Abstract: Aspects of the present disclosure are directed to detecting Atrial Fibrillation (AF). As may be implemented in accordance with one or more embodiments, a time series of inter-beat intervals is computed from a recording of activity of a beating heart. The time series is decomposed into subcomponents, and an envelope of at least one of the subcomponents is computed. The presence of atrial fibrillation (AF) is detected based upon characteristics of the envelope that are indicative of AF.

Abstract: Physiological signals are denoised. In accordance with an example embodiment, a denoised physiological signal is generated from an input signal including a desired physiological signal and noise. The input signal is decomposed from a first domain into subcomponents in a second domain of higher dimension than the first domain. Target subcomponents of the input signal that are associated with the desired physiological signal are identified, based upon the spatial distribution of the subcomponents. A denoised physiological signal is constructed in the first domain from at least one of the identified target subcomponents.

Abstract: Various aspects are directed to identifying a region of interest in a physiological signal. As may be consistent with one or more embodiments, the physiological signal is decomposed into subcomponents, and a subset of the subcomponents is selected based upon overlap of spectral energy with expected spectral energy of the region of interest, in at least one of the subcomponents. At least two of the subcomponents in the subset are combined and compared to a threshold, with the comparison being used to identify the location of the region of interest.

Abstract: A cardiac-based metric is computed based upon characteristics of a subject's cardiac function. In accordance with one or more embodiments, the end of a mechanical systole is identified for each of a plurality of cardiac cycles of a subject, based upon an acoustical vibration associated with closure of an aortic valve during the cardiac cycle. The end of an electrical systole of an electrocardiogram (ECG) signal for each cardiac cycle is also identified. A cardiac-based metric is computed, based upon a time difference between the end of the electrical systole and the end of the mechanical systole, for the respective cardiac cycles.

Abstract: Aspects of the present disclosure are directed to detecting Atrial Fibrillation (AF). As may be implemented in accordance with one or more embodiments, a time series of inter-beat intervals is computed from a recording of activity of a beating heart. The time series is decomposed into subcomponents, and an envelope of at least one of the subcomponents is computed. The presence of atrial fibrillation (AF) is detected based upon characteristics of the envelope that are indicative of AF.

Abstract: A T-wave offset point of an ECG signal is provided. In accordance with various example embodiments, a location of a QRS complex in the ECG signal is identified and used to determine a first time window of the ECG signal in which to search for a T-wave offset point. The T-wave offset point is identified within the first time window, and the identified T-wave offset point is provided as an output based upon a noise characteristic of the ECG signal in a second time window that includes at least a portion of the T-wave.

Abstract: A cardiac-based metric is computed based upon characteristics of a subject's cardiac function. In accordance with one or more embodiments, the end of a mechanical systole is identified for each of a plurality of cardiac cycles of a subject, based upon an acoustical vibration associated with closure of an aortic valve during the cardiac cycle. The end of an electrical systole of an electrocardiogram (ECG) signal for each cardiac cycle is also identified. A cardiac-based metric is computed, based upon a time difference between the end of the electrical systole and the end of the mechanical systole, for the respective cardiac cycles.

Abstract: Aspects of the present disclosure are directed to processing ECG signals from a subject. As may be implemented in accordance with one or more embodiments, respective electrodes sense ECG signals from a subject, and the ECG signals are digitized and processed, such as to remove noise, detect a QRS complex, evaluate quality, detect arrhythmia and/or to store the signals. In response to an input from the subject, one or more of the ECG signals is recorded along with sound from the user, such as to concurrently record the user's voice for describing conditions in connection with the recording of the ECG signals. This approach can be carried out in an enclosed housing, operated adjacent the subject's thorax. The processed digitized ECG signals and the audio signals are then communicated for receipt by an external device.

Abstract: Implantable medical devices and techniques are implemented that use bio-impedance to measure aspects of patient physiology. A signal separation method is performed at least in part in an implantable device. The method involves detecting a plurality of impedance signals using a plurality of implantable electrodes coupled to the implantable device. The method further involves separating one or more signals from the plurality of impedance signals using a signal separation technique, such as an algorithm-based separation technique.

Abstract: A cardiac-based metric is computed based upon characteristics of a subject's cardiac function. In accordance with one or more embodiments, the end of a mechanical systole is identified for each of a plurality of cardiac cycles of a subject, based upon an acoustical vibration associated with closure of an aortic valve during the cardiac cycle. The end of an electrical systole of an electrocardiogram (ECG) signal for each cardiac cycle is also identified. A cardiac-based metric is computed, based upon a time difference between the end of the electrical systole and the end of the mechanical systole, for the respective cardiac cycles.

