Abstract: The temporal correlation between a bioelectrical brain signal of a patient and patient motion data, such as a signal indicative of patient motion or a patient posture indicator, is displayed by a display device. In some examples, the patient posture indicator comprises a graphical representation of at least a portion of a body of the patient. In some examples, the temporal correlation between a bioelectrical brain signal, a signal indicative of patient motion, and a signal indicative of cardiac activity of the patient is displayed by the display device.

Abstract: This disclosure describes a capacitive interface circuit for a low power system. The capacitive interface circuit is configured to achieve very low noise sensing of capacitance-based transducers, such as a micro-electro-mechanical system (MEMS)-based sensor, with high resolution and low power. The capacitive interface circuit uses a differential amplifier and correlated triple sampling (CTS) to substantially eliminate, or at least reduce, kT/C noise, as well as amplifier offset and flicker (1/f) noise, from the output of the amplifier. The capacitive interface circuit may further include an output stage that reduces glitching, i.e., clock transients, in the output signal by allowing transients in the amplifier output to settle. In this manner, the circuit can be used in a low power system to produce a stable, low-noise output.

Abstract: Methods of delivering optical stimulation to a target tissue from an optical stimulation device are provided. One method comprises sensing a temperature at the optical stimulation device or proximate to the optical stimulation device, and adjusting the delivery of light to the target tissue based on the sensed temperature. Another method comprises delivering the light to the target tissue with an optical light guide and sensing bioelectric signals with a sense electrode, wherein the optical light guide and the sense electrode each comprise a material that produces substantially no induced current in an electromagnetic field. Another method comprises delivering light from a light source of an optical stimulation device to a window of the optical stimulation device, delivering the light from the window to an optical light guide optically connected to the window, and delivering the light to a target tissue via the optical light guide.

Abstract: A method for delivering optical stimulation comprises transfecting a target tissue with a light-sensitive channel protein sensitive to light in a wavelength range, delivering light in the wavelength range to the target tissue via an optical stimulation device, substantially simultaneously with delivering light to the target tissue, sensing bioelectric signals, determining a patient therapeutic state based on the bioelectric signals, and adjusting the delivery of the light to the target tissue based on the sensed patient therapeutic state.

Abstract: This disclosure describes a chopper stabilized instrumentation amplifier. The amplifier is configured to achieve stable measurements at low frequency with very low power consumption. The instrumentation amplifier uses a differential architecture and a mixer amplifier to substantially eliminate noise and offset from an output signal produced by the amplifier. Dynamic limitations, i.e., glitching, that result from chopper stabilization at low power are substantially eliminated through a combination of chopping at low impedance nodes within the mixer amplifier and feedback. The signal path of the amplifier operates as a continuous time system, providing minimal aliasing of noise or external signals entering the signal pathway at the chop frequency or its harmonics. The amplifier can be used in a low power system, such as an implantable medical device, to provide a stable, low-noise output signal.

Abstract: In general, this disclosure is directed to a mixer amplifier that can be utilized within a chopper stabilized instrumentation amplifier. The chopper stabilized instrumentation amplifier may be used for physiological signal sensing, impedance sensing, telemetry or other test and measurement applications. In some examples, the mixer amplifier may include a current source configured to generate a modulated current at a modulation frequency for application to a load to produce an input signal, an amplifier configured to amplify the input signal to produce an amplified signal, and a demodulator configured to demodulate the amplified signal at the modulation frequency to produce an output signal indicating an impedance of the load.

Abstract: This disclosure describes a chopper stabilized instrumentation amplifier. The amplifier is configured to achieve stable measurements at low frequency with very low power consumption. The instrumentation amplifier uses a differential architecture and a mixer amplifier to substantially eliminate noise and offset from an output signal produced by the amplifier. Dynamic limitations, i.e., glitching, that result from chopper stabilization at low power are substantially eliminated through a combination of chopping at low impedance nodes within the mixer amplifier and feedback. The signal path of the amplifier operates as a continuous time system, providing minimal aliasing of noise or external signals entering the signal pathway at the chop frequency or its harmonics. The amplifier can be used in a low power system, such as an implantable medical device, to provide a stable, low-noise output signal.

