Abstract: Various systems and methods for implementing context-based digital signal processing are described herein. An object detection system includes a processor to: access sensor data from a first sensor and a second sensor integrated in a vehicle; access an operating context of the vehicle; assign a first weight to a first object detection result from sensor data of the first sensor, the first weight adjusted based on the operating context; assign a second weight to a second object detection result from sensor data of the second sensor, the second weight adjusted based on the operating context; and perform a combined object detection technique by combining the first object detection result weighted by the first weight and the second object detection result weighted by the second weight.

Abstract: Provided is a method, system and/or apparatus for determining prospective heart failure event risk. Acquired from a device memory are a heart failure patient's current and preceding risk assessment periods. Counting detected data observations in the current risk assessment period for a current risk assessment total amount and counting detected data observations in the preceding risk assessment period for a preceding risk assessment period total amount. Associating the current risk assessment and preceding risk assessment total amounts with a lookup table to acquire prospective risk of heart failure (HF) event for the preceding risk assessment period and the current risk assessment period. Employing weighted sums of the prospective risk of the HF event for the preceding risk assessment period and the current risk assessment period to calculate a weighted prospective risk of the HF event for a patient. Displaying on a graphical user interface the weighted prospective risk of the HF event for the patient.

Abstract: A method and device apparatus to deliver a pacing therapy capable of remodeling a patient's heart over a period of time that includes monitoring one or more parameters in response to a delivered cardiac remodeling pacing, determining whether the cardiac remodeling pacing has an effect on cardiac normalization in response to the monitoring, and adjusting the cardiac remodeling pacing in response to the determined effect on cardiac normalization. The method and device may also perform short-term monitoring of one or more parameters in response to the delivered cardiac remodeling pacing, monitor one or more long-term parameter indicative of a long-term effect of the delivered cardiac remodeling pacing, determine the long-term effect of the delivered cardiac remodeling pacing on cardiac normalization in response to the monitoring, and adjust the cardiac remodeling pacing in response to one or both of the short-term monitoring and the determined long-term effect on cardiac normalization.

Abstract: A method and device apparatus to deliver a pacing therapy capable of remodeling a patient's heart over a period of time that includes delivering remodeling pacing during a first interval, the first interval comprising a first rate and a first duration, determining whether to adjust one or both of the first rate and the first duration during delivery of remodeling pacing during a next interval subsequent to the first interval, and delivering remodeling pacing during the next interval in response to the determining, wherein the next interval comprises one of the first rate and the first duration of the first interval and the adjusted one or both of the first rate and the first duration.

Abstract: Machine vision processing includes capturing 3D spatial data representing a field of view and including ranging measurements to various points within the field of view, applying a segmentation algorithm to the 3D spatial data to produce a segmentation assessment indicating a presence of individual objects within the field of view, wherein the segmentation algorithm is based on at least one adjustable parameter, and adjusting a value of the at least one adjustable parameter based on the ranging measurements. The segmentation assessment is based on application of the segmentation algorithm to the 3D spatial data, with different values of the at least one adjustable parameter value corresponding to different values of the ranging measurements of the various points within the field of view.

Abstract: A health care system acquires data determines whether a patient is at risk of hypervolemia or hypovolemia. The method comprises (a) acquiring from a device memory a patient's absolute intrathoracic impedance data over a pre-specified time period, (b) determining a running average of the intrathoracic impedance data over the pre-specified time period, and (c) determining by the system whether the running average of the intrathoracic impedance data over the pre-specified time period exceeds one of a first and second range, the first range being a higher value boundary of intrathoracic electrical impedance and the second range being a lower value boundary of intrathoracic electrical impedance.

