Abstract: A diagnostic electrocardiogram system employing an electrode lead system (40) for generating one or more electrode signals indicative of electrical activity of a subject heart (10). The diagnostic electrocardiogram system further employs a diagnostic electrocardiograph (50) coupled to the electrode lead system (40) for communicating (e.g., listing, displaying and/or printing a subject electrocardiogram (20) and one more diagnostic electrocardiograms (30) designated as a morphology match to the subject electrocardiogram (20). The subject electrocardiogram (20) includes one or more interpretations of ECG features derived from tire electrical activity of the subject heart (10) as indicated by tire electrode signal(s) (e.g., an algorithmic interpretation and/or an electrocardiographer interpretation of the subject electrocardiogram (20)). The diagnostic electrocardiogram(s) includes one or more diagnoses of ECG features derived from recorded electrical activity of diagnosed heart(s) (11) (e.g.

Abstract: The present disclosure describes various systems and methods of modifying a display to automatically visually trace an abnormality associated with a physiological signal received from a patient (i.e., subject). In particular aspects, the systems and methods described herein utilize three-dimensional display outputs of physiological signals that allow for the immediate differentiation between normal portions of the physiological signal and abnormal portions of the physiological signal.

Abstract: A non-transitory computer-readable medium stores instructions readable and executable by at least one electronic processor (20) to perform an atrial fibrillation (AF) detection method (100). The method includes: generating a time-frequency representation of an electrocardiogram (ECG) signal acquired over a time interval; processing the time-frequency representation using a neural network (NN) (32) to output probabilities for rhythms of a set of rhythms including at least atrial fibrillation; assigning a rhythm for the ECG signal based on the probabilities for the rhythms of the set of rhythms output by the neural network; and controlling a display device (24) to display the rhythm assigned to the ECG signal.

Abstract: Abstract: A traumatic brain injury (“TBI”) guideline system employing a patient monitoring sensor (30) and a patient monitoring device (10). In operation, the patient monitoring sensor (30) generates data for monitoring a TBI parameter of a patient (e.g., systolic blood pressure, blood oxygen saturation or carbon dioxide expiration of the patient), and the patient monitoring device (10) generates a TBI indicator derived from a comparison of the TBI parameter data to parameter guideline data associated with a poten -tial TBI of the patient. The patient monitoring device (10) may include a patient data monitor module (17a) to monitor the TBI parameter data, and a TBI monitor module (17b) to generate the TBI indicator. The TBI indicator is informative of a TBI status of the patient (e.g., a hypotension status, a hypoxia status or a ventilation status of the patient), and/or a TBI treatment for the patient (e.g., a ventilation treatment for the patient).

Abstract: Techniques are described herein for converting time series data such as electrocardiogram (“ECG”) data into forms suitable for application across machine learning models, and for applying those converted data as input across machine learning models to, for instance, determine health conditions of underlying subjects. In various embodiments, a two-dimensional image may be generated (601) based on vectorcardiography (“VCG”) data, wherein the VCG data is measured directly or is based on electrocardiogram (“ECG”) data measured from a subject. The two-dimensional image may be applied (612) as input across a machine learning model to generate output, wherein the machine learning model is configured for use in processing two-dimensional images. A health condition of the subject may be determined (614) based on the output.

Abstract: A non-transitory computer-readable medium stores instructions readable and executable by at least one electronic processor (20) to perform an atrial fibrillation (AF) detection method (100). The method includes: generating a time-frequency representation of an electrocardiogram (ECG) signal acquired over a time interval; processing the time-frequency representation using a neural network (NN) (32) to output probabilities for rhythms of a set of rhythms including at least atrial fibrillation; assigning a rhythm for the ECG signal based on the probabilities for the rhythms of the set of rhythms output by the neural network; and controlling a display device (24) to display the rhythm assigned to the ECG signal.

Abstract: A system and method for modeling and visualizing ST segment morphology in an ECG. Many cardiac conditions show ST-elevation in ECG data and may be misdiagnosed as a consequence. The exemplary embodiments model a segment in the ECG with a curve and extract features from the curve to discriminate between the cardiac conditions, including STEMI.

