Abstract: A method of monitoring a patient for seizure activity may include monitoring the patient by collecting an electromyography signal, determining features of the signal using wavelet analysis and inputting determined feature values into a neural network trained to detect seizure activity. Related apparatuses are also described.

Abstract: Systems and methods are described for detecting and characterizing seizures or seizure-related events. The methods herein may include determining magnitude and/or scaled magnitude data for each of at least one high and low frequency group of signals. Based on the determined magnitudes and/or scaled magnitude data, seizures or seizure-related events may be characterized.

Abstract: Methods and apparatuses for detecting and characterizing seizures are described. In some embodiments, the methods and apparatuses include collecting an electrical signal from one or more electrodes disposed on the head or scalp of a patient and processing the electrical signal to determine the dynamics of a seizure event.

Abstract: Methods and apparatuses for detecting and characterizing seizures are described. In some embodiments, the methods and apparatuses include collecting an EEG signal and selecting or filtering the signal in order to increase a prevalence of a part of said EEG signal derived from activation of muscle. In some embodiments, one or more EEG signals may be analyzed with one or more algorithms designed to detect muscle components of the signal in order to perform seizure semiology and/or to differentiate detected seizures based on type.

Abstract: Patients afflicted by a seizure may be monitored for the presence of post-ictal motor manifestations that may indicate that the patient is at heightened risk of adverse effects of a seizure, including, for example, risk of sudden explained death in epilepsy. If the patient is deemed to be at risk of experiencing adverse effects of a seizure, one or more system responses may be initiated as appropriate for the at-risk patient.

Abstract: A method of monitoring a patient for seizure activity may include monitoring the patient by collecting an electromyography signal, determining features of the signal using wavelet analysis and inputting determined feature values into a neural network trained to detect seizure activity. Related apparatuses are also described.

Abstract: A system for monitoring a patient for seizure activity may include one or more detection units. The detection units may include one or more electromyography sensors configured to evaluate a patient for seizure activity. The detection units may further include one or more sensors configured to detect an orientation of the patient and/or when a sensor is bearing a portion of the patient's weight or otherwise pressed against the patient. In some embodiments, methods of detection may include adjusting an algorithm for seizure detection based on a recognition of whether a muscle is constrained or whether the muscle is free to move.

Abstract: A method and apparatus for detecting seizures with motor manifestations including detecting EMG signals, isolating from the EMG signals spectral data for a plurality of frequency bands, and calculating a T-squared value there from. The T-squared values may be detected in real time, such as in a patient's home environment, and the T-squared data may be compared to a threshold T-squared value to determine whether an alarm is sent.

Abstract: Systems and methods are described for detecting and characterizing seizures or seizure-related events. The methods herein may include determining magnitude and/or scaled magnitude data for each of at least one high and low frequency group of signals. Based on the determined magnitudes and/or scaled magnitude data, seizures or seizure-related events may be characterized.

Abstract: A monitoring system may include each of a left and a right side EMG detection device and may be configured to detect either seizure activity associated with the left side or right side of a patient's body. The system may further be configured to differentiate symmetric or asymmetric seizure activity. For example, the system may characterize whether left and right side EMG detectors show similar or dissimilar levels of activation during a suspected seizure. In some embodiments, the system may also include one or more orientation and/or position sensors. The system may be configured to determine the orientation and position of the sensor over time and to correlate this data with EMG signals.

Abstract: A monitoring system may include each of a left and a right side EMG detection device and may be configured to detect either seizure activity associated with the left side or right side of a patient's body. The system may further be configured to differentiate symmetric or asymmetric seizure activity. For example, the system may characterize whether left and right side EMG detectors show similar or dissimilar levels of activation during a suspected seizure. In some embodiments, the system may also include one or more orientation and/or position sensors. The system may be configured to determine the orientation and position of the sensor over time and to correlate this data with EMG signals.

Abstract: A method and apparatus for detecting seizures with motor manifestations including detecting EMG signals, isolating from the EMG signals spectral data for a plurality of frequency bands, and calculating a T-squared value there from. The T-squared values may be detected in real time, such as in a patient's home environment, and the T-squared data may be compared to a threshold T-squared value to determine whether an alarm is sent.

Abstract: A method of detecting seizures may comprise receiving an EMG signal and processing the received EMG signal to determine whether a seizure characteristic is present in the EMG signal during a time window. An apparatus for detecting seizures with motor manifestations may comprise one or more EMG electrodes capable of providing an EMG signal substantially representing seizure-related muscle activity; and a processor configured to receive the EMG signal, process the EMG signal to determine whether a seizure may be occurring, and generate an alert if a seizure is determined to be occurring based on the EMG signal.

Abstract: A method of detecting seizures may comprise receiving an EMG signal and processing the received EMG signal to determine whether a seizure characteristic is present in the EMG signal during a time window. An apparatus for detecting seizures with motor manifestations may comprise one or more EMG electrodes capable of providing an EMG signal substantially representing seizure-related muscle activity; and a processor configured to receive the EMG signal, process the EMG signal to determine whether a seizure may be occurring, and generate an alert if a seizure is determined to be occurring based on the EMG signal.

Abstract: A method and apparatus for detecting seizures with motor manifestations including detecting EMG signals, isolating from the EMG signals spectral data for a plurality of frequency bands, and calculating a T-squared value there from. The T-squared values may be detected in real time, such as in a patient's home environment, and the T-squared data may be compared to a threshold T-squared value to determine whether an alarm is sent.

Abstract: A method of monitoring a patient for seizure activity may include monitoring the patient by collecting an EMG signal and determining whether samples of signal that include regions of elevated signal amplitude are present in the collected signal. The samples may further be qualified based on one or more properties of a clonic-phase portion of a seizure, and if the samples are qualified, a qualified-clonic-phase burst activity level may be determined. A response may then be executed if the qualified-clonic-phase burst activity level exceeds a threshold value. Related apparatuses are also described.

Abstract: A method and apparatus for detecting seizures with motor manifestations including detecting EMG signals, isolating from the EMG signals spectral data for a plurality of frequency bands, and calculating a T-squared value there from. The T-squared values may be detected in real time, such as in a patient's home environment, and the T-squared data may be compared to a threshold T-squared value to determine whether an alarm is sent.

Abstract: Patients afflicted by a seizure may be monitored for the presence of post-ictal motor manifestations that may indicate that the patient is at heightened risk of adverse effects of a seizure, including, for example, risk of sudden explained death in epilepsy. If the patient is deemed to be at risk of experiencing adverse effects of a seizure, one or more system responses may be initiated as appropriate for the at-risk patient.

Abstract: A method and apparatus for detecting seizures with motor manifestations including detecting EMG signals, isolating from the EMG signals spectral data for a plurality of frequency bands, and calculating a T-squared value there from. The T-squared values may be detected in real time, such as in a patient's home environment, and the T-squared data may be compared to a threshold T-squared value to determine whether an alarm is sent.

Abstract: A monitoring system may include each of a left and a right side EMG detection device and may be configured to detect either seizure activity associated with the left side or right side of a patient's body. The system may further be configured to differentiate symmetric or asymmetric seizure activity. For example, the system may characterize whether left and right side EMG detectors show similar or dissimilar levels of activation during a suspected seizure. In some embodiments, the system may also include one or more orientation and/or position sensors. The system may be configured to determine the orientation and position of the sensor over time and to correlate this data with EMG signals.