced.

A system is provided to treat sleep apnea, with a method of use. The system includes a handpiece which includes at least one penetrating electrode configured to penetrate tissue and one or more temperature sensors. A processor is coupled with the one or more temperature sensors and the at least one penetrating electrode, and a memory is configured to store instructions executable by the processor. The instructions, when executed, are operable to emit, by the at least one penetrating electrode, RF energy from the electrodes to heat a region of a base of a tongue until a target energy is delivered to the region. The instructions are also operable to measure, by the one or more temperature sensors, the temperature of the region.

A method and apparatus deliver a variable flow of oxygen to a patient. The apparatus may include a flow control valve, a pressure sensor to detect a patient's breathing pressure and ambient pressure, an oxygen flow analyzer to measure oxygen flow to the patient, and a processor to analyze the breathing pressure values, ambient pressure value, and oxygen flow rate values and to determine when a patient is inhaling. When the processor determines the patient is inhaling, the processor calculates an optimal oxygen flow rate to deliver to a patient, which may depend on a pre-selected flow rate and an oxygen backlog, and the processor sends a signal to the flow control valve to deliver the optimal oxygen flow rate to the patient.

Apparatus, systems, and methods for real time monitoring of a patient's breathing utilizing automatically controlled devices and machine learning based techniques to determine a full breath of a patient and to identify splinting points, and to thereby transmit stimulation signals to the patient so as to assist the patient breathe through splinting points. In some embodiments, a wearable breathing monitoring device comprising of one or more sensors configured to monitor the user's breathing and a stimulator apparatus comprising one or more transmitters configured to transmit stimulation signals to the patient at a time corresponding to a detected splinting point is provided. The stimulator apparatus is configured to apply electrical pulses according to a stimulation schedule via the transmitters to target nerves of the user's body.