Abstract: A traumatic brain injury (“TBI”) guideline system employing a patient monitoring sensor (30) and a patient monitoring device (10). In operation, the patient monitoring sensor (30) generates data for monitoring a TBI parameter of a patient (e.g., systolic blood pressure, blood oxygen saturation or carbon dioxide expiration of the patient), and the patient monitoring device (10) generates a TBI indicator derived from a comparison of the TBI parameter data to parameter guideline data associated with a potential TBI of the patient. The patient monitoring device (10) may include a patient data monitor module (17a) to monitor the TBI parameter data, and a TBI monitor module (17b) to generate the TBI indicator. The TBI indicator is informative of a TBI status of the patient (e.g., a hypotension status, a hypoxia status or a ventilation status of the patient), and/or a TBI treatment for the patient (e.g., a ventilation treatment for the patient).

Abstract: A PPG PRV device for generating a PRV parameter of a PPG signal (20) as an estimation of a HRV parameter of an ECG signal. The PPG PRV device employs a PPG probe (700) and a PPG PRV controller (710). In operation, the PPG probe (700) generate a PPG signal (20). In response thereto, the PPG PRV controller (710) generates a normalized PPG signal (20?) including a plurality of pulses of the PPG signal (20) designated as normal pulses by the PPG PRV controller (710) and excluding at least one pulse of the PPG signal (20) designated at least one abnormal pulse by the PPG PRV controller (710), wherein the normalized PPG signal (20?) is HRV comparable to the ECG signal. The PPG PRV controller (710) derives the PRV parameter from a HRV measurement of the normalized PPG signal (20?).

Abstract: Various embodiments of a physiological waveform summarizing system of the present disclosure encompass a clinical physiological waveform (CPW) interface (20), and a physiological waveform summarizing monitor (30). In operation, the monitor (30) extracts a set of dominant physiological templates (41) from a clinical physiological waveform (CPW) communicated by the interface (20) to the monitor (30). The set of dominant physiological templates (41) represent a dominating major physiological rhythm (DMPR) of the clinical physiological waveform (CPW) temporally spanning over consecutive intervals of the clinical physiological waveform (CPW), and each dominant physiological template (41) is derived from a different interval of the consecutive intervals of the clinical physiological waveform (CPW).

Abstract: There is provided a method and apparatus for determining respiratory information for a subject. One or more physiological signals indicative of at least one physiological characteristic of the subject is acquired (202) and contextual information relating to at least one of the subject and the one or more physiological signals is obtained (204). Based on the contextual information, at least one signal processing algorithm for each of the one or more physiological signals is selected (206), the at least one signal processing algorithm being adapted to determine respiratory information. Respiratory information for the subject is determined based on the one or more physiological signals using the at least one signal processing algorithm selected for the one or more physiological signals (208).

Abstract: Methods and systems for bedsore prevention. In accordance with various embodiments, methods and systems use information available from ECG and/or EMG sensor devices to determine whether a patient has performed a qualified movement within a predetermined time period. Upon determining a patient has not performed a qualified movement within the predetermined time period, a notification to that effect may be communicated to a clinical team member.

Abstract: There is provided a method and apparatus for determining respiratory information for a subject. One or more physiological signals indicative of at least one physiological characteristic of the subject is acquired (202) and contextual information relating to at least one of the subject and the one or more physiological signals is obtained (204). Based on the contextual information, at least one signal processing algorithm for each of the one or more physiological signals is selected (206), the at least one signal processing algorithm being adapted to determine respiratory information. Respiratory information for the subject is determined based on the one or more physiological signals using the at least one signal processing algorithm selected for the one or more physiological signals (208).

Abstract: A PPG PRV device for generating a PRV parameter of a PPG signal (20) as an estimation of a HRV parameter of an ECG signal. The PPG PRV device employs a PPG probe (700) and a PPG PRV controller (710). In operation, the PPG probe (700) generate a PPG signal (20). In response thereto, the PPG PRV controller (710) generates a normalized PPG signal (20?) including a plurality of pulses of the PPG signal (20) designated as normal pulses by the PPG PRV controller (710) and excluding at least one pulse of the PPG signal (20) designated at least one abnormal pulse by the PPG PRV controller (710), wherein the normalized PPG signal (20?) is HRV comparable to the ECG signal. The PPG PRV controller (710) derives the PRV parameter from a HRV measurement of the normalized PPG signal (20?).

