A method for guiding deep breathing may include receiving a request from a user to initiate a deep breathing exercise on a user-controlled device. The method may include monitoring deep breathing using one or more sensors on an ear-worn device in response to initiating the deep breathing exercise. Examples of sensors include at least one of a motion detector, a microphone, a heart rate sensor, and an electrophysiological sensor. The method may further include initiating an end to the deep breathing exercise. The method may be used with various hearing systems including an ear-worn device and optionally a user-controllable device, such as a smartphone.

The medical system and method detect arrhythmic events. The medical system includes at least one processor programmed to perform the method. A photoplethysmogram (PPG) signal generated using a PPG probe positioned on or within a patient and a pulse signal generated using an accelerometer positioned on or within the patient received. Features from the PPG signal are extracted to PPG feature vectors, and features are extracted from the pulse signal to pulse feature vectors. The PPG feature vectors are correlated with the pulse feature vectors, and correlated PPG feature vectors and correlated pulse feature vectors are evaluated to detect arrhythmic events.

The medical system and method detect arrhythmic events. The medical system includes at least one processor programmed to perform the method. A photoplethysmogram (PPG) signal generated using a PPG probe positioned on or within a patient and a pulse signal generated using an accelerometer positioned on or within the patient received. Features from the PPG signal are extracted to PPG feature vectors, and features are extracted from the pulse signal to pulse feature vectors. The PPG feature vectors are correlated with the pulse feature vectors, and correlated PPG feature vectors and correlated pulse feature vectors are evaluated to detect arrhythmic events.

A device, system and method unobtrusively and reliably detect apnoea of a subject (2). An input unit (11) receives image data of subject. The image data includes a sequence of images over time. A cardiac activity extraction unit (12) extracts from the image data a cardiac activity signal representing the subject's cardiac activity from a skin area of the subject using remote photoplethysmography. A motion signal extraction unit (14) extracts from said image data a motion signal representing motion of a subject's body part caused by breathing of the subject. An analysis unit (16) determines a similarity between said cardiac activity signal and said motion signal. A decision unit (18) detects apnoea of the subject based on the determined similarity.

A device, system and method unobtrusively and reliably detect apnoea of a subject (2). An input unit (11) receives image data of subject. The image data includes a sequence of images over time. A cardiac activity extraction unit (12) extracts from the image data a cardiac activity signal representing the subject's cardiac activity from a skin area of the subject using remote photoplethysmography. A motion signal extraction unit (14) extracts from said image data a motion signal representing motion of a subject's body part caused by breathing of the subject. An analysis unit (16) determines a similarity between said cardiac activity signal and said motion signal. A decision unit (18) detects apnoea of the subject based on the determined similarity.

Devices and methods are described that provide improved diagnosis from the processing of physiological data. The methods include use of multiple algorithms and intelligently combing the results of multiple algorithms to provide a single optimized diagnostic result. The algorithms are adaptive and may be customized for particular data sets or for particular patients. Examples are shown with applications to electrocardiogram data, but the methods taught are applicable to many types of physiological data.

A system for obtaining and providing enhanced PAP metrics of a patient's sleep period includes: a pressure support device for use in providing a flow of breathing gas to the patient; a processing unit; and a number of auxiliary devices in wireless communication with the processing unit. Each auxiliary device of the number of auxiliary devices is structured to detect and collect sleep-related data of the patient. The processing unit is programmed to: receive data obtained by a number of sensors of the pressure support device during operation of the pressure support device in providing the flow of breathing gas to the patient; receive supplemental data obtained by the number of auxiliary devices while the pressure support device is not providing the flow of breathing gas to the patient; and determine the enhanced PAP metrics of the sleep period of the patient utilizing the data and the supplemental data.

Doppler signal processing device for detecting an object according to a received wireless signal. The Doppler signal processing device includes a frequency analysis unit for generating a frequency domain signal vector according to at least one digital signal, an interference suppression unit for performing a suppression operation according to the frequency domain signal vector and a frequency domain interference estimation signal vector to generate an interference suppressed frequency domain signal vector, an interference estimation unit for generating the frequency domain interference estimation signal vector according to the frequency domain signal vector, a detection unit for generating a result signal according to the interference suppressed frequency domain signal vector, an error detection unit for optionally providing an error detection control signal to the interference estimation unit to adjust a rate of updating the frequency domain interference estimation signal vector.

Doppler signal processing device for detecting an object according to a received wireless signal. The Doppler signal processing device includes a frequency analysis unit for generating a frequency domain signal vector according to at least one digital signal, an interference suppression unit for performing a suppression operation according to the frequency domain signal vector and a frequency domain interference estimation signal vector to generate an interference suppressed frequency domain signal vector, an interference estimation unit for generating the frequency domain interference estimation signal vector according to the frequency domain signal vector, a detection unit for generating a result signal according to the interference suppressed frequency domain signal vector, an error detection unit for optionally providing an error detection control signal to the interference estimation unit to adjust a rate of updating the frequency domain interference estimation signal vector.

The present disclosure relates to a method and a system for validating inspiratory muscle activity of a patient. Left and right electrical activity signals respectively representing activity of a left muscle and of a right muscle synchronized with an inspiratory effort of the patient are acquired from non-invasive sensors. A cardiac activity signal is extracted from the left and right electrical activity signals. A synchrony, a symmetry or a proportionality of the left and right electrical activity signals from which the cardiac activity signal is extracted is verified. A mechanical ventilation system incorporating the system for validating inspiratory muscle activity of the patient is also disclosed.