A medical device is configured to sense first ventricular event signals from a first cardiac electrical signal and sense second ventricular event signals from a second cardiac electrical signal. The medical device is configured to determine sensed event data in response to the first ventricular event signals and the second ventricular event signals. The medical device may select one of the first cardiac electrical signal or the second cardiac electrical signal for providing input for tachyarrhythmia detection based on the sensed event data.

Systems, devices, and methods for tracking and determining the motion of a cardiac implant is disclosed. The motion of the implant is determined by transmitting acoustic energy to a tissue location using an acoustic controller-transmitter comprising an array of acoustic transducers; wherein the implant is configured to convert the transmitted acoustic energy to electrical energy; and the tracking is achieved by determining the electrical energy delivered to the tissue throughout one or more cardiac cycles in order to create a motion profile of the cardiac implant.

The invention provides systems and methods for monitoring the wellbeing of a fetus by the non-invasive detection and analysis of fetal cardiac electrical activity data.

The invention provides systems and methods for monitoring the wellbeing of a fetus by the non-invasive detection and analysis of fetal cardiac electrical activity data.

In an example, a signal segment evaluator can be programmed to evaluate a morphology of at least one electrophysiological signal to identify a signal segment of interest. The morphology of the signal segment of interest can be indicative of an electrophysiological event of a patient during a respective time interval. A reconstruction engine can be programmed to reconstruct electrophysiological signals on a surface of interest within a body of the patient based on the electrophysiological signals measured from an outer surface of the patient and geometry data representing an anatomy of the patient. A map generator can be programmed to generate a map representing the reconstructed electrophysiological signals on the surface of interest for the respective time interval of the signal segment of interest. A target generator can be programmed to identify a target site within the patient's body based on the map for the electrophysiological event.

In an example, a signal segment evaluator can be programmed to evaluate a morphology of at least one electrophysiological signal to identify a signal segment of interest. The morphology of the signal segment of interest can be indicative of an electrophysiological event of a patient during a respective time interval. A reconstruction engine can be programmed to reconstruct electrophysiological signals on a surface of interest within a body of the patient based on the electrophysiological signals measured from an outer surface of the patient and geometry data representing an anatomy of the patient. A map generator can be programmed to generate a map representing the reconstructed electrophysiological signals on the surface of interest for the respective time interval of the signal segment of interest. A target generator can be programmed to identify a target site within the patient's body based on the map for the electrophysiological event.

An apparatus yields signals that are equivalent to ECG signals and allow determination of a heartbeat interval or heart rate from bio-vibration signals including vibrations derived from heartbeats. An ECG meter acquires ECG signals of a sample, and a piezoelectric sensor acquires bio-vibration signals of the sample simultaneously. The bio-vibration signals include beating vibration signals derived from heartbeats. A learning unit of a prediction modeling apparatus establishes a prediction model by machine learning in which ECG signals are used as teaching data, and model input signals obtained by performing a specified processing on the bio-vibration signals are input. The learning unit delivers the prediction model to a prediction unit of a signal processing apparatus. The prediction model predicts and outputs pECG signals upon input of model input signals obtained by performing a specified processing on bio-vibration signals acquired from a subject under prediction with a piezoelectric sensor.

Methods and systems are provided for cardiac triggering of an imaging system. a method for an imaging system comprises acquiring, during a scan of a subject, an electrical signal indicating a periodic physiological motion of an organ of the subject, inputting a sample of the electrical signal into a trained neural network to detect whether a peak is present in the sample, triggering acquisition of image data responsive to detecting the peak in the sample, and not triggering the acquisition of image data responsive to not detecting the peak in the sample. In this way, the timing of data acquisition may be optimally and robustly synchronized with a cardiac cycle.

A wearable medical device for determining whether a patient is experiencing a cardiac event using electrocardiogram (ECG) templates includes a plurality of ECG electrodes and a medical device controller. The medical device controller includes a memory, a baseline generator, a cardiac event detector, and a mode selector. The baseline generator is configured to record a baseline ECG signal, generate one or more ECG templates, and store the one or more ECG templates in the memory. The cardiac event detector is configured to select an ECG template and compare a current ECG signal with the selected ECG template to determine whether the patient is experiencing a cardiac arrhythmia. The mode selector is configured to identify whether any ECG templates are stored in the memory, determine whether a new ECG template is needed, and, upon determining that a new ECG template is needed, enter a baselining mode by activating the baseline generator.

A wearable medical device for determining whether a patient is experiencing a cardiac event using electrocardiogram (ECG) templates includes a plurality of ECG electrodes and a medical device controller. The medical device controller includes a memory, a baseline generator, a cardiac event detector, and a mode selector. The baseline generator is configured to record a baseline ECG signal, generate one or more ECG templates, and store the one or more ECG templates in the memory. The cardiac event detector is configured to select an ECG template and compare a current ECG signal with the selected ECG template to determine whether the patient is experiencing a cardiac arrhythmia. The mode selector is configured to identify whether any ECG templates are stored in the memory, determine whether a new ECG template is needed, and, upon determining that a new ECG template is needed, enter a baselining mode by activating the baseline generator.