Abstract: A patient state is detected with at least one classification boundary generated by a supervised machine learning technique, such as a support vector machine. In some examples, the patient state detection is used to at least one of control the delivery of therapy to a patient, to generate a patient notification, to initiate data recording, or to evaluate a patient condition. In addition, an evaluation metric can be determined based on a feature vector, which is determined based on characteristics of a patient parameter signal, and the classification boundary. Example evaluation metrics can be based on a distance between at least one feature vector and the classification boundary and/or a trajectory of a plurality of feature vectors relative to the classification boundary over time.

Abstract: A patient state is detected with at least one classification boundary generated by a supervised machine learning technique, such as a support vector machine. The patient state can be, for example, a patient posture state. In some examples, the patient state detection is used to at least one of control the delivery of therapy to a patient, to generate a patient notification, to initiate data recording, or to evaluate a patient condition. In addition, an evaluation metric can be determined based on a feature vector, which is determined based on characteristics of a patient parameter signal, and the classification boundary. Example evaluation metrics can be based on a distance between at least one feature vector and the classification boundary and/or a trajectory of a plurality of feature vectors relative to the classification boundary over time.

Abstract: A patient state is detected with at least one classification boundary generated by a supervised machine learning technique, such as a support vector machine. In some examples, the patient state detection is used to at least one of control the delivery of therapy to a patient, to generate a patient notification, to initiate data recording, or to evaluate a patient condition. In addition, an evaluation metric can be determined based on a feature vector, which is determined based on characteristics of a patient parameter signal, and the classification boundary. Example evaluation metrics can be based on a distance between at least one feature vector and the classification boundary and/or a trajectory of a plurality of feature vectors relative to the classification boundary over time.

Abstract: Posture-responsive therapy is delivered by the medical system based on posture state input from only one of multiple posture sensors at any given time. An example implantable medical system includes a first posture sensor and a second sensor. A processor controls therapy delivery to the patient based on at least one of a patient posture state or a patient activity level determined based on input from only one of the first or second posture sensors. In some examples, one of multiple posture sensors of an implantable posture-responsive medical system is used to automatically reorient another posture sensor (of the system), which has become disoriented. The disoriented posture sensor may be automatically reoriented for one or more posture states at a time.

Abstract: This disclosure describes techniques implemented by a medical device, such as an implantable medical device (IMD). The IMD may be configured to detect a posture state of a patient, and deliver posture-responsive therapy. In particular, the IMD not only detects the posture state of a patient, but also detects timing associated with the detected posture state, such as the time of day, the day of the week, or a specific time of day associated with a specific day. In this way, the posture-responsive therapy delivered by the IMD may be dependent not only on the posture state of the patient, but also on the timing associated with the posture state. The same posture state, therefore, may result in different types of therapy to the patient if the same posture occurs at different times of the day.

Abstract: Posture-responsive therapy is delivered by the medical system based on posture state input from only one of multiple posture sensors at any given time. An example implantable medical system includes a first posture sensor and a second sensor. A processor controls therapy delivery to the patient based on at least one of a patient posture state or a patient activity level determined based on input from only one of the first or second posture sensors. In some examples, one of multiple posture sensors of an implantable posture-responsive medical system is used to automatically reorient another posture sensor (of the system), which has become disoriented. The disoriented posture sensor may be automatically reoriented for one or more posture states at a time.

Abstract: A patient state is detected with at least one classification boundary generated by a supervised machine learning technique, such as a support vector machine. In some examples, the patient state detection is used to at least one of control the delivery of therapy to a patient, to generate a patient notification, to initiate data recording, or to evaluate a patient condition. In addition, an evaluation metric can be determined based on a feature vector, which is determined based on characteristics of a patient parameter signal, and the classification boundary. Example evaluation metrics can be based on a distance between at least one feature vector and the classification boundary and/or a trajectory of a plurality of feature vectors relative to the classification boundary over time.

