A sensor of an implantable medical device senses electrical activity of the brain. A data analyzer of the device monitors an electrographic signal corresponding to the electrical activity of the sensed brain signal, and processes the brain signal to obtain a measure of phase-amplitude coupling. For a selected portion of the electrographic signal, the data analyzer detects first features and second features of the electrographic signal. The first features represent oscillations in a low frequency range, while the second features represent oscillations in a frequency range higher than the low frequency range. For example, the low frequency range may correspond to theta frequency and the higher frequency range may correspond to gamma frequency. The data analyzer determines a measure of phase-amplitude coupling between oscillations in the low frequency range and oscillations in the higher frequency range based on occurrences of second features which coincide with first features.

In certain embodiments, a headpiece 101 may contain the functional components described above integrated therein, and the headpiece may be releasably matable, The present invention relates to systems and methods for patient monitoring and intervention to prevent sudden unexpected death, as may occur in patients with epilepsy (SUDEP) or in infants as part of SUID, SIDS and/or suffocation. More specifically, the present invention provides a wearable device configured for monitoring a wearer and/or his environment, identifying and/or assessing death risk to the wearer, initiating communications to a caregiver that might provide an intervention or other treatment, and/or itself performing an action acting as an intervention to prevent death of the wearer. The wearable device includes particular sensors for gathering data from the wearer and/or the wearer's environment. Optionally, the wearable device may further include stimulators for delivering a death-preventing intervention stimulus to the wearer.

A system and method to facilitate the analysis of long-term scalp EEG recordings and the investigation of underlying epileptogenicity. Pathological high frequency oscillations are identified and extracted from the EEG recordings and electrophysiological source imaging is used to reconstruct cortical activity, which can be used as an aid in surgical resection for the treatment of epilepsy.

Systems and methods for a shape-memory in-ear biosensor for polysomnography and monitoring physiological signals are provided. Various embodiments include an earpiece made from a shape memory or temperature-dependent phase transition material embedded into polydimethylsiloxane elastomer body. The earpiece can use electrodes to detect physiological signals by making direct contact with a user's skin and without the use of electrically conductive gel. When heated above the glass transition temperature of the shape memory polymer, various embodiments of the biosensor may be folded. When cooled, the biosensor will maintain the folded shape. The folded biosensor may then be inserted into the ear canal of a user where, in response to heating by the user's body, it partially unfolds to conform to the shape of the ear canal.

A medical device or system of medical devices can be configured to detect an indicator of a symptom in a patient; in response to detecting the indicator of the symptom in the patient, deliver to the patient a first stimulation therapy; and in response to determining that the indicator of the symptom has been present for more than a threshold amount of time after beginning to deliver the first stimulation therapy a second stimulation therapy different than the first stimulation therapy.

Methods and systems for detecting seizures include generating two-dimensional frames that each include a first set of elements that store measurements from sensors and a second set of elements that store values calculated from said measurements. The two-dimensional frames are classified using a machine learning model. It is determined that a subject experienced a seizure during a measurement interval based on an output of the machine learning model. A corrective action is performed responsive to the determination that the subject experienced a seizure.

One embodiment provides a method, including: obtaining EEG data from one or more single channel EEG sensor worn by a user; classifying, using a processor, the EEG data as one of nominal and abnormal; and providing an indication associated with a classification of the EEG data. Other embodiments are described and claimed.

The invention relates to a computer program for training a neurological condition detection algorithm to be used for neurological condition detection in an implantable neurostimulation device having a target electrode arrangement, the computer program comprising the following steps: a) inputting EEG data in a computer which executes the computer program, the EEG data being recorded by at least one EEG from at least one patient using an electrode system with a plurality of electrode channels, b) identifying neurological activity in the EEG data, which corresponds to a neurological condition, based upon neurological condition identification tags included in the EEG data and/or input in the computer, c) selecting a subset of electrode channels out of the available electrode channels in the EEG data depending c1) on the identified neurological activity and/or c2) on characteristic data of the target electrode arrangement, d) training a neurological condition detection algorithm by using the EEG data only of the selected subset of electrode channels.

A seizure detection system including one or more circuits configured to receive an electroencephalogram (EEG) signal generated based on electrical brain activity of a patient. The one or more circuits are configured to identify candidate seizures with a statistical analysis that identifies the candidate seizures based on changes in non-linear features of the EEG signal, determine to switch from identifying the candidate seizures with the statistical analysis to an artificial intelligence model, and switch from identifying the candidate seizures with the statistical analysis to identifying the candidate seizures based on the artificial intelligence model with the EEG signal.

An implantable device has a slim carrier with first and second sides. The two sides each have a signal electrode and a body potential electrode. The body potential electrodes are internally connected. Electrodes on opposing sides are aligned. An insulating extension of insulating material extends beyond a perimeter of the carrier to increase device sensitivity. If the carrier is hollow, there may be an IC inside to provide active functions including power management, communication, device control, and signal storage. The IC may include an amplifier and an ADC to sense signals, that it may store in memory and/or communicate to an external interface unit (EIU). The IC may include a DAC and a power amplifier to electrically stimulate tissue with signals received from the EIU.