A medical device includes an acoustic transducer, which is configured to sense infrasonic waves emitted from a body of a living subject with a periodicity determined by a periodic physiological activity and to output an electrical signal in response to the sensed waves. At least one speaker is configured to output audible sounds in response to an electrical input. Processing circuitry is configured to process the electrical signal so as to generate a frequency-stretched signal in which infrasonic frequency components of the electrical input are shifted to audible frequencies while preserving the periodicity of the periodic physiological activity in the frequency-stretched signal, and to input the frequency-stretched signal to the at least one speaker.

The present disclosure provides for monitoring brain health and predicting and detecting seizures via a wearable head ap paratus. An exemplary system includes a wearable head apparatus with a plurality of sensors. The system includes a memory device with instructions for performing a method. The method provides for first receiving electroencephalography (EEG) data and/or other data types output by the plurality of sensors. The EEG data includes electrical signals representing brain activity of a user. The method provides for processing the EEG data and/or other data types using a machine learning model to identify a time window of a subset of the EEG data and/or other data types, which represents a seizure. The method provides for tagging the time window as seizure data. A representation of the time window of the EEG data and/or other data types is then output.

The present disclosure provides for monitoring brain health and predicting and detecting seizures via a wearable head ap paratus. An exemplary system includes a wearable head apparatus with a plurality of sensors. The system includes a memory device with instructions for performing a method. The method provides for first receiving electroencephalography (EEG) data and/or other data types output by the plurality of sensors. The EEG data includes electrical signals representing brain activity of a user. The method provides for processing the EEG data and/or other data types using a machine learning model to identify a time window of a subset of the EEG data and/or other data types, which represents a seizure. The method provides for tagging the time window as seizure data. A representation of the time window of the EEG data and/or other data types is then output.

A system includes an earpiece and a telecommunications device in communication with the earpiece. The earpiece includes a power source, a processor configured to process at least one algorithm stored within the earpiece, and an optical sensor. The optical sensor includes at least one optical emitter configured to direct optical energy to a region of an ear of a subject wearing the earpiece and at least one optical detector configured to sense absorbed, scattered, and/or reflected optical energy emanating from the ear region. The telecommunications device is configured to modify the at least one algorithm, to download additional algorithms to the earpiece, and to activate and deactivate the optical sensor.

A system includes an earpiece and a telecommunications device in communication with the earpiece. The earpiece includes a power source, a processor configured to process at least one algorithm stored within the earpiece, and an optical sensor. The optical sensor includes at least one optical emitter configured to direct optical energy to a region of an ear of a subject wearing the earpiece and at least one optical detector configured to sense absorbed, scattered, and/or reflected optical energy emanating from the ear region. The telecommunications device is configured to modify the at least one algorithm, to download additional algorithms to the earpiece, and to activate and deactivate the optical sensor.

Wearable apparatus for monitoring various physiological and environmental factors are provided. Real-time, noninvasive health and environmental monitors include a plurality of compact sensors integrated within small, low-profile devices, such as earpiece modules. Physiological and environmental data is collected and wirelessly transmitted into a wireless network, where the data is stored and/or processed.

Wearable apparatus for monitoring various physiological and environmental factors are provided. Real-time, noninvasive health and environmental monitors include a plurality of compact sensors integrated within small, low-profile devices, such as earpiece modules. Physiological and environmental data is collected and wirelessly transmitted into a wireless network, where the data is stored and/or processed.

A bioacoustic sound testing device includes a band power calculator that calculates a band power in a predetermined period for each of a plurality of predetermined frequency bands on the basis of a bioacoustic sound, a reference power calculator that calculates a respiratory airflow reflecting power on the basis of a band power of a band reflecting a respiratory airflow, calculates a specific change reflecting power on the basis of a band power of a band reflecting a specific change in a living body, and calculates a reference power on the basis of the respiratory airflow reflecting power and the specific change reflecting power, and an index value calculator that calculates at least one index value on the basis of the reference power and the band powers of the plurality of predetermined frequency bands.

A monitor recorder optimized for electrocardiography and respiratory data acquisition and processing is provided. The recorder includes a sealed housing and an electronic circuitry comprised within the sealed housing, which includes an electrocardiographic front end circuit electrically interfaced to an externally-powered micro-controller and operable to sense electrocardiographic signals through electrodes provided on the patch; the micro-controller interfaced to one or more respiratory sensors, the micro-controller operable to sample the electrocardiographic signals, to sample respiratory events detected by the one or more respiratory sensors upon receiving one or more signals from the one or more respiratory sensors, to buffer each of the respiratory event samples, to compress each of the buffered respiratory event samples, to buffer each of the compressed respiratory event samples, and to write the buffered compressed respiratory event samples and the samples of the electrocardiography signals into an externally-powered flash memory; and the memory interfaced with the micro-controller.

A method and apparatus for the treatment of Sleep Apnea events and Hypopnea episodes wherein one embodiment comprises a wearable, belt like apparatus containing a microphone and a plethysmograph. The microphone and plethysmograph generate signals that are representative of physiological aspects of respiration, and the signals are transferred to an imbedded computer. The embedded computer extracts the sound of breathing and the sound of the heart beat by Digital Signal Processing techniques. The embedded computer has elements for determining when respiration parameters falls out of defined boundaries for said respiration parameters. This exemplary method provides real-time detection of the onset of a Sleep Apnea event or Hypopnea episode and supplies stimulation signals upon the determination of a Sleep Apnea event or Hypopnea episode to initiate an inhalation. In one embodiment, the stimulus is applied to the patient by a cutaneous rumble effects actuator and/or audio effects broadcasting.