Systems, methods and devices for reducing noise in health monitoring including monitoring systems, methods and/or devices receiving a health signal and/or having at least one electrode or sensor for health monitoring.

Systems, methods and devices for reducing noise in health monitoring including monitoring systems, methods and/or devices receiving a health signal and/or having at least one electrode or sensor for health monitoring.

An Adaptive Advisory Control (AA Control) interactive process involving algorithm-based assessment and communication of physiologic and behavioral parameters and patterns assists patients with diabetes with the optimization of their glycemic control. The method and system may uses all available sources of information about the patient; (i) EO Data (e.g. self-monitoring of blood glucose (SMBG) and CMG), (ii) Insulin Data (e.g. insulin pump log files or patient treatment records), and (iii) Patient Self Reporting Data (e.g. self treatment behaviors, meals, and exercise) to: retroactively assess the risk of hypoglycemia, retroactively assess risk-based reduction of insulin delivery, and then report to the patient how a risk-based insulin reduction system would have acted consistently to prevent hypoglycemia.

Systems, methods and devices for reducing noise in health monitoring including monitoring systems, methods and/or devices receiving a health signal and/or having at least one electrode or sensor for health monitoring.

Systems, methods and devices for reducing noise in health monitoring including monitoring systems, methods and/or devices receiving a health signal and/or having at least one electrode or sensor for health monitoring.

Systems, methods and devices for reducing noise in health monitoring including monitoring systems, methods and/or devices receiving a health signal and/or having at least one electrode or sensor for health monitoring.

Systems, methods and devices for reducing noise in health monitoring including monitoring systems, methods and/or devices receiving a health signal and/or having at least one electrode or sensor for health monitoring.

The present disclosure generally relates to a system and method for collecting and merging physiological data from a plurality of medical devices and sensors where merged physiological data from the medical devices is sent to a magnetic-resonance imaging (MRI) display device. MRI scan data and data from the plurality of medical devices may be collected in real time and may also be sent over a computer network for storage. Alternatively data may be sent over one or more computer networks from each different medical device and be merged at a remote computing device.

A real-time sensing measurement apparatus for a respiratory volume of a tested person includes a controller, a pulse measuring device, a temperature sensor, a flow rate sensor and a storage module. The controller receives an electric signal transmitted by the pulse measuring device, the temperature sensor and the flow rate sensor. The controller, the pulse measuring device, the temperature sensor, the flow rate sensor and the storage module communicate through electric signals. The pulse measuring device, the temperature sensor and the flow rate sensor monitor the respiratory volume of the tested person in real time to obtain test data by the storage module. The different states of the user can be measured in real time by the pulse measuring device, the temperature sensor and the flow rate sensor, and measurement results are rectified in various aspects.

Some embodiments of the current disclosure are directed toward cardiac diagnosis and/or arrhythmia monitoring, and more particularly, systems, devices and methods for arrhythmia monitoring with a trained classifier including at least one neural network. In some embodiments, an external heart monitoring device may include a plurality ECG electrodes to sense surface ECG activity, ECG processing circuitry to process the surface ECG activity to provide at least one ECG signal, a non-transitory computer-readable medium comprising a rhythm change classifier comprising at least one neural network, and at least one processor to receive the ECG signal(s), detect with the rhythm change classifier time data corresponding to a predetermined rhythm change in the ECG signal(s), determine based on the detected time data at least one ECG signal portion corresponding to the predetermined rhythm change, and transmit the at least one determined ECG signal portion to a remote computer system.