Abstract: A system for managing an individualized cardiac rehabilitation plan is provided. The system includes an externally worn device and a server. The server includes a processor configured to receive input regarding the rehabilitation plan; generate one or more plans specifying an individualized set of rehabilitative exercise sessions for a patient, receive electrocardiogram (ECG) and non-ECG physiological information acquired from the patient compare the ECG and/or non-ECG physiological information to predetermined criteria and dynamically adjust the cardiac rehabilitation plan based on the comparison to create an adjusted cardiac rehabilitation plan.

Abstract: A patient monitoring device includes an ECG sensor coupled to a patient, a sensor coupled to the patient and configured to detect bio-vibrational signals, and a radiofrequency monitoring device configured to produce information responsive to electromagnetic energy reflected from the patient's thoracic cavity. A processor processes the ECG signals, the bio-vibrational signals, and the radio frequency information to generate a plurality of physiological parameters of the patient. The processor also performs at least one of a predictive analysis and a trend analysis of the plurality of physiological parameters to determine a current clinical condition of the patient. The trend analysis includes determining a substantial relationship between changes in the plurality of physiological parameters.

Abstract: A medical device that includes a power source, a therapy delivery interface, therapy electrodes, electrocardiogram (ECG) sensing electrodes to sense ECG signal of a heart of a patient, a sensor interface to receive and digitize the ECG signal, and a processor. The processor is configured to analyze the ECG signal to determine a cardiac rhythm and a transform value representing a magnitude of a frequency component of the cardiac rhythm, analyze the cardiac rhythm and the transform value to detect a shockable cardiac arrhythmia by classifying the cardiac rhythm as a noise rhythm or a shockable cardiac arrhythmia rhythm based on the transform value, and causing the processor to detect the cardiac arrhythmia if classifying the cardiac rhythm as a shockable cardiac arrhythmia rhythm, initiate a treatment alarm sequence, adjust the shock delivery parameter for a defibrillation shock, and provide the defibrillation shock via the therapy electrodes.

Abstract: A wearable medical device is provided for monitoring a cardiac condition of a patient, where the device is releasably mounted to the patient's chest and includes at least two skin-facing electrodes forming a first one or more ECG leads for ongoing monitoring of heart functioning and at least one touch electrode for intermittently obtaining additional circuit vectors for deriving additional metrics regarding the functioning of the patient's heart. Each touch electrode is configured to form an additional lead/vector that is a larger vector and/or separated by at least 15° from a corresponding first lead/vector formed from the first one or more ECG leads in a vector cardiogram representation of the first one or more ECG leads and the additional lead/vector.

Abstract: A patient monitoring device includes an ECG sensor coupled to a patient, a sensor coupled to the patient and configured to detect bio-vibrational signals, and a radio frequency monitoring device configured to produce information responsive to electromagnetic energy reflected from the patient's thoracic cavity. A processor processes the ECG signals, the bio-vibrational signals, and the radio frequency information to generate a plurality of physiological parameters of the patient. The processor also performs at least one of a predictive analysis and a trend analysis of the plurality of physiological parameters to determine a current clinical condition of the patient. The trend analysis includes determining a substantial relationship between changes in the plurality of physiological parameters.

Abstract: Systems and methods are provided for monitoring progression of a cardiac disease in a patient by providing cardio-vibrational image matrixes and/or ECG image matrices generated using sensor data supplied by a medical device. In some examples, cardio-vibrational image matrices and/or ECG image matrices are output as image files. In some implementations, systems and methods are provided for using such cardio-vibrational image matrices and/or an ECG image matrices, and/or other clinical information, using machine learning classifiers, to assess cardiac risk in a patient. In some implementations, systems and methods are provided for using cardio-vibrational image matrixes and/or ECG image matrices, and/or other clinical information for real-time analysis of cardiac risk.

Abstract: A wearable medical device is provided for monitoring a cardiac condition of a patient, where the device is releasably mounted to the patient's chest and includes at least two skin-facing electrodes forming a first one or more ECG leads for ongoing monitoring of heart functioning and at least one touch electrode for intermittently obtaining additional circuit vectors for deriving additional metrics regarding the functioning of the patient's heart. Each touch electrode is configured to form an additional lead/vector that is a larger vector and/or separated by at least 15° from a corresponding first lead/vector formed from the first one or more ECG leads in a vector cardiogram representation of the first one or more ECG leads and the additional lead/vector.

