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: A method of monitoring a patient for seizure activity may include detecting portions of elevated electromyography signal amplitude and analyzing whether the elevations meet one or characteristics of either an epileptic seizure or non-epileptic psychogenic event.

Abstract: A system and method for detecting and analyzing the characteristics of seizures, such as tonic-clonic seizures, comprising at least one processor unit capable of recording a signal from a sensor unit, one or more analysis modules configured to analyze the recorded signal, one or more evaluation modules configured to evaluate the output signals from the analysis modules. A first analysis module is configured to analyze the recorded signal within a first frequency band, and a second analysis module is configured to analyze the recorded signal within a second frequency band, and the evaluation module is configured to calculate the ratio between the two output signals from the analysis modules. The system includes a seizure detection module configured to detect a seizure and a time-point extraction module configured to determine at least one starting point and an end point of the seizure.

Abstract: Methods are described herein for adjusting characteristics of signals used to verify or track the reliability of communication between a remote sensor and a managing device. For example, an exchange rate of signals sent between the remote sensor and managing device may be adjusted to minimize power consumption when the devices are determined to be reliably communicating. In some embodiments, apparatuses and methods herein may further be configured to inform an individual of when a remote sensor approaches a physical boundary, moves out of a communication range, is turned off, becomes damaged, or otherwise has or may become incapable of proper or reliable communication.

Abstract: Apparatuses and methods for monitoring a patient for seizure activity and for verifying the contact integrity of electrodes included among an EMG sensor may include generating a test signal of known periodicity and applying the signal to at least one electrode in a sensor system. The test signal may be monitored to verify contact integrity of the electrodes.

Abstract: A method and system for detecting a probability of psychogenic non-epileptic seizures using a portable battery powered device placed on the body of a patient and a device for manually logging detected seizures within a time period. The device may, advantageously, have a seizure detection algorithm which automatically records seizures detected within that time period. The two sets of data may then be transferred to a another device where the logged time stamps are matched to the recorded time stamps for determining if the detected seizure is a generalized tonic-clonic seizure (GTCS) or might be a psychogenic nonepileptic seizure (PNES). This provides a cheap and simple method for registering a probability of PNES by using a seizure detection device having an EMG-sensor or an accelerometer.

Abstract: A method and apparatus for monitoring a patient for seizure activity including collecting and processing EMG signal data and categorizing the detected data to execute risk stratification. A transmission protocol that is tailored for detected events may be selected and executed.

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 reviewing medical data for a patient susceptible to seizure activity may include monitoring the patient using electromyography electrodes and acoustic sensors. Data may be collected and organized for review and analysis by processing of the data to facilitate graphing of electromyography data and enabling a user to play an audio recording of detected sounds.

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 system for detecting the onset of seizures comprising a measuring unit having one or more sensors for detecting the electromyographic signal of one or more muscles of the user. The sensors are connected to a pre-processing module comprising a high-pass filter which filters out noise and motion artefacts related to normal muscle activities. The pre-processing module is connected to a feature extraction module comprising a threshold detector which counts the number of crossings with a hysteresis within a predetermined time window. The feature extraction module is connected to a classification module which compares the extracted features to a first and second threshold and generates an event signal if the extracted features are above the first and second threshold.

Abstract: Methods and apparatuses are described for qualifying peaks included among a collected EMG signal and include methods where various test groupings of peaks may be constructed from among a group of peaks. Methods and apparatuses herein may facilitate identification of patterns of abnormal motor manifestation, initiation of alarms if those patterns are identified, and the organization of data for inclusion in a searchable medical database.

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.

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 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.