Abstract: Cardiac systems and methods using ECG and blood information for arrhythmia detection and discrimination. Detection circuitry is configured to produce an ECG. An implantable blood sensor configured to produce a blood sensor signal is coupled to a processor. The processor is coupled to the detection and energy delivery circuitry, and used to evaluate and treat cardiac rhythms using both the cardiac electrophysiologic and blood sensor signals. The blood sensor is configured for subcutaneous non-intrathoracic placement and provided in or on the housing, on a lead coupled to the housing, and/or separate to the housing and coupled to the processor via hardwire or wireless link. The blood sensor may be configured for optical sensing, using a blood oxygen saturation sensor or pulse oximeter. A cardiac rhythm may be evaluated using the electrocardiogram signal and the blood sensor signal, and tachyarrhythmias may be treated after confirmation using the blood sense signal.

Abstract: A cardiac-based metric is computed based upon characteristics of a subject's cardiac function. In accordance with one or more embodiments, the end of a mechanical systole is identified for each of a plurality of cardiac cycles of a subject, based upon an acoustical vibration associated with closure of an aortic valve during the cardiac cycle. The end of an electrical systole of an electrocardiogram (ECG) signal for each cardiac cycle is also identified. A cardiac-based metric is computed, based upon a time difference between the end of the electrical systole and the end of the mechanical systole, for the respective cardiac cycles.

Abstract: An evaluation of heart failure status is provided based on a disordered breathing index. Patient respiration is sensed and a respiration signal is generated. Disordered breathing episodes are detected based on the respiration signal. A disordered breathing index is determined based on the disordered breathing episodes. The disordered breathing index is trended and used to evaluate heart failure status. The disordered breathing index may be combined with additional information and/or may take into account patient activity, posture, sleep stage, or other patient information.

Abstract: A system and related method for identifying a trigger event to a patient health-related exacerbation. The system includes at least one sensor configured to collect data related to the patient health-related exacerbation, an analyzer configured to validate the collected data, a patient interface device configured to receive patient inputs to at least one question, and an identifier device configured to receive the patient inputs and collected data and identify the trigger event. The system may also include an expert system configured to receive the patient inputs and the validated collected data and identify a primary patient disease, and a reporting device configured to generate reports related to the identified primary patient disease, patient inputs, and sensed patient conditions.

Abstract: Physiological signals are denoised. In accordance with an example embodiment, a denoised physiological signal is generated from an input signal including a desired physiological signal and noise. The input signal is decomposed from a first domain into subcomponents in a second domain of higher dimension than the first domain. Target subcomponents of the input signal that are associated with the desired physiological signal are identified, based upon the spatial distribution of the subcomponents. A denoised physiological signal is constructed in the first domain from at least one of the identified target subcomponents.

Abstract: An evaluation of heart failure status is provided based on a disordered breathing index. Patient respiration is sensed and a respiration signal is generated. Disordered breathing episodes are detected based on the respiration signal. A disordered breathing index is determined based on the disordered breathing episodes. The disordered breathing index is trended and used to evaluate heart failure status. The disordered breathing index may be combined with additional information and/or may take into account patient activity, posture, sleep stage, or other patient information.

Abstract: A cardiac-based metric is computed based upon characteristics of a subject's cardiac function. In accordance with one or more embodiments, the end of a mechanical systole is identified for each of a plurality of cardiac cycles of a subject, based upon an acoustical vibration associated with closure of an aortic valve during the cardiac cycle. The end of an electrical systole of an electrocardiogram (ECG) signal for each cardiac cycle is also identified. A cardiac-based metric is computed, based upon a time difference between the end of the electrical systole and the end of the mechanical systole, for the respective cardiac cycles.

Abstract: Physiological signals are denoised. In accordance with an example embodiment, a denoised physiological signal is generated from an input signal including a desired physiological signal and noise. The input signal is decomposed from a first domain into subcomponents in a second domain of higher dimension than the first domain. Target subcomponents of the input signal that are associated with the desired physiological signal are identified, based upon the spatial distribution of the subcomponents. A denoised physiological signal is constructed in the first domain from at least one of the identified target subcomponents.

Abstract: Cardiac monitoring and/or stimulation methods and systems that provide one or more of monitoring, diagnosing, defibrillation, and pacing. Cardiac signal separation is employed to detect, monitor, track and/or trend ischemia using cardiac activation sequence information. Ischemia detection may involve sensing composite cardiac signals using implantable electrodes, and performing a signal separation that produces one or more cardiac activation signal vectors associated with one or more cardiac activation sequences. A change in the signal vector may be detected using subsequent separations. The change may be an elevation or depression of the ST segment of a cardiac cycle or other change indicative of myocardial ischemia, myocardial infarction, or other pathological change. The change may be used to predict, quantify, and/or qualify an event such as an arrhythmia, a myocardial infarction, or other pathologic change. Information associated with the vectors may be stored and used to track the vectors.

Abstract: Physiological signals are denoised. In accordance with an example embodiment, a denoised physiological signal is generated from an input signal including a desired physiological signal and noise. The input signal is decomposed from a first domain into subcomponents in a second domain of higher dimension than the first domain. Target subcomponents of the input signal that are associated with the desired physiological signal are identified, based upon the spatial distribution of the subcomponents. A denoised physiological signal is constructed in the first domain from at least one of the identified target subcomponents.