An example device for detecting one or more parameters of a cardiac signal is disclosed herein. The device includes one or more electrodes and sensing circuitry configured to sense a cardiac signal via the one or more electrodes. The device further includes processing circuitry configured to determine an R-wave of the cardiac signal and determine whether the R-wave is noisy. Based on the R-wave being noisy, the processing circuitry is configured to determine whether the cardiac signal around a determined T-wave is noisy. Based on the cardiac signal around the determined T-wave not being noisy, the processing circuitry is configured to determine a QT interval or a corrected QT interval based on the determined T-wave and the determined R-wave.

An example device for detecting one or more parameters of a cardiac signal is disclosed herein. The device includes one or more electrodes and sensing circuitry configured to sense a cardiac signal via the one or more electrodes. The device further includes processing circuitry configured to determine an R-wave of the cardiac signal and determine whether the R-wave is noisy. Based on the R-wave being noisy, the processing circuitry is configured to determine whether the cardiac signal around a determined T-wave is noisy. Based on the cardiac signal around the determined T-wave not being noisy, the processing circuitry is configured to determine a QT interval or a corrected QT interval based on the determined T-wave and the determined R-wave.

There is provided a learning device, including a learning unit that learns output related to a target feature point to be observed in a repetition section observed periodically, with the use of the first sensor data being acquired by the first system and having a time length corresponding to the repetition section, as learning data, and of teacher data based on the second sensor data acquired by the second system at a time point when a specific period of time has elapsed since a start time point of the time length related to the first sensor data, the second system being less affected by noises than the first system, in which the specific period of time is set on the basis of a time length from a start time point of the repetition section to a time point at which the target feature point is expected to appear.

An example device for detecting one or more parameters of a cardiac signal is disclosed herein. The device includes one or more electrodes and sensing circuitry configured to sense a cardiac signal via the one or more electrodes. The device further includes processing circuitry configured to determine an R-wave of the cardiac signal and determine a previous RR interval of the cardiac signal and a current RR interval of the cardiac signal based on the determined R-wave. The processing circuitry is further configured to determine a search window based on one or more of the current RR interval or the previous RR interval, determine a T-wave of the cardiac signal in the search window, and determine a QT interval based on the determined T-wave and the determined R-wave.

An example device for detecting one or more parameters of a cardiac signal is disclosed herein. The device includes one or more electrodes and sensing circuitry configured to sense a cardiac signal via the one or more electrodes. The device further includes processing circuitry configured to determine an R-wave of the cardiac signal and determine a previous RR interval of the cardiac signal and a current RR interval of the cardiac signal based on the determined R-wave. The processing circuitry is further configured to determine a search window based on one or more of the current RR interval or the previous RR interval, determine a T-wave of the cardiac signal in the search window, and determine a QT interval based on the determined T-wave and the determined R-wave.

Methods and systems are provided for predicting cardiac arrhythmias based on multi-modal patient monitoring data via deep learning. In an example, a method may include predicting an imminent onset of a cardiac arrhythmia in a patient, before the cardiac arrhythmia occurs, by analyzing patient monitoring data via a multi-arm deep learning model, outputting an arrhythmia event in response to the prediction, and outputting a report indicating features of the patient monitoring data contributing to the prediction. In this way, the multi-arm deep learning model may predict cardiac arrhythmias before their onset.

Described herein are methods, devices, and systems that monitor heart rate and/or for arrhythmic episodes based on sensed intervals that can include true R-R intervals as well as over-sensed R-R intervals. True R-R intervals are initially identified from an ordered list of the sensed intervals by comparing individual sensed intervals to a sum of an immediately preceding two intervals, and/or an immediately following two intervals. True R-R intervals are also identified by comparing sensed intervals to a mean or median of durations of sensed intervals already identified as true R-R intervals. Individual intervals in a remaining ordered list of sensed intervals (from which true R-R intervals have been removed) are classified as either a short interval or a long interval, and over-sensed R-R intervals are identified based on the results thereof. Such embodiments can be used, e.g., to reduce the reporting of and/or inappropriate responses to false positive tachycardia detections.

A smart hardware security engine using biometric features and hardware-specific features is provided. The smart security engine can combine one or more entropy sources, including individually distinguishable biometric features, and hardware-specific features to perform secret key generation for user registration and authentication. Such hybrid signatures may be distinct from person-to-person (e.g., due to the biometric features) and from device-to-device (e.g., due to the hardware-specific features) while varying over time. Thus, embodiments described herein can be used for personal device authentication as well as secret random key generation, significantly reducing the scope of an attack.

Methods and computer systems are described that classify a cardiogram as being an atrial fibrillation (AF) or ventricular fibrillation (VF) cardiogram, automatically detect an AF epoch within an AF cardiogram, and automatically detect a VF epoch within a VF cardiogram. A classification and identification (C&I) system includes a classification system, an AF identification system, and a VF identification system. The C&I system processes cardiograms collected from patients to classify the cardiograms as being AF cardiograms or VF cardiograms and to identify AF epochs within the AF cardiograms or VF epochs within the VF cardiograms. The C&I system may then identify an AF source location of an AF based on the AF epochs and a VF source location of a VF based on the VF epochs. The C&I system may display a graphic of a heart that includes an indication of a source location.

Methods and computer systems are described that classify a cardiogram as being an atrial fibrillation (AF) or ventricular fibrillation (VF) cardiogram, automatically detect an AF epoch within an AF cardiogram, and automatically detect a VF epoch within a VF cardiogram. A classification and identification (C&I) system includes a classification system, an AF identification system, and a VF identification system. The C&I system processes cardiograms collected from patients to classify the cardiograms as being AF cardiograms or VF cardiograms and to identify AF epochs within the AF cardiograms or VF epochs within the VF cardiograms. The C&I system may then identify an AF source location of an AF based on the AF epochs and a VF source location of a VF based on the VF epochs. The C&I system may display a graphic of a heart that includes an indication of a source location.