Abstract: In some examples, determining a heart failure status includes using an implantable medical device configured for subcutaneous implantation and comprising a plurality of electrodes and an optical sensor. Processing circuitry of a system comprising the device may determine, for a patient, a current tissue oxygen saturation value based on a signal received from the at least one optical sensor, a current tissue impedance value based on a subcutaneous tissue impedance signal received from the electrodes, and a current pulse transit time value based on a cardiac electrogram signal received from the electrodes and at least one of the signal received from the optical sensor and the subcutaneous tissue impedance signal. The processing circuitry may further compare the current tissue oxygen saturation value, current tissue impedance value, and current pulse transit time value to corresponding baseline values, and determine the heart failure status of the patient based on the comparison.

Abstract: Methods and systems for seamless adjustment of treatment are disclosed. A determination can be made as to whether to intervene with a patient's treatment based on data obtained from implantable electrodes and/or non-implantable electrodes. The data from non-implantable electrodes have a correction factor applied to adjust for less accuracy compared to data acquired from implantable electrodes.

Abstract: In situations in which an implantable medical device (IMD) (e.g., an extravascular ICD) is co-implanted with a leadless pacing device (LPD), it may be important that the IMD knows when the LPD is delivering pacing, such as anti-tachycardia pacing (ATP). Techniques are described herein for detecting, with the IMD and based on the sensed electrical signal, pacing pulses and adjusting operation to account for the detected pulses, e.g., blanking the sensed electrical signal or modifying a tachyarrhythmia detection algorithm. In one example, the IMD includes a pace pulse detector that detects, based on the processing of sensed electrical signals, delivery of a pacing pulse from a second implantable medical device and blank, based on the detection of the pacing pulse, the sensed electrical signal to remove the pacing pulse from the sensed electrical signal.

Abstract: A health care system acquires data determines whether a patient is at risk of hypervolemia or hypovolemia. The method comprises (a) acquiring from a device memory a patient's absolute intrathoracic impedance data over a pre-specified time period, (b) determining a running average of the intrathoracic impedance data over the pre-specified time period, and (c) determining by the system whether the running average of the intrathoracic impedance data over the pre-specified time period exceeds one of a first and second range, the first range being a higher value boundary of intrathoracic electrical impedance and the second range being a lower value boundary of intrathoracic electrical impedance.

Abstract: In situations in which an implantable medical device (e.g., a subcutaneous ICD) is co-implanted with a leadless pacing device (LPD), it may be important that the subcutaneous ICD knows when the LPD is delivering pacing, such as anti-tachycardia pacing (ATP). Techniques are described herein for detecting, with the ICD and based on the sensed electrical signal, pacing pulses and adjusting operation to account for the detected pulses, e.g., blanking the sensed electrical signal or modifying a tachyarrhythmia detection algorithm. In one example, the ICD includes a first pace pulse detector configured to obtain a sensed electrical signal and analyze the sensed electrical signal to detect a first type of pulses having a first set of characteristics and a second pace pulse detector configured to obtain the sensed electrical signal and analyze the sensed electrical signal to detect a second type of pulses having a second set of characteristics.

Abstract: A method and device for differentiating heart failure risk scores that includes determining receipt of a current data transmission and acquiring patient metrics from a remote monitoring device, determining a daily heart failure risk score for each day occurring during a time period from a previous received data transmission to the current received data transmission based on the acquired patient metrics, and determining a maximum daily heart failure risk score of the determined daily heart failure risk scores during a lookback window prior to the current received data transmission. A heart failure risk score is determined for the received data transmission based on the determined maximum daily heart failure risk score, and a heart failure risk score alert is determined for the received data transmission based on the proximity of the determined maximum heart failure risk score and the current received data transmission.

Abstract: An implantable medical device comprises a sensing module configured to obtain electrical signals from one or more electrodes and a control module configured to process the electrical signals from the sensing module in accordance with a tachyarrhythmia detection algorithm to monitor for a tachyarrhythmia. The control module detects initiation of a pacing train delivered by a second implantable medical device, determines a type of the detected pacing train, and modifies the tachyarrhythmia detection algorithm based on the type of the detected pacing train.

