Abstract: The efficient utilization is provided within a cloud-computing environment where a master plane executing on a master node controls spinning up and down worker nodes, with worker nodes whose output is necessary for functioning of other worker nodes being spun up before the worker nodes needing that output. By dedicating the available computational resources to the earlier-spun worker nodes, these worker nodes are able to process a greater amount of monitoring data. Once the output of these worker nodes is ready, the available resources are reallocated to the worker nodes that use the output of the earlier-spun worker nodes. Additional control over the cardiac monitoring can be provided by communicating with a monitor to increase the rate at which the monitor transmits the collected data as well as pausing generation of alerts based on the monitoring data if such alerts are likely to be inaccurate.

Abstract: A method for continuous data transfer is provided. Data blocks are generated from a continuous data stream captured via a physiological monitoring device by segmenting data from the continuous data stream into the data blocks. A time at which the data associated with each data block occurs is determined and a sample number is associated with each data block. The data blocks are transmitted from the physiological monitoring device to a server. The data blocks are ordered on the server based on the time and the sample number associated with each data block.

Abstract: A method for continuous data transfer is provided. Data blocks are generated from a continuous data stream captured via a physiological monitoring device by segmenting data from the continuous data stream into the data blocks. A time at which the data associated with each data block occurs is determined and a sample number is associated with each data block. The data blocks are transmitted from the physiological monitoring device to a server. The data blocks are ordered on the server based on the time and the sample number associated with each data block.

Abstract: A system and method for atrial fibrillation detection in non-noise ECG data with the aid of a digital computer are provided. Electrocardiography (ECG) features and annotated patterns of the features are maintained in a database, at least some of the patterns associated with atrial fibrillation. A classifier is trained based on the annotated patterns, the classifier implemented by a convolutional neural network. A representation of an ECG signal recorded by one or more ambulatory monitors is received. Noise is detected in the representations and ECG features in the representation falling within each of the non-noise temporal windows are detected. The trained classifier is used to identify patterns of the ECG features. A value indicative of whether portions of the representation are associated the patient experiencing atrial fibrillation is calculated. That one or more of the portions are associated with the patient experiencing atrial fibrillation is determined.

Abstract: A system for facilitating a cardiac rhythm disorder diagnosis is provided. A download station is adapted to retrieve cutaneous action potentials of a patient recorded as ECG data. A plot of R-R interval data is generated based on the ECG data and displayed. A presentation of the R-R interval data in the display is changed by identifying a section of the R-R interval data and removing portions of the R-R interval data associated with an earlier recordation time than the R-R interval data in the section and a later recordation time than the R-R interval data in the section. The R-R interval data remaining in the display is provided in greater detail. Report strips are generated from the remaining R-R interval data and classified with a cardiac condition of the patient.

Abstract: A system and method for atrial fibrillation detection in non-noise ECG data with the aid of a digital computer are provided. Electrocardiography (ECG) features and annotated patterns of the features are maintained in a database, at least some of the patterns associated with atrial fibrillation. A classifier is trained based on the annotated patterns, the classifier implemented by a convolutional neural network. A representation of an ECG signal recorded by one or more ambulatory monitors is received. Noise is detected in the representations and ECG features in the representation falling within each of the non-noise temporal windows are detected. The trained classifier is used to identify patterns of the ECG features. A value indicative of whether portions of the representation are associated the patient experiencing atrial fibrillation is calculated. That one or more of the portions are associated with the patient experiencing atrial fibrillation is determined.

Abstract: A system and method for neural-network-based atrial fibrillation detection with the aid of a digital computer are provided. Electrocardiography (ECG) features and annotated patterns of the features are maintained in a database, at least some of the patterns associated with atrial fibrillation. A classifier is trained based on the annotated patterns, the classifier implemented by a convolutional neural network. A representation of an ECG signal recorded by one or more ambulatory monitors is received. ECG features in the representation falling within each of the temporal windows are detected. The trained classifier is used to identify patterns of the ECG features. At least one matrix with weights for the patterns are generated. A value indicative of whether portions of the representation are associated the patient experiencing atrial fibrillation is calculated. That one or more of the portions are associated with the patient experiencing atrial fibrillation is determined.

