Abstract:
A sleep quality assessment approach involves collecting data based on detected physiological or non-physiological patient conditions. At least one of detecting patient conditions and collecting data is performed using an implantable device. Sleep quality may be evaluated using the collected data by an implantable or patient-external sleep quality processor. One approach to sleep quality evaluation involves computing one or more summary metrics based on occurrences of movement disorders or breathing disorders during sleep.
Abstract:
The health state of a subject is automatically evaluated or predicted using at least one implantable device. In varying examples, the health state is determined by sensing or receiving information about at least one physiological process having a circadian rhythm whose presence, absence, or baseline change is associated with impending disease, and comparing such rhythm to baseline circadian rhythm prediction criteria. Other chronobiological rhythms beside circadian may also be used. The baseline prediction criteria may be derived using one or more past physiological process observation of the subject or population of subjects in a non-disease health state. The prediction processing may be performed by the at least one implantable device or by an external device in communication with the implantable device. Systems and methods for invoking a therapy in response to the health state, such as to prevent or minimize the consequences of predicted impending heart failure, are also discussed.
Abstract:
Devices and methods for detecting disordered breathing involve determining that the patient is asleep and sensing one or more signals associated with disordered breathing indicative of sleep-disordered breathing while the patient is asleep. Sleep-disordered breathing is detected using the sensed signals associated with disordered breathing. The sensed signals associated with disordered breathing may also be used to acquire a respiration pattern of one or more respiration cycles. Characteristics of the respiration pattern are determined. The respiration pattern is classified as a disordered breathing episode based on the characteristics of the respiration pattern. One or more processes involved in the detection of disordered breathing are performed using an implantable device.
Abstract:
Systems and methods involve use of a medical device comprising sensing circuitry. One or more respiratory parameters are detected using the device. Patient baseline weight is provided, and an output signal indicative of a patient's congestive heart failure status is generated based on a change in the one or more respiratory parameters and a change in the patient's measured weight or predicted weight relative to the patient baseline weight. The respiratory parameters may include one or more of respiration rate, relative tidal volume, an index indicative of rapid shallow breathing by the patient, an index derived by computing a respiration rate and a tidal volume for each patient breath, and an index indicative of dyspnea, for example.
Abstract:
Patient respiration may be characterized using a marked respiration waveform involving a respiration waveform annotated with symbols, markers or other indicators representing one or more respiration characteristics. A respiration waveform may be acquired by sensing a physiological parameter modulated by respiration. A marked respiration waveform may be generated based on the acquired respiration waveform and one or more detected respiration waveform characteristics and/or respiration-related conditions. One or more components used to generate the marked respiratory waveform may be fully or partially implantable.
Abstract:
A device and method can monitor or trend a patient's respiration rate measurements according to the time of day. The device, which may be implantable or external, collects and classifies respiration rate measurements over time. The trended information about particular classes of respiration rate measurements is then communicated to a remote external device, which in turn provides an indication of heart failure decompensation. Examples of classes of respiration rate measurements include a daily maximum respiration rate value, a daily minimum respiration rate value, a daily maximum respiration rate variability value, a daily minimum respiration rate variability value, and a daily central respiration rate value. These respiration rate measurements can be further classified into daytime or nighttime respiration rate measurements.
Abstract:
An apparatus comprises plurality of sensors and a processor. Each sensor provides a sensor signal that includes physiological information and at least one sensor is implantable. The processor includes a physiological change event detection module that detects a physiological change event from a sensor signal and produces an indication of occurrence of one or more detected physiological change events, and a heart failure (HF) detection module. The HF detection module determines, using a first rule, whether the detected physiological change event is indicative of a change in HF status of a subject, determines whether to override the first rule HF determination using a second rules, and declares whether the change in HF status occurred according to the first and second rules.
Abstract:
This document discusses, among other things, systems and methods for measuring the dynamics of pulmonary congestion in heart failure subjects over time to monitor the subjects susceptibility to pulmonary edema, including sensing and receiving information indicative of a bodily pressure and information indicative of pulmonary fluid, and using the transient responses of these measurements to compute parameters related to the dynamics of thoracic fluid accumulation, such as a critical pressure (Pc), a critical time (Tc), or a filtration index (Kfi).
Abstract:
An approach to collecting and organizing information associated with events affecting sleep is presented. The sleep logbook system may acquire information associated with the sleep during periods of sleep and/or during periods of wakefulness. The information is organized as a sleep logbook entry. The user can access the sleep information by operating a user interface. The information may be presented in textual or graphical form.
Abstract:
A device and method can monitor or trend a patient's respiration rate measurements according to the time of day. The device, which may be implantable or external, collects and classifies respiration rate measurements over time. The trended information about particular classes of respiration rate measurements is then communicated to a remote external device, which in turn provides an indication of heart failure decompensation. Examples of classes of respiration rate measurements include a daily maximum respiration rate value, a daily minimum respiration rate value, a daily maximum respiration rate variability value, a daily minimum respiration rate variability value, and a daily central respiration rate value. These respiration rate measurements can be further classified into daytime or nighttime respiration rate measurements.