Methods and apparatus provide monitoring of coughing and/or a sleep disordered breathing state of a person. One or more sensors may be configured for non-contact active and/or passive sensing. The processor(s) may extract respiratory effort signal(s) from one or more motion signals generated by active non-contact sensing with the sensor(s). The processor(s) may extract one or more energy band signals from an acoustic audio signal generated by passive non-contact sensing with the sensor(s). The processor(s) may assess the energy band signal(s) and/or the respiratory efforts signal(s) to generate intensity signal(s) representing sleep disorder breathing modulation. The processor(s) may classify feature(s) derived from the one or more intensity signals to generate measure(s) of coughing and/or sleep disordered breathing. The processor may evaluate sensing signal(s) to generate indication(s) of cough event(s) and/or cough type which may include generating an indication of a coronavirus disease or a coronavirus disease cough type.

A method of detecting coughs using photoplethysmography sensor data is presented. In some embodiments, accelerometry sensor data may also be used in conjunction. A system is presented for the purpose of automatic cough detection, which may also incorporate auxiliary data relevant to the occurrence of coughs. Auxiliary data, may be used to improve cough detection and/or be for contextualization and categorization of detected coughs.

During a focused-ultrasound or other non-invasive treatment procedure, the motion of the treatment target or other object(s) of interest can be tracked in real time based on (i) the comparison of treatment images against a reference library of images that have been acquired prior to treatment for the anticipated range of motion and have been processed to identify the location of the target or other object(s) therein and (ii) complementary information associated with the stage of the target motion during treatment.

In a method and an image capturing system (5) for capturing signals and image data of a volume segment of an examination object, raw data of the volume segment are captured, and image time stamps are captured at which certain of the raw data are captured. Physiological signals of the examination object are captured at the same time as capturing the raw data. Signal time stamps are captured at which certain of the physiological signals are captured. The capture of the raw data and the capture of the physiological signals is controlled by the same processor of the image capturing system, so that both the image time stamps and the signal time stamps are predetermined by the same processor.

In a method and an image capturing system (5) for capturing signals and image data of a volume segment of an examination object, raw data of the volume segment are captured, and image time stamps are captured at which certain of the raw data are captured. Physiological signals of the examination object are captured at the same time as capturing the raw data. Signal time stamps are captured at which certain of the physiological signals are captured. The capture of the raw data and the capture of the physiological signals is controlled by the same processor of the image capturing system, so that both the image time stamps and the signal time stamps are predetermined by the same processor.

A sleep monitoring system includes an ECG device (2) and a respiration inductance plethysmogram (3) which monitor cardiac activity and physical (ribcage) respiration respectively and feed representative signals to a digital data processor. Operations (5-9) process the beat interval data, while in a second thread, operations (20-24) independently process the amplitude modulation of the ECG data caused by the respiratory motion of the subject. The inductance plethysmogram device (3) provides an input to the processor which represents respiration as directly monitored independently of the ECG. Operations (30-34) process this direct respiration data independently and in parallel, in a third thread. All extracted features are fed to a classifier which in step (10) combines selected combinations of features to make decisions in real time.

A sleep monitoring system includes an ECG device (2) and a respiration inductance plethysmogram (3) which monitor cardiac activity and physical (ribcage) respiration respectively and feed representative signals to a digital data processor. Operations (5-9) process the beat interval data, while in a second thread, operations (20-24) independently process the amplitude modulation of the ECG data caused by the respiratory motion of the subject. The inductance plethysmogram device (3) provides an input to the processor which represents respiration as directly monitored independently of the ECG. Operations (30-34) process this direct respiration data independently and in parallel, in a third thread. All extracted features are fed to a classifier which in step (10) combines selected combinations of features to make decisions in real time.

Autoregressive modelling is used to identify periodic physiological signals such as heart rate or breathing rate in an image of a subject. The colour channels of a video signal are windowed and normalised by dividing each signal by its mean. The ratios of the normalised channels to each other are found and principal component analyses conducted on the ratio signals. The most periodic of the principal components is selected and autoregressive models of one or more different orders are fitted to the selected component. Poles of the fitted autoregressive models of different orders are taken and pure sinusoids corresponding to the frequency of each pole are generated and their cross-correlation with the original component is found. Whichever pole corresponds to the sinusoid with the maximum cross-correlation is selected as the best estimate of the frequency of periodic physiological information in the original video signal. The method may be used in a patient monitor or in a webcam-enabled device such as a tablet computer or smart phone.

The present invention is a respiratory monitoring device which uses 2+ sensors to map respiratory motion in patients to interpret into a respiratory effort and severity score. The core components of the invention are contact-based sensors that measure relative motion of the chest, abdomen, and/or other key anatomical features, a processing unit which takes in the data from all sensors, an algorithm that analyzes and compares the data from each sensor to understand relative motion and interpret it into clinically-relevant information, and a display screen that shares this information with clinicians. The sensors are connected to each other and the information processing unit which shares data with the screen for display of a respiratory severity score based on analysis of Thoraco-Abdominal Asynchrony (TAA) or similar indicators of respiratory effort as measured by the sensor network and analyzed by the algorithm.

Systems and methods of identifying medical disorders in one or more subjects are disclosed herein. In one embodiment, sound is transmitted toward a subject and at least a portion of the sound reflected by the subject and is acquired as echo data. The acquired echo data is used to generate a motion waveform having a plurality of peaks detected therein. At feast a portion of the plurality of peaks may be indicative of movement of the subject. One or more medical disorders in the subject can be identified based on, for example, time durations and/or amplitude changes between peaks detected in the motion waveform.