Abstract: A method for continuous data transfer is provided. Data blocks are generated from a continuous data stream captured via a physiological monitoring device by segmenting data from the continuous data stream into the data blocks. A time at which the data associated with each data block occurs is determined and a sample number is associated with each data block. The data blocks are transmitted from the physiological monitoring device to a server. The data blocks are ordered on the server based on the time and the sample number associated with each data block.

Abstract: A system for facilitating a cardiac rhythm disorder diagnosis is provided. A download station is adapted to retrieve cutaneous action potentials of a patient recorded as ECG data. A plot of R-R interval data is generated based on the ECG data and displayed. A presentation of the R-R interval data in the display is changed by identifying a section of the R-R interval data and removing portions of the R-R interval data associated with an earlier recordation time than the R-R interval data in the section and a later recordation time than the R-R interval data in the section. The R-R interval data remaining in the display is provided in greater detail. Report strips are generated from the remaining R-R interval data and classified with a cardiac condition of the patient.

Abstract: A system for facilitating a cardiac rhythm disorder diagnosis with the aid of a digital computer is provided. A download station retrieves cutaneous action potentials of a patient by an ECG monitoring and recording device as ECG data. An R-R interval plot of the ECG data includes heart rates associated with R-R intervals plotted along a y-axis of the plot with respect to time, with time represented by an x-axis. The R-R intervals are calculated as a difference between recording times of successive pairs of R-wave peaks and each heart rate is associated with each time difference. Runs of the R-R intervals in the plot are identified and each run represents a cardiac event. Cardiac rhythm measurements are automatically calculated and occurrences of different types of the cardiac events are tracked. One or more of the cardiac events and the cardiac rhythm measurements are reported.

Abstract: A system and method for neural-network-based atrial fibrillation detection with the aid of a digital computer are provided. Electrocardiography (ECG) features and annotated patterns of the features are maintained in a database, at least some of the patterns associated with atrial fibrillation. A classifier is trained based on the annotated patterns, the classifier implemented by a convolutional neural network. A representation of an ECG signal recorded by one or more ambulatory monitors is received. ECG features in the representation falling within each of the temporal windows are detected. The trained classifier is used to identify patterns of the ECG features. At least one matrix with weights for the patterns are generated. A value indicative of whether portions of the representation are associated the patient experiencing atrial fibrillation is calculated. That one or more of the portions are associated with the patient experiencing atrial fibrillation is determined.

Abstract: A system for facilitating a cardiac rhythm disorder diagnosis with the aid of a digital computer is provided. A download station retrieves cutaneous action potentials of a patient by an ECG monitoring and recording device as ECG data. An R-R interval plot of the ECG data includes R-R intervals plotted along an x-axis of the plot and heart rates associated with the R-R intervals plotted along a y-axis of the plot. The R-R intervals are calculated as a difference between recording times of successive pairs of R-wave peaks and each heart rate is associated with each time difference. Runs of the R-R intervals in the plot are identified and each run represents a cardiac event. Cardiac rhythm measurements are automatically calculated and occurrences of different types of the cardiac events are tracked. One or more of the cardiac events and the cardiac rhythm measurements are reported.

Abstract: A system and method for classifier-based atrial fibrillation detection with the aid of a digital computer are provided. Electrocardiography (ECG) features and annotated patterns of the features are maintained in a database, at least some of the patterns associated with atrial fibrillation. A classifier is trained based on the annotated patterns. A representation of an ECG signal recorded by one or more ambulatory monitors is received. ECG features in the representation falling within each of the temporal windows are detected. The trained classifier is used to identify patterns of the ECG features. At least one matrix with weights for the patterns are generated. A value indicative of whether portions of the representation are associated the patient experiencing atrial fibrillation is calculated. That one or more of the portions are associated with the patient experiencing atrial fibrillation is determined. An action is taken based on one or more of the determinations.

Abstract: A system for facilitating a cardiac rhythm disorder diagnosis with the aid of a digital computer is provided. A download station retrieves cutaneous action potentials of a patient recorded over a time by an ECG device as ECG data. An R-R interval plot of the ECG data includes R-R intervals plotted along an x-axis of the plot and heart rates associated with the R-R intervals plotted along a y-axis. The R-R intervals are calculated as a difference between recording times of successive pairs of R-wave peaks. Each heart rate is associated with each time difference. One or more portions of the R-R intervals are identified in the plot. Each portion of the R-R intervals includes a cardiac event. Report strips are generated and each includes one portion of the R-R intervals and a portion of the ECG data. The report strips are included in a cardiac report for the patient.

