Resmed Sensor Technologies Limited

University College Dublin

Resmed Limited

Resmed Pty Ltd

Stephen James Redmond

Conor Joseph Heneghan

Steven Paul Farrugia

Klaus Henry Schindhelm

Alberto Zaffaroni

Niall Fox

Philip De Chazal

Michael Waclaw Colefax

Qestra Camille Mulqueeny

Nigel Hamilton Lovell

Faizan Javed

Emer O'Hare

Rami Khushaba

Patrick Celka

Conor Heneghan

Disclosed is a cardio-pulmonary health monitoring apparatus. The apparatus comprises a contactless motion sensor configured to generate one or more movement signals representing bodily movement of a patient during a monitoring session; a processor; and a memory storing program instructions configured to cause the processor to carry out a method of processing the one or more movement signals. The method comprises extracting one or more sleep disordered breathing features from the one or more movement signals, and predicting whether a clinical event is likely to occur during a predetermined prediction horizon based on the one or more sleep disordered breathing features.

Method and apparatus for monitoring cardio-pulmonary health

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

Sleep monitoring system

A controller or processor(s) implements detection of respiratory related conditions, such as asynchrony, associated with use of a respiratory treatment apparatus or ventilator. Based on data derived from sensor signals associated with the respiratory treatment, the detector may evaluate a feature set of detection values to determine whether or not an asynchrony occurs in a breath of the patient's respiratory cycle such as by comparing the values against a set of thresholds. Different events may also be identified based on the particular feature set and threshold(s) involved in the detection processing. Automated determination of feature sets may also be implemented to design different asynchrony event classifiers. The methodologies may be implemented by computers or by respiratory treatment apparatus. The detection of such asynchrony events can then also serve as part of control logic for automated adjustments to the control parameters of the respiratory treatment generated by the respiratory treatment apparatus.

Detection of asynchrony

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

A controller or processor(s) () implements detection of respiratory related conditions, such as asynchrony, associated with use of a respiratory treatment apparatus () or ventilator. Based on data derived from sensor signals associated with the respiratory treatment, the detector may evaluate a feature set of detection values to determine whether or not an asynchrony occurs in a breath of the patient's respiratory cycle such as by comparing the values against a set of thresholds. Different events may also be identified based on the particular feature set and threshold(s) involved in the detection processing. Automated determination of feature sets may also be implemented to design different asynchrony event classifiers. The methodologies may be implemented by computers or by respiratory treatment apparatus. The detection of such asynchrony events can then also serve as part of control logic for automated adjustments to the control parameters of the respiratory treatment generated by the respiratory treatment apparatus.

A sleep monitoring system includes an ECG device () and a respiration inductance plethysmogram () which monitor cardiac activity and physical (ribcage) respiration respectively and feed representative signals to a digital data processor. Operations (-) process the beat interval data, while in a second thread, operations (-) independently process the amplitude modulation of the ECG data caused by the respiratory motion of the subject. The inductance plethysmogram device () provides an input to the processor which represents respiration as directly monitored independently of the ECG. Operations (-) 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.

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Dublin, Ireland

Stephen James Redmond