Abstract: A charging system is disclosed. In one embodiment, the system includes a charging unit having a primary coil, and an implantable medical device comprising a secondary coil to receive charge from the primary coil. The implantable medical device further includes a half-wave voltage-doubling rectifier coupled to the secondary coil, a full-wave rectifier coupled to the secondary coil, and a rechargeable power source. Control logic is provided to periodically configure the rechargeable power source to receive charge from a selected one of the voltage-doubling circuit and the full-wave rectifier in a manner that increases rate at which charge is transferred from the secondary coil to the rechargeable power source. The control logic may configure the rechargeable power source to receive charge based on one or more monitored conditions which may include, for example, an indication of a current, a voltage, a coupling coefficient, back-scatter, and temperature.

Abstract: Intracranial pressure of a patient may be monitored in order to evaluate a seizure disorder. In some examples, trends in the intracranial pressure over time may be monitored, e.g., to detect changes to the patient's condition. In addition, in some examples, a seizure metric may be generated for a detected seizure based on sensed intracranial pressures. The seizure metric may indicate, for example, an average, median, or highest relative intracranial pressure value observed during a seizure, a percent change from a baseline value during the seizure, or the time for the intracranial pressure to return to a baseline state after the occurrence of a seizure. In addition to or instead of intracranial pressure, patient motion or posture may be monitored in order to assess the patient's seizure disorder. For example, a seizure type or severity may be determined based on patient motion sensed during a seizure.

Abstract: Intracranial pressure of a patient may be monitored in order to evaluate a seizure disorder. In some examples, trends in the intracranial pressure over time may be monitored, e.g., to detect changes to the patient's condition. In addition, in some examples, a seizure metric may be generated for a detected seizure based on sensed intracranial pressures. The seizure metric may indicate, for example, an average, median, or highest relative intracranial pressure value observed during a seizure, a percent change from a baseline value during the seizure, or the time for the intracranial pressure to return to a baseline state after the occurrence of a seizure. In addition to or instead of intracranial pressure, patient motion or posture may be monitored in order to assess the patient's seizure disorder. For example, a seizure type or severity may be determined based on patient motion sensed during a seizure.

Abstract: This disclosure describes a chopper-stabilized sigma-delta analog-to-digital converter (ADC). The ADC is configured to provide accurate output at low frequency with relatively low power. The chopper-stabilized ADC substantially reduces or eliminates noise and offset from an output signal produced by the mixer amplifier. Dynamic limitations, i.e., glitching that result from chopper stabilization at low power are substantially eliminated or reduced through a combination of chopping at low impedance nodes within the mixer amplifier and feedback. The signal path of the ADC operates as a continuous time system, providing minimal aliasing of noise or external signals entering the signal pathway at the chop frequency or its harmonics. In this manner, the chopper-stabilized ADC can be used in a low power system, such as an implantable medical device (IMD), to provide a stable, low-noise output signal.

Abstract: A system and method for determining complex intercardiac impedance to detect various cardiac functions are disclosed involving a signal generator means for providing an adjustable direct current signal, a modulator for modulating the adjustable direct current signal to produce a modulated signal, at least one electrode for propagating the modulated signal across a myocardium, at least one sensor for detecting an outputted modulated signal from the myocardium, and at least one circuit to reduce the influence of process noise (aggressors) in the outputted modulated signal. The at least one circuit comprises an amplifier, a demodulator, and an integrator. The amplitude and phase of the final outputted modulated signal indicate the complex impedance of the myocardium. Changes in the complex impedance patterns of the myocardium provide indication of reduced oxygen and blood flow to the myocardium.

Abstract: Brain signals may be monitored at different locations of a mood circuit in order to determine a mood state of the patient. A relationship (e.g., a ratio) between frequency band characteristics of the monitored brain signals may be indicative of a particular mood state. In some examples, therapy parameter values that define the therapy delivered to the patient may be selected to maintain a target relationship (e.g., a target ratio) between the frequency band characteristics of the brain signals monitored within the mood circuit. In addition, in some examples, therapy delivery to the patient may be controlled based on the frequency band characteristics of brain signals sensed at different portions of the mood circuit.