Provided herein are devices (e.g., electrochemical sensors useful for detecting volatile organic compounds associated with certain diseases or conditions and/or diagnosing certain diseases or conditions). The devices comprise one or more layers of metal on a layer of silicon, and a layer of molecularly imprinted polymer in electrical communication with the one or more layers of metal, wherein the one or more layers of metal are each independently selected from a layer of chromium, platinum, gold, nickel, cobalt, tungsten, rhodium, iridium, silver, tin, titanium or tantalum, or an alloy thereof. Methods of using the devices (e.g., to detect one or more analytes in a sample, to detect and/or diagnose a disease or condition in a subject), and methods of making the devices are also provided.

A capnography system includes a CO.sub.2 sensing system having a CO.sub.2 sensor configured to measure a CO.sub.2 concentration in exhaled breath of a subject, a processor configured to derive one or more breath related parameters based on the measured CO.sub.2 concentration, and a communication unit. The capnography system includes a tubing system configured to allow flow of respiratory gasses therethrough. The tubing system includes a connector configured to connect the tubing system to the CO.sub.2 sensing system and a communication component configured to provide an indication of a type of the tubing system to the communication unit. The communication unit is configured to transfer data to the processor based on the indication obtained from the communication component, and the processor is configured to change or suggest a change of an operation mode of the CO.sub.2 sensing system based on the data.

A method of modeling lungs of a patient includes acquiring computed tomography data of a patient's lungs, storing a software application within a memory associated with a computer, the computer having a processor configured to execute the software application, executing the software application to differentiate tissue located within the patient's lung using the acquired CT data, generate a 3-D model of the patient's lungs based on the acquired CT data and the differentiated tissue, apply a material property to each tissue of the differentiated tissue within the generated 3-D model, generate a mesh of the 3-D model of the patient's lungs, calculate a displacement of the patient's lungs in a collapsed state based on the material property applied to the differentiated tissue and the generated mesh of the generated 3-D model, and display a collapsed lung model of the patient's lungs based on the calculated displacement of the patient's lungs.

A method of modeling lungs of a patient includes acquiring computed tomography data of a patient's lungs, storing a software application within a memory associated with a computer, the computer having a processor configured to execute the software application, executing the software application to differentiate tissue located within the patient's lung using the acquired CT data, generate a 3-D model of the patient's lungs based on the acquired CT data and the differentiated tissue, apply a material property to each tissue of the differentiated tissue within the generated 3-D model, generate a mesh of the 3-D model of the patient's lungs, calculate a displacement of the patient's lungs in a collapsed state based on the material property applied to the differentiated tissue and the generated mesh of the generated 3-D model, and display a collapsed lung model of the patient's lungs based on the calculated displacement of the patient's lungs.

A system is provided for detecting lung abnormalities in a subject. The system comprises a first sensor, for sensing a first acoustic signal generated by, or applied to, the subject and an arrangement of one or more second sensors for detecting a plurality of second acoustic signals from the lungs of the subject, wherein the second acoustic signals comprise attenuated versions of the first acoustic signal after passing through the lungs of the subject. The system further comprises a processor, wherein the processor is configured to determine a respective signal attenuation between the first acoustic signal and at least a subset of the plurality of second acoustic signals and process the signal attenuations thereby to detect a lung abnormality.

In one aspect, a computer-implemented method includes receiving a signal corresponding to impedance across a patient's chest cavity; filtering the signal using one or more filters that reduce noise and center the signal around a zero baseline; adjusting an amplitude of the filtered signal based on a threshold value; separating the amplitude-adjusted signal into component signals, where each of the component signals represents a frequency-limited band; detecting a fractional phase transition of a component signal of the component signals; selecting a dominant component signal from the component signals based on amplitudes of the component signals at a time corresponding to the detected fractional phase transition; determining a frequency of the dominant component signal at the time corresponding to the detected fractional phase transition; and determining a respiratory rate of the patient based on the determined frequency.

Accordingly, systems and methods have been developed to monitor, diagnose, and predict the course of COPD and determine what stage of COPD a patient is currently experiencing based, for example, analysis of respiratory sounds and images of sputum. This includes systems and methods that may be performed by the patient in the home in some cases, and some that utilized sensors available on mobile devices including microphones and cameras. The system may also be able to predict COPD exacerbation events and warn caregivers in advance.

Accordingly, systems and methods have been developed to monitor, diagnose, and predict the course of COPD and determine what stage of COPD a patient is currently experiencing based, for example, analysis of respiratory sounds and images of sputum. This includes systems and methods that may be performed by the patient in the home in some cases, and some that utilized sensors available on mobile devices including microphones and cameras. The system may also be able to predict COPD exacerbation events and warn caregivers in advance.

A method for predicting the presence of a malady of the respiratory system in a subject comprising: operating at least one electronic processor to transform one or more sounds of the subject that are associated with the malady into corresponding one or more image representations of said sounds; applying said one or more representations to at least one pattern classifier trained to predict the presence of the malady; and operating said processor to predict the presence of the malady in the subject based on at least one output of the at least one pattern classifier.

The invention provides a system and method for determining a respiratory effort for a subject. The method comprises obtaining a relaxed signal representing a subject breathing in a relaxed manner and a forced signal representing a subject breathing in a forced manner. The relaxed signal is then smoothed over a first averaging window and the forced signal is smoothed over a second averaging window, wherein the first averaging window is longer than the second averaging window. Based on the smoothed relaxed signal and the smoothed forced signal, a respiratory effort can thus be determined.