Newness and distinctiveness is claimed in the features of ornamentation as shown inside the broken line circle in the accompanying representation.

Display screen or portion thereof with graphical user interface

A method and an apparatus to analyze two measured signals that are modeled as containing desired and undesired portions such as noise, FM and AM modulation. Coefficients relate the two signals according to a model defined in accordance with the present invention. In one embodiment, a transformation is used to evaluate a ratio of the two measured signals in order to find appropriate coefficients. The measured signals are then fed into a signal scrubber which uses the coefficients to remove the unwanted portions. The signal scrubbing is performed in either the time domain or in the frequency domain. The method and apparatus are particularly advantageous to blood oximetry and pulserate measurements. In another embodiment, an estimate of the pulserate is obtained by applying a set of rules to a spectral transform of the scrubbed signal. In another embodiment, an estimate of the pulserate is obtained by transforming the scrubbed signal from a first spectral domain into a second spectral domain. The pulserate is found by identifying the largest spectral peak in the second spectral domain.

Signal processing apparatus and method

Display screen or portion thereof with transitional graphical user interface

A pulse oximeter may reduce power consumption in the absence of overriding conditions. Various sampling mechanisms may be used individually or in combination. Various parameters may be monitored to trigger or override a reduced power consumption state. In this manner, a pulse oximeter can lower power consumption without sacrificing performance during, for example, high noise conditions or oxygen desaturations.

Low power pulse oximeter

The rapidly expanding field of big data analytics has started to play a pivotal role in the evolution of healthcare practices and research. It has provided tools to accumulate, manage, analyze, and assimilate large volumes of disparate, structured, and unstructured data produced by current healthcare systems. Big data analytics has been recently applied towards aiding the process of care delivery and disease exploration. However, the adoption rate and research development in this space is still hindered by some fundamental problems inherent within the big data paradigm. In this paper, we discuss some of these major challenges with a focus on three upcoming and promising areas of medical research: image, signal, and genomics based analytics. Recent research which targets utilization of large volumes of medical data while combining multimodal data from disparate sources is discussed. Potential areas of research within this field which have the ability to provide meaningful impact on healthcare delivery are also examined.

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Big Data Analytics in Healthcare

Medical patient monitoring devices that have the capability of detecting the physical proximity of a clinician are disclosed. The medical patient monitoring devices may be configured to perform a selected action when the presence of a clinician is detected. Systems and methods for facilitating communication between medical devices that use different medical communication protocol formats are also disclosed. For example, a medical communication protocol translator can be configured to receive an input message formatted according to a first protocol format from a first medical device and to output an output message formatted according to a second protocol format supported by a second medical device using a set of translation rules. Medical monitoring reporting systems are also disclosed. The medical monitoring reporting systems may be used to analyze a stored collection of physiological parameter data to simulate the effect of changing various medical monitoring options.

Medical monitoring system

A data capture system utilizes a sensor with emitters adapted to transmit light into a fleshy medium and a detector adapted to generate intensity signals in response to receiving light after absorption by the fleshy medium. A monitor is configured to input the intensity signals, generate digitized signals from the intensity signals at a sampling rate and compute at least one physiological parameter responsive to magnitudes of the digitized signals. A data storage device is integrated with the monitor and is adapted to record data derived from the digitized signals on a removable storage media at the sampling rate.

Pulse oximetry data capture system

A method of storing streaming physiological information obtained from a medical patient in a multi-patient monitoring environment includes receiving identification information, retrieving parameter descriptors, creating a round-robin database file, receiving a data stream, and using a predetermined data rate to map the data stream to locations in the round-robin database file.

Systems and methods for storing, analyzing, retrieving and displaying streaming medical data

Physiological information obtained from a medical patient can be stored in a dynamic round-robin database. Parameter descriptors may be used to identify parameter values in the records. The parameter values can be dynamically updated by changing the parameter descriptors to provide for a flexible database. In addition, the size of files used in the database can be dynamically adjusted to account for patient condition. In certain implementations, the round-robin database can be adaptive, such that an amount of data stored in the database is adapted based on patient condition and/or signal condition. Additionally, medical data obtained from a clinical network of physiological monitors can be stored in a journal database. The medical data can include device events that occurred in response to clinician interactions with one or more medical devices and device-initiated events, such as alarms and the like. The journal database can be analyzed to derive statistics, which may be used to improve clinician and/or hospital performance.

Systems and methods for storing, analyzing, and retrieving medical data

Systems and method for monitoring patient physiological data are presented herein. In one embodiment, a physiological sensor and a mobile computing device can be connected via a cable or cables, and a processing board can be connected between the sensor and the mobile computing device to conduct advanced signal processing on the data received from the sensor before the data is transmitted for display on the mobile computing device.

Bilal Muhsin

Papers

Medical monitoring system

Pulse oximetry data capture system

Systems and methods for storing, analyzing, retrieving and displaying streaming medical data

Systems and methods for storing, analyzing, and retrieving medical data

Physiological monitor with mobile computing device connectivity