A patient monitoring device configured to monitor cardiac activity and sleep stage information of a patient is provided. The device includes a plurality of electrodes to acquire electrocardiogram (ECG) signals from the patient, at least one motion sensor configured to generate a motion signal based upon movement of the patient, and at least one processor. The processor is configured derive motion parameters from the motion signal, derive ECG parameters from the ECG signals, determine whether the patient is in an immobilized sleep stage or a non-immobilized sleep stage based upon the motion parameters and the ECG parameters, adjust one or more cardiac arrhythmia detection parameters such that the device operates in a first monitoring and treatment mode when the patient is in an immobilized sleep stage, and monitor the patient for the cardiac arrhythmia using the first monitoring and treatment mode.

A system is provided that includes a first electrode configured to be located within a septal wall, and a second electrode configured to be located outside of the septal wall. The system also includes an impedance circuit configured to measure impedance along an impedance monitoring (IM) vector between the first and second electrodes. One or more processors are also provided that are configured to obtain impedance data indicative of an impedance along the IM vector with the first electrode located at different depths within the septal wall, the impedance data including a set of data values associated with different depths of the first electrode within the septal wall. The one or more processors are also configured to determine when the first electrode is located at a target depth within the septal wall based on the impedance data.

Techniques are disclosed for using feature delineation to reduce the impact of machine learning cardiac arrhythmia detection on power consumption of medical devices. In one example, a medical device performs feature-based delineation of cardiac electrogram data sensed from a patient to obtain cardiac features indicative of an episode of arrhythmia in the patient. The medical device determines whether the cardiac features satisfy threshold criteria for application of a machine learning model for verifying the feature-based delineation of the cardiac electrogram data. In response to determining that the cardiac features satisfy the threshold criteria, the medical device applies the machine learning model to the sensed cardiac electrogram data to verify that the episode of arrhythmia has occurred or determine a classification of the episode of arrhythmia.

Systems and methods for managing heart failure are described. The system receives physiological information including a first HS signal corresponding to paced ventricular contractions and a second HS signal corresponding to intrinsic ventricular contractions. The system detects worsening heart failure (WHF) using the received physiological information. A signal analyzer circuit can generate a paced HS metric from the first HS signal and a sensed HS metric from the second HS signal, and determine a concordance indicator between the paced and the sensed HS metrics. In response to the detected WHF, the system can use the concordance indicator to generate a therapy adjustment indicator for adjusting electrostimulation therapy, or a worsening cardiac contractility indicator indicating the detected WHF is attributed to degrading myocardial contractility.

A system and a method include an implantable medical device (IMD) having one or more inputs configured to receive one or more sensed signals from one or more electrodes. A plurality of filters are configured to filter the one or more sensed signals and output a plurality of filtered signals. Memory is configured to store program instructions. A processor, when executing the program instructions, is configured to receive the plurality of filtered signals, and analyze the plurality of filtered signals to determine a desired one of the plurality of filters.

Implantable devices and systems include one or more leads adapted to be emplaced in the internal thoracic vein (ITV) of a patient. The lead may include features to adapt the lead for such placement. An associated device for use with the lead may include operational circuitry adapted for use with a lead having an electrode for sensing and/or therapy purposes coupled thereto. Methods for implantation and use of such devices and systems are disclosed as well.

A computer implemented method and system are provided for detecting premature ventricular contractions (PVCs) in cardiac activity. The method and system obtain cardiac activity (CA) signals for a series of beats, and, for at least a portion of the series of beats, calculate QRS scores for corresponding QRS complex segments from the CA signals. The method and system calculate a variability metric for QRS scores across the series of beats, calculate a QRS complex template using QRS segments from the series of beats, calculate correlation coefficients between the QRS complex template and the QRS complex segments, compare the variability metric to a variability threshold and the correlation coefficients to a correlation threshold, and designate the CA signals to include a predetermined level of PVC burden based on the determining.

A medical having a motion sensor is configured to set an atrial event sensing parameter used for sensing atrial event signals from a motion signal produced by the motion sensor. The medical device sets an atrial event sensing parameter by applying a sensing window during each one of multiple ventricular cycles, determining a feature of the motion signal during the sensing window for at least a portion of the ventricular cycles, and setting the atrial event sensing parameter based on the determined features. The medical device may sense the atrial event from the motion signal according to the atrial event sensing parameter.

Systems and methods for monitoring physiologic response to Valsalva maneuver (VM) are disclosed. An exemplary patient monitor may detect a natural incidence of a VM session occurred in an ambulatory setting using a heart sound (HS) signal sensed from the patient. The patient monitor may include a physiologic response analyzer to sense patient physiologic response during the detected VM session, and generate a cardiovascular or autonomic function indicator based on the sensed physiologic response to the VM. Using the physiologic response to the VM, the system may detect a target physiologic event using the sensed physiologic response to the VM.

A system includes a pulse generator including a can electrode and a lead couplable to the pulse generator, the lead including a distal coil electrode and a proximal coil electrode, wherein both of the coil electrodes are electrically uncoupled from the can electrode such that a unipolar sensing vector is provided between at least one of the coil electrodes and the can electrode.