Provided is a method for long-distance transmission of physiological signals in a closed loop system, including generating a signal at a user terminal of the closed loop system, compressing the signal to generate a compressed signal, transmitting the compressed signal from the user terminal to a computing terminal of the closed loop system, receiving and comparing the compressed signal with a database at the computing terminal to generate a comparison result and a feedback signal, and transmitting the feedback signal from the computing terminal to the user terminal. A time interval between generating the signal and receiving the feedback signal at the user terminal is less than a threshold.

Provided is a method for long-distance transmission of physiological signals in a closed loop system, including generating a signal at a user terminal of the closed loop system, compressing the signal to generate a compressed signal, transmitting the compressed signal from the user terminal to a computing terminal of the closed loop system, receiving and comparing the compressed signal with a database at the computing terminal to generate a comparison result and a feedback signal, and transmitting the feedback signal from the computing terminal to the user terminal. A time interval between generating the signal and receiving the feedback signal at the user terminal is less than a threshold.

An information processing apparatus includes an information acquisition unit and an estimation unit. The information acquisition unit is configured to acquire biological information on a measurement target portion of a measurement target person, the biological information being measured by a biological information measurement apparatus. The estimation unit is configured to estimate a position and a size of the measurement target portion using positional information on a plurality of locations in the measurement target portion and complementary positional information.

Methods, systems, and apparatuses for detecting seizure events are disclosed, including a system for identification of an increased risk of a severe neurological event. The system may include an electroencephalogram (“EEG”) monitoring unit configured to collect EEG data from the patient during at least a postictal phase or one or more seizures and a processing unit configured to receive the EEG data from the EEG monitoring unit. The processing unit is configured to detect postictal EEG suppression from the EEG data and to identify the increased risk of the severe neurological event based on the detected postictal EEG suppression. Other embodiments are described and claimed.

Methods, systems, and apparatuses for detecting seizure events are disclosed, including a system for identification of an increased risk of a severe neurological event. The system may include an electroencephalogram (“EEG”) monitoring unit configured to collect EEG data from the patient during at least a postictal phase or one or more seizures and a processing unit configured to receive the EEG data from the EEG monitoring unit. The processing unit is configured to detect postictal EEG suppression from the EEG data and to identify the increased risk of the severe neurological event based on the detected postictal EEG suppression. Other embodiments are described and claimed.

A sleep study system comprises a set of sensors for monitoring physiological parameters of a subject during sleep as part of a sleep study and for monitoring the sleep stage of the subject. It is determined if intervention to the subject is needed for maintenance or repair to the sleep study system. If so, a time to perform the intervention is also derived based on the sleep stage of the subject, in particular so as to be least disruptive to the subject.

A method and device allowing a physiological signal, typically representative of brain activity, to be transcribed in the form of sensory signals perceptible to a human user, typically acoustic signals is provided. For this purpose, a physiological signal is acquired and then analysed in such a way as to detect therein patterns that are then parameterised in the time domain. One or more parameters of these patterns are used to determine one or more parameters of the generated sensory signals and/or to determine one or more parameters of temporal envelopes used to modulate the sensory signals. This method and device can be applied, in particular, to neuro-acoustic transduction.

A method and device allowing a physiological signal, typically representative of brain activity, to be transcribed in the form of sensory signals perceptible to a human user, typically acoustic signals is provided. For this purpose, a physiological signal is acquired and then analysed in such a way as to detect therein patterns that are then parameterised in the time domain. One or more parameters of these patterns are used to determine one or more parameters of the generated sensory signals and/or to determine one or more parameters of temporal envelopes used to modulate the sensory signals. This method and device can be applied, in particular, to neuro-acoustic transduction.

A computer network implemented system for improving the operation of one or more biofeedback computer systems is provided. The system includes an intelligent bio-signal processing system that is operable to: capture bio-signal data and in addition optionally non-bio-signal data; and analyze the bio-signal data and non-bio-signal data, if any, so as to: extract one or more features related to at least one individual interacting with the biofeedback computer system; classify the individual based on the features by establishing one or more brain wave interaction profiles for the individual for improving the interaction of the individual with the one or more biofeedback computer systems, and initiate the storage of the brain waive interaction profiles to a database; and access one or more machine learning components or processes for further improving the interaction of the individual with the one or more biofeedback computer systems by updating automatically the brain wave interaction profiles based on detecting one or more defined interactions between the individual and the one or more of the biofeedback computer systems. A number of additional system and computer implemented method features are also provided.

A computer network implemented system for improving the operation of one or more biofeedback computer systems is provided. The system includes an intelligent bio-signal processing system that is operable to: capture bio-signal data and in addition optionally non-bio-signal data; and analyze the bio-signal data and non-bio-signal data, if any, so as to: extract one or more features related to at least one individual interacting with the biofeedback computer system; classify the individual based on the features by establishing one or more brain wave interaction profiles for the individual for improving the interaction of the individual with the one or more biofeedback computer systems, and initiate the storage of the brain waive interaction profiles to a database; and access one or more machine learning components or processes for further improving the interaction of the individual with the one or more biofeedback computer systems by updating automatically the brain wave interaction profiles based on detecting one or more defined interactions between the individual and the one or more of the biofeedback computer systems. A number of additional system and computer implemented method features are also provided.