Abstract: A patient-worn ambulatory cardiac monitoring device for monitoring a patient during a patient activity includes at least one physiological sensor configured to detect signals indicative of cardiac activity, an activity sensor and associated circuitry configured to monitor patient movements, and a vibrational sensor configured to monitor a cardio-vibrational signal of the patient. The at least one physiological sensor can include one of an ECG sensor and a heart rate sensor. At least one processor in communication with the at least one physiological sensor, the activity sensor, and the vibrational sensor, is configured to measure, during the patient activity, at least one time interval between an ECG fiducial point in an ECG signal and a cardio-vibrational fiducial point in the cardio-vibrational signal during a cardiac cycle of the patient’s heart.

Abstract: Techniques that enable medical devices to quickly recover from loss of sensory functions are provided. In some examples, a medical device is configured to advantageously leverage differences between a first type of sensing electrode and a second type of sensing electrode that has a shorter recovery time than the first type of sensing electrode. In some examples, a medical device is configured to reference data generated by a first conditioning circuit that is configured to process signals acquired under a first set of environmental conditions and to reference data generated by a second conditioning circuit that is configured to process signal acquired under a second set of environmental conditions. In some examples, a medical device is configured to arrange electrodes used by the medical device to acquire signals in at specific locations to reduce the amount of disruptive power the electrodes encounter.

Abstract: Systems and methods are provided for monitoring progression of a cardiac disease in a patient by providing cardio-vibrational image matrixes and/or ECG image matrices generated using sensor data supplied by a medical device. In some examples, cardio-vibrational image matrices and/or ECG image matrices are output as image files. In some implementations, systems and methods are provided for using such cardio-vibrational image matrices and/or an ECG image matrices, and/or other clinical information, using machine learning classifiers, to assess cardiac risk in a patient. In some implementations, systems and methods are provided for using cardio-vibrational image matrixes and/or ECG image matrices, and/or other clinical information for real-time analysis of cardiac risk.

Abstract: A patient-worn ambulatory cardiac monitoring device for monitoring a patient during a patient activity includes at least one physiological sensor configured to detect signals indicative of cardiac activity, an activity sensor and associated circuitry configured to monitor patient movements, and a vibrational sensor configured to monitor a cardio-vibrational signal of the patient. The at least one physiological sensor can include one of an ECG sensor and a heart rate sensor. At least one processor in communication with the at least one physiological sensor, the activity sensor, and the vibrational sensor, is configured to measure, during the patient activity, at least one time interval between an ECG fiducial point in an ECG signal and a cardio-vibrational fiducial point in the cardio-vibrational signal during a cardiac cycle of the patient's heart.

Abstract: A medical device that includes a power source, a therapy delivery interface, therapy electrodes, electrocardiogram (ECG) sensing electrodes to sense ECG signal of a heart of a patient, a sensor interface to receive and digitize the ECG signal, and a processor. The processor is configured to analyze the ECG signal to determine a cardiac rhythm and a transform value representing a magnitude of a frequency component of the cardiac rhythm, analyze the cardiac rhythm and the transform value to detect a shockable cardiac arrhythmia by classifying the cardiac rhythm as a noise rhythm or a shockable cardiac arrhythmia rhythm based on the transform value, and causing the processor to detect the cardiac arrhythmia if classifying the cardiac rhythm as a shockable cardiac arrhythmia rhythm, initiate a treatment alarm sequence, adjust the shock delivery parameter for a defibrillation shock, and provide the defibrillation shock via the therapy electrodes.

Abstract: A medical device that includes a power source, a therapy delivery interface, therapy electrodes, electrocardiogram (ECG) sensing electrodes to sense ECG signal of a heart of a patient, a sensor interface to receive and digitize the ECG signal, and a processor. The processor is configured to analyze the ECG signal to determine a cardiac rhythm and a transform value representing a magnitude of a frequency component of the cardiac rhythm, analyze the cardiac rhythm and the transform value to detect a shockable cardiac arrhythmia by classifying the cardiac rhythm as a noise rhythm or a shockable cardiac arrhythmia rhythm based on the transform value, and causing the processor to detect the cardiac arrhythmia if classifying the cardiac rhythm as a shockable cardiac arrhythmia rhythm, initiate a treatment alarm sequence, adjust the shock delivery parameter for a defibrillation shock, and provide the defibrillation shock via the therapy electrodes.