Abstract: A method of operation of a medical device system for determining prospective heart failure hospitalization risk. The method includes measuring one or more data observations via one or more electrodes of an implanted medical device disposed in a patient's body. The data observations are stored into memory of the implantable medical device of a patient. The data observations are transmitted to an external device. The processor of the external device parses the data observations into one or more evaluation periods. Using the number of observations in one or more evaluation periods, a look up table, stored into memory of the external device, is accessed. The look up table associates prospective heart failure hospitalization risk with the data observations noted in the evaluation period. One or more embodiments involve a weighted prospective heart failure hospitalization risk for the set of evaluation periods. The prospective heart failure hospitalization is then displayed on the graphical user interface.

Abstract: In situations in which an implantable medical device (e.g., a subcutaneous ICD) is co-implanted with a leadless pacing device (LPD), it may be important that the subcutaneous ICD knows when the LPD is delivering pacing, such as anti-tachycardia pacing (ATP). Techniques are described herein for detecting, with the ICD and based on the sensed electrical signal, pacing pulses and adjusting operation to account for the detected pulses, e.g., blanking the sensed electrical signal or modifying a tachyarrhythmia detection algorithm. In one example, the ICD includes a first pace pulse detector configured to obtain a sensed electrical signal and analyze the sensed electrical signal to detect a first type of pulses having a first set of characteristics and a second pace pulse detector configured to obtain the sensed electrical signal and analyze the sensed electrical signal to detect a second type of pulses having a second set of characteristics.

Abstract: A method for differentiating heart failure risk scores that includes receiving a current data transmission and acquiring patient metrics from a remote device, determining a daily heart failure risk score for each day occurring during a time period from a previous received data transmission to the current received data transmission based on the acquired patient metrics, determining a maximum daily heart failure risk score of the determined daily heart failure risk scores during a lookback window prior to the current received data transmission, determining a heart failure risk status alert for the received data transmission based on the temporal proximity of the determined maximum heart failure risk score and receipt of the current data transmission, selecting a type of notification based on the heart failure risk status differentiation, and indicating the transmission heart failure risk status and the heart failure risk status differentiation via the selected type of notification.

Abstract: Systems and techniques for neural network training are described herein. A training set may be received for a neural network. Here, the neural network may comprise a set of nodes arranged in layers and a set of inter-node weights between nodes in the set of nodes. The neural network may then be iteratively trained to create a trained neural network. An iteration of the training may include generating a random unit vector, creating an update vector by calculating a magnitude for the random unit vector based on a degree that the random unit vector matches a gradient—where the gradient represented by a dual number, and updating a parameter vector for an inter-node weight by subtracting the update vector from a previous parameter vector of the inter-node weight. The trained neural network may then be used to classify data.

Abstract: Machine vision processing includes capturing 3D spatial data representing a field of view and including ranging measurements to various points within the field of view, applying a segmentation algorithm to the 3D spatial data to produce a segmentation assessment indicating a presence of individual objects within the field of view, wherein the segmentation algorithm is based on at least one adjustable parameter, and adjusting a value of the at least one adjustable parameter based on the ranging measurements. The segmentation assessment is based on application of the segmentation algorithm to the 3D spatial data, with different values of the at least one adjustable parameter value corresponding to different values of the ranging measurements of the various points within the field of view.

Abstract: Various systems and methods for implementing context-based digital signal processing are described herein. An object detection system includes a processor to: access sensor data from a first sensor and a second sensor integrated in a vehicle; access an operating context of the vehicle; assign a first weight to a first object detection result from sensor data of the first sensor, the first weight adjusted based on the operating context; assign a second weight to a second object detection result from sensor data of the second sensor, the second weight adjusted based on the operating context; and perform a combined object detection technique by combining the first object detection result weighted by the first weight and the second object detection result weighted by the second weight.

Abstract: The exemplary systems and methods may monitor one or more signals to be used to assess the hemodynamic status of a patient. The one or more signals may be used to calculate, or determine, a plurality of pulse transit times. The plurality of pulse transit times may be used to determine hemodynamic status values that may be indicative of a patient's aggregate hemodynamic status.