Devices and methods for allowing for improved assisted ventilation of a patient. The methods and devices provide a number of benefits over conventional approaches for assisted ventilation. For example, the methods and devices described herein permit blind insertion of a device that can allow ventilation regardless of whether the device is positioned within a trachea or an esophagus.

Various embodiments are described herein for a controller for controlling the operation of a breathing assistance device that provides breathing assistance to a user. The controller comprises a processor that generates a respiratory index value that is determined during a current monitoring time period to detect a respiratory failure, or predict the respiratory failure when at least one PSG signal is measured. The respiratory index value is compared to a threshold to determine if the control signal needs to be updated to reduce or eliminate respiratory failure that the user is currently experiencing or to prevent a predicted respiratory failure from occurring.

Methods of and devices for detecting a measurable characteristic of the gas sample. The methods and devices are able to detect a value of or a change of measurable characteristic (e.g., such as chemical concentrations), a change of chemical compositions and/or biological responses of a living organism that are induced by a stressor. The biological responses are able to include cellular stress, damage, and immune responses. The stressor is able to include an exposure to ionizing radiation. The effects of the stressors are able to be monitored in terms of changes in the chemical concentrations and chemical compositions in an exhaled breath. The chemicals are able to function as bio-markers. The chemicals that are to be monitored are able to include nitric oxide, carbon monoxide, carbon dioxide, ethane, and other molecules related to specific disease resulting from the stressor.

Methods of and devices for detecting a measurable characteristic of the gas sample. The methods and devices are able to detect a value of or a change of measurable characteristic (e.g., such as chemical concentrations), a change of chemical compositions and/or biological responses of a living organism that are induced by a stressor. The biological responses are able to include cellular stress, damage, and immune responses. The stressor is able to include an exposure to ionizing radiation. The effects of the stressors are able to be monitored in terms of changes in the chemical concentrations and chemical compositions in an exhaled breath. The chemicals are able to function as bio-markers. The chemicals that are to be monitored are able to include nitric oxide, carbon monoxide, carbon dioxide, ethane, and other molecules related to specific disease resulting from the stressor.

The invention is a breath sensing device comprising a sensing pad, a plurality of coverage sensing units, and a signal processor, the coverage sensing units are disposed on the sensing pad, when a user lies down on the sensing pad, a plurality of coverage signals sensed by the coverage sensing units respectively produce a periodic change according to the deformation of a curve along the body of the user while breathing, the signal processor is electrically connected to the coverage sensing units to receive the coverage signals, and the signal processor calculates to obtain duration and proportion of the user's inspiration, expiration, and pause phase and depth information of breathing from a waveform of the coverage signals based on a signal-processing algorithm.

The invention is a breath sensing device comprising a sensing pad, a plurality of coverage sensing units, and a signal processor, the coverage sensing units are disposed on the sensing pad, when a user lies down on the sensing pad, a plurality of coverage signals sensed by the coverage sensing units respectively produce a periodic change according to the deformation of a curve along the body of the user while breathing, the signal processor is electrically connected to the coverage sensing units to receive the coverage signals, and the signal processor calculates to obtain duration and proportion of the user's inspiration, expiration, and pause phase and depth information of breathing from a waveform of the coverage signals based on a signal-processing algorithm.

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.

An integrated system and associated methodology allow performing at-home spirometry tests using smart devices which leverage the built-in millimeter-wave (mmWave) technology. Implementations leverage deep learning with some embodiments including a combination of mmWave signal processing and CNN-LSTM (Convolutional Neural Network-Long Short-Term Memory Network) architecture. Smartphone devices are transformed into reliable at-home spirometers by having a user hold a device in front of their mouth, inhale their full lung volume, and forcibly exhale until the entire volume is expelled, as in typical spirometry tests. Airflow on the device surface creates tiny vibrations which directly affect the phase of the reflected mmWave signal from nearby objects. Stronger airflow yields larger vibration and higher phase change. The technology analyzes tiny vibrations created by airflow on the device surface and combines wireless signal processing with deep learning. The resulting low-cost, contactless method of lung function monitoring is not affected by noise and motion and provides all key spirometry indicators.

An integrated system and associated methodology allow performing at-home spirometry tests using smart devices which leverage the built-in millimeter-wave (mmWave) technology. Implementations leverage deep learning with some embodiments including a combination of mmWave signal processing and CNN-LSTM (Convolutional Neural Network-Long Short-Term Memory Network) architecture. Smartphone devices are transformed into reliable at-home spirometers by having a user hold a device in front of their mouth, inhale their full lung volume, and forcibly exhale until the entire volume is expelled, as in typical spirometry tests. Airflow on the device surface creates tiny vibrations which directly affect the phase of the reflected mmWave signal from nearby objects. Stronger airflow yields larger vibration and higher phase change. The technology analyzes tiny vibrations created by airflow on the device surface and combines wireless signal processing with deep learning. The resulting low-cost, contactless method of lung function monitoring is not affected by noise and motion and provides all key spirometry indicators.

A wearable device includes a housing with a window and an electronic module supported by the housing. The electronic module includes a photoplethysmography sensor, a motion sensor, and a signal processor that processes signals from the motion sensor and signals from the photoplethysmography sensor. The signal processor is configured to remove frequency bands from the photoplethysmography sensor signals that are outside of a range of interest using a band-pass filter to produce pre-conditioned signals, and to further process the pre-conditioned signals using the motion sensor signals to reduce motion artifacts from footsteps during subject running. The device includes non-air light transmissive material in optical communication with the photoplethysmography sensor and the window that serves as a light guide for the photoplethysmography sensor. The window optically exposes the photoplethysmography sensor to a body of a subject wearing the device via the non-air light transmissive material.