Abstract: A system and method for classifier-based atrial fibrillation detection with the aid of a digital computer are provided. Electrocardiography (ECG) features and annotated patterns of the features are maintained in a database, at least some of the patterns associated with atrial fibrillation. A classifier is trained based on the annotated patterns. A representation of an ECG signal recorded by one or more ambulatory monitors is received. ECG features in the representation falling within each of the temporal windows are detected. The trained classifier is used to identify patterns of the ECG features. At least one matrix with weights for the patterns are generated. A value indicative of whether portions of the representation are associated the patient experiencing atrial fibrillation is calculated. That one or more of the portions are associated with the patient experiencing atrial fibrillation is determined. An action is taken based on one or more of the determinations.

Abstract: A system for facilitating a cardiac rhythm disorder diagnosis with the aid of a digital computer is provided. A download station retrieves cutaneous action potentials of a patient recorded over a time by an ECG device as ECG data. An R-R interval plot of the ECG data includes R-R intervals plotted along an x-axis of the plot and heart rates associated with the R-R intervals plotted along a y-axis. The R-R intervals are calculated as a difference between recording times of successive pairs of R-wave peaks. Each heart rate is associated with each time difference. One or more portions of the R-R intervals are identified in the plot. Each portion of the R-R intervals includes a cardiac event. Report strips are generated and each includes one portion of the R-R intervals and a portion of the ECG data. The report strips are included in a cardiac report for the patient.

Abstract: A system and method for machine-learning based atrial fibrillation detection are provided. A database is maintained that is operable to maintain a plurality of ECG features and annotated patterns of the features. At least one server is configured to: train a classifier based on the annotated patterns in the database; receive a representation of an ECG signal recorded by an ambulatory monitor recorder during a plurality of temporal windows; detect a plurality of the ECG features in at least some of the portions of the representation falling within each of the temporal windows; use the trained classifier to identify patterns of the ECG features within one or more of the portions of the ECG signal; for each of the portions, calculate a score indicative of whether the portion of the representation within that ECG signal is associated the patient experiencing atrial fibrillation; and take an action based on the score.

Abstract: A system and method for machine-learning based atrial fibrillation detection are provided. A database is maintained that is operable to maintain a plurality of ECG features and annotated patterns of the features. At least one server is configured to: train a classifier based on the annotated patterns in the database; receive a representation of an ECG signal recorded by an ambulatory monitor recorder during a plurality of temporal windows; detect a plurality of the ECG features in at least some of the portions of the representation falling within each of the temporal windows; use the trained classifier to identify patterns of the ECG features within one or more of the portions of the ECG signal; for each of the portions, calculate a score indicative of whether the portion of the representation within that ECG signal is associated the patient experiencing atrial fibrillation; and take an action based on the score.

Abstract: A volume is represented using high spatial resolution ultrasound data. By modulating B-mode data with Doppler or flow information, the spatial resolution or contrast of the B-mode data may be enhanced. The same set of ultrasound data is used for identifying a boundary, for placing the perspective position within the volume and rendering from the perspective position. The identification of the boundary and rendering are automated or performed by a processor. Ultrasound data may be used for generating three-dimensional fly-through representations, allowing for virtual endoscopy or other diagnostically useful views of structure or fluid flow channel. Virtual processes are not invasive, allow for more patient comfort, have selectable or different depths of focus, may allow for fly-through of different types of anatomy and may be used as a training tool.

Abstract: An ultrasound medical imaging system having a flexible, versatile and programmable architecture, particularly with respect to a back end subsystem. The architecture for the back end subsystem is generalized and includes multiple identical and interchangeable processing elements or boards that combine to produce sufficient processing power to support real time imaging in the various modes of ultrasound operations. The architecture may also tune front end parameters for a front end subsystem based on back end processing, bypass faulty processing elements or boards, and/or distribute processing load across operational processing elements or boards. A method for operating such an ultrasound medical imaging system is also disclosed.