Abstract: Systems, devices, and techniques that enable medical devices to integrate and interoperate with one another are provided. In some examples, a wearable cardiac defibrillator (WCD) advantageously interoperates with an implanted pacemaker to provide a variety of benefits. For instance, in some examples, the WCD oversees execution of an antitachycardia (ATP) protocol by the implanted pacemaker and intervenes as needed. In other examples, the WCD drives an ATP protocol in which internal pacing pulses are provided by the implanted pacemaker under the control of the WCD. In other examples, the WCD monitors the activity of the implanted pacemaker to identify potential maintenance issues affecting the implanted pacemaker. The WCD and the implanted pacemaker may also interoperate to classify and act upon particular arrhythmia conditions.

Abstract: Systems and methods are provided for monitoring progression of a cardiac disease in a patient by providing cardio-vibrational image matrixes and/or ECG image matrices generated using sensor data supplied by a medical device. In some examples, cardio-vibrational image matrices and/or ECG image matrices are output as image files. In some implementations, systems and methods are provided for using such cardio-vibrational image matrices and/or an ECG image matrices, and/or other clinical information, using machine learning classifiers, to assess cardiac risk in a patient. In some implementations, systems and methods are provided for using cardio-vibrational image matrixes and/or ECG image matrices, and/or other clinical information for real-time analysis of cardiac risk.

Abstract: Systems, devices, and techniques that enable medical devices to integrate and interoperate with one another are provided. In some examples, a wearable cardiac defibrillator (WCD) advantageously interoperates with an implanted pacemaker to provide a variety of benefits. For instance, in some examples, the WCD oversees execution of an antitachycardia (ATP) protocol by the implanted pacemaker and intervenes as needed. In other examples, the WCD drives an ATP protocol in which internal pacing pulses are provided by the implanted pacemaker under the control of the WCD. In other examples, the WCD monitors the activity of the implanted pacemaker to identify potential maintenance issues affecting the implanted pacemaker. The WCD and the implanted pacemaker may also interoperate to classify and act upon particular arrhythmia conditions.

Abstract: A patient monitoring device includes an ECG sensor coupled to a patient, a sensor coupled to the patient and configured to bio-vibrational signals, and a radio frequency monitoring device configured to produce information responsive to electromagnetic energy reflected from the patient's thoracic cavity. A processor processes the ECG signals, the bio-vibrational signals, and the radio frequency information to generate a plurality of physiological parameters of the patient. The processor also performs at least one of a predictive analysis and a trend analysis of the plurality of physiological to determine a current clinical condition of the patient. The trend analysis includes determining a substantial relationship between changes in the plurality of physiological parameters.

Abstract: A patient monitoring device includes an ECG sensor coupled to a patient, a sensor coupled to the patient and configured to bio-vibrational signals, and a radio frequency monitoring device configured to produce information responsive to electromagnetic energy reflected from the patient's thoracic cavity. A processor processes the ECG signals, the bio-vibrational signals, and the radio frequency information to generate a plurality of physiological parameters of the patient. The processor also performs at least one of a predictive analysis and a trend analysis of the plurality of physiological to determine a current clinical condition of the patient. The trend analysis includes determining a substantial relationship between changes in the plurality of physiological parameters.

Abstract: A system for managing an individualized cardiac rehabilitation plan is provided. The system includes an externally worn device and a server. The server includes a processor configured to receive input regarding the rehabilitation plan; generate one or more plans specifying an individualized set of rehabilitative exercise sessions for a patient, receive electrocardiogram (ECG) and non-ECG physiological information acquired from the patient compare the ECG and/or non-ECG physiological information to predetermined criteria and dynamically adjust the cardiac rehabilitation plan based on the comparison to create an adjusted cardiac rehabilitation plan.

Abstract: A system for providing electrocardiogram (ECG) information to a healthcare provider (HCP) is provided. The system includes a network interface configured to receive drug prescription information from a computing device associated with the HCP and ECG data from a medical device associated with a patient. The system also includes a memory configured to store the drug prescription information and the ECG data. The system can further a processor configured to select a period of time over which to review the received drug prescription information and the received ECG data, retrieve the received ECG data for the selected period of time, derive one or more ECG metrics including at least one heart rate parameter, and generate a graphical representation indicating a trend in the one or more ECG metrics and a relationship between the one or more ECG metrics and the drug prescription information.