Abstract: Described herein are methods and systems for clustering events in temporal signals, such as physiological signals. In some embodiments, a method for identifying events of interest from noisy physiological signal data is provided, the method comprising: receiving physiological signal data; generating filtered signal using a filter defined by a spectral band; generating signal peak data by identifying, for each of a plurality of time windows, a signal peak in the filtered data; grouping waveforms corresponding to the time windows based on the signal peak data to generate clustered event data; determining parameters for a noise beta distribution; identifying a cluster of waveforms that does not fall within the noise beta distribution; generating a graphical representation based on the waveforms in the cluster; and displaying the graphical representation.

Abstract: Described herein are methods and systems for clustering events in temporal signals, such as physiological signals. In some embodiments, a method for identifying events of interest from noisy physiological signal data is provided, the method comprising: receiving physiological signal data; generating filtered signal using a filter defined by a spectral band; generating signal peak data by identifying, for each of a plurality of time windows, a signal peak in the filtered data; grouping waveforms corresponding to the time windows based on the signal peak data to generate clustered event data; determining parameters for a noise beta distribution; identifying a cluster of waveforms that does not fall within the noise beta distribution; generating a graphical representation based on the waveforms in the cluster; and displaying the graphical representation.

Abstract: A prediction and stimulation system or method is provided for neurological disorders characterized by a local dysfunction in neuronal activity regulation. An array of electrodes detects neuronal electrical activity of selected brain region. A detection module detects in each electrode changes in electric field and neuronal activity. A prediction module predicts for each electrode abnormal regimes determined by neurological disorder. A neuromodulation module interfaced with the electrodes selects one or more electrodes in the array where a predefined threshold has been exceeded and stimulates the brain region through each of these selected electrodes using an intermittent therapeutic stimulation pattern with a frequency between 150 and 200 Hz. The provided therapy allows for seizure prediction and detection with high accuracy, potential low risk and increased battery life.

Abstract: A prediction and stimulation system or method is provided for neurological disorders characterized by a local dysfunction in neuronal activity regulation. An array of electrodes detects neuronal electrical activity of selected brain region. A detection module detects in each electrode changes in electric field and neuronal activity. A prediction module predicts for each electrode abnormal regimes determined by neurological disorder. A neuromodulation module interfaced with the electrodes selects one or more electrodes in the array where a predefined threshold has been exceeded and stimulates the brain region through each of these selected electrodes using an intermittent therapeutic stimulation pattern with a frequency between 150 and 200 Hz. The provided therapy allows for seizure prediction and detection with high accuracy, potential low risk and increased battery life.