Abstract: An ECG controller for an ECG device is connectable to a base ECG lead system (e.g., a 12-lead system) whereby the ECG controller implements an ECG waveform morphology based and ECG lead redundancy based detection and classification of any cable interchange (e.g., a limb cable interchange or a precordial cable interchange) between the ECG controller and the base ECG lead system. Alternatively, the ECG controller is further connectable to a sub-base ECG lead system (e.g., a limb only-lead system or a limited precordial-lead system) whereby the ECG controller implements an ECG waveform morphology based detection and classification of any cable interchange (e.g., a limb cable interchange or a precordial cable interchange) between the electrode interface and the sub-base ECG lead system.

Abstract: A non-transitory computer-readable medium stores instructions readable and executable by at least one electronic processor (20) to perform an atrial fibrillation (AF) detection method (100). The method includes: generating a time-frequency representation of an electrocardiogram (ECG) signal acquired over a time interval; processing the time-frequency representation using a neural network (NN) (32) to output probabilities for rhythms of a set of rhythms including at least atrial fibrillation; assigning a rhythm for the ECG signal based on the probabilities for the rhythms of the set of rhythms output by the neural network; and controlling a display device (24) to display the rhythm assigned to the ECG signal.

Abstract: There is provided a method and apparatus for determining respiratory information for a subject. One or more physiological signals indicative of at least one physiological characteristic of the subject is acquired (202) and contextual information relating to at least one of the subject and the one or more physiological signals is obtained (204). Based on the contextual information, at least one signal processing algorithm for each of the one or more physiological signals is selected (206), the at least one signal processing algorithm being adapted to determine respiratory information. Respiratory information for the subject is determined based on the one or more physiological signals Obtain contextual information relating using the at least one signal processing algorithm selected for the one or more physiological signals (208).

Abstract: A diagnostic electrocardiogram system employing an electrode lead system (40) for generating one or more electrode signals indicative of electrical activity of a subject heart (10). The diagnostic electrocardiogram system further employs a diagnostic electrocardiograph (50) coupled to the electrode lead system (40) for communicating (e.g., listing, displaying and/or printing a subject electrocardiogram (20) and one more diagnostic electrocardiograms (30) designated as a morphology match to the subject electrocardiogram (20). The subject electrocardiogram (20) includes one or more interpretations of ECG features derived from tire electrical activity of the subject heart (10) as indicated by tire electrode signal(s) (e.g., an algorithmic interpretation and/or an electrocardiographer interpretation of the subject electrocardiogram (20)). The diagnostic electrocardiogram(s) includes one or more diagnoses of ECG features derived from recorded electrical activity of diagnosed heart(s) (11) (e.g.

Abstract: When detecting ischemia and/or myocardial infarction in a subject, electrocardiogram (ECG) segments are analyzed for elevated ST segments indicative of ischemia and/or infarction. To mitigate false positive alerts, an ECG segment comprising an elevated ST segment that triggers an alert is presented to a clinician for verification as being indicative of ischemia or infarction, or as being attributable to a confounding condition not indicative of ischemia or infarction. Clinician feedback is used to adjust an elevated ST segment detection algorithm to improve accuracy and mitigate false positive alerts.

Abstract: In various embodiments, a plurality of inputs for a CDS algorithm that is executable by one or more processors may be identified (602). A plurality of health parameters associated with a patient-of-interest may be obtained (604) for use as at least some of the plurality of inputs for the CDS algorithm. One or more of the plurality of inputs for which a corresponding health parameter associated with the patient is out-of-date or unavailable may be identified as suboptimal (606). The CDS algorithm may then be executed (616) using the plurality of health parameters as input. Output indicative of a result of the CDS algorithm may be rendered (618). In various embodiments, the output may present at least one data point associated with the suboptimal one or more inputs more conspicuously than one or more data points associated with other inputs, e.g., using visual or audible annotations.