An apparatus including circuitry configured to determine a probability by combining at least: a probability that an event is present within a current feature of interest given a first set of previous features of interest, and a probability that the event is present within the current feature of interest given a second set of previous features of interest, different to the first set of previous features of interest; circuitry configured to detect the event based on the determined probability; and circuitry configured to control, in dependence on the detection of the event, performance of an action.

The invention provides a system and method for measuring vital signs (e.g. SYS, DIA, SpO2, heart rate, and respiratory rate) and motion (e.g. activity level, posture, degree of motion, and arm height) from a patient. The system features: (i) first and second sensors configured to independently generate time-dependent waveforms indicative of one or more contractile properties of the patient's heart; and (ii) at least three motion-detecting sensors positioned on the forearm, upper arm, and a body location other than the forearm or upper arm of the patient. Each motion-detecting sensor generates at least one time-dependent motion waveform indicative of motion of the location on the patient's body to which it is affixed. A processing component, typically worn on the patient's body and featuring a microprocessor, receives the time-dependent waveforms generated by the different sensors and processes them to determine: (i) a pulse transit time calculated using a time difference between features in two separate time-dependent waveforms, (ii) a blood pressure value calculated from the time difference, and (iii) a motion parameter calculated from at least one motion waveform.

The invention provides a system and method for measuring vital signs (e.g. SYS, DIA, SpO2, heart rate, and respiratory rate) and motion (e.g. activity level, posture, degree of motion, and arm height) from a patient. The system features: (i) first and second sensors configured to independently generate time-dependent waveforms indicative of one or more contractile properties of the patient's heart; and (ii) at least three motion-detecting sensors positioned on the forearm, upper arm, and a body location other than the forearm or upper arm of the patient. Each motion-detecting sensor generates at least one time-dependent motion waveform indicative of motion of the location on the patient's body to which it is affixed. A processing component, typically worn on the patient's body and featuring a microprocessor, receives the time-dependent waveforms generated by the different sensors and processes them to determine: (i) a pulse transit time calculated using a time difference between features in two separate time-dependent waveforms, (ii) a blood pressure value calculated from the time difference, and (iii) a motion parameter calculated from at least one motion waveform.

Methods, systems, and devices for signal analysis in an implanted cardiac monitoring and treatment device such as an implantable cardioverter defibrillator. In some examples, captured data including detected events is analyzed to identify likely overdetection of cardiac events. In some illustrative examples, when overdetection is identified, data may be modified to correct for overdetection, to reduce the impact of overdetection, or to ignore overdetected data. Several examples emphasize the use of morphology analysis using correlation to static templates and/or inter-event correlation analysis.

A sleep monitor for monitoring baby sleep uses sleep state classification based on heartbeat feature respiration features. The sleep monitor automatically retrains the classification during use of the sleep monitor. Training examples for use in this training process are generated automatically by detecting time instants whereat the baby in the bed is in a wake state, based on signals from the at least one of a sound feature detector a movement feature detector (112) and an open eye detector (114). The retraining may comprise using time sequence from the end of detection of wake states to assign a class to heartbeat feature and/or respiration feature values during that time sequence for the training process. In an embodiment, the retraining comprises clustering detected heartbeat feature and/or respiration feature values detected outside the detected wake states.

The present disclosure generally relates to systems and methods and systems of a noninvasive technique for characterizing cardiac chamber size and cardiac mechanical function. A mathematical analysis of three-dimensional (3D) high resolution data may be used to estimate chamber size and cardiac mechanical function. For example, high-resolution mammalian signals are analyzed across multiple leads, as 3D orthogonal (X,Y,Z), or 10-channel data, for 30 to 800 seconds, to derive estimates of cardiac chamber size and cardiac mechanical function. Multiple mathematical approaches may be used to analyze the dynamical and geometrical properties of the data.

An arrangement may include a first system provided for processing physiological data representative of a beating heart. The first system may be adapted to execute a process for using at least one pattern to detect a notable finding in the physiological data and for sending the notable finding to a second system. The second system may be adapted to execute a process for analyzing the notable finding, for determining at least one new pattern to send to the first system, and for sending the at least one new pattern to the first system. The at least one new pattern may also include a rule that includes a set of conditions and an action to perform if the set of conditions is met.

Cardiac electrograms are recorded in a plurality of channels. Beats are classified automatically into respective classifications according to a resemblance of the morphologic characteristics of the beats to members of a set of templates. Respective electroanatomic maps of the heart are generated from the classified beats.

Embodiments of the present disclosure relate to systems and methods for determining a subject's blood pressure using one or more implantable medical devices (IMDs). In an embodiment, a medical system comprises: at least one implantable medical device configured to sense signals associated with heart sounds of a subject and a processing unit communicatively coupled to the at least one implantable medical device. The processing unit is configured to: receive heart sound signals corresponding to the signals associated with the heart sounds; and calculate a surrogate of the subject's blood pressure using at least one heart sound signal of the received heart sound signals.

Systems and methods for assessing sympathetic nervous system (SNS) tone for renal neuromodulation therapy are disclosed herein. A system configured in accordance with embodiments of the present technology can include, for example, a detector attached to or implanted in a patient and a receiver communicatively coupled to the detector. The detector can measure cardiac data and the receiver and/or a device communicatively coupled thereto can analyze the cardiac data to provide one or more SNS tone indicators. The SNS tone indicators can be used to determine whether a patient will be responsive to a neuromodulation therapy and/or whether a neuromodulation therapy was effective.