Abstract: An integrated circuit chip and method for EEG monitoring. In one embodiment, the integrated circuit chip includes an Analog Front End cell in communication with an electrode and a Classification Processor wherein a signal received from the electrode is processed by the Classification Engine cell and designated as seizure or non-seizure. In another embodiment, the Analog Front End cell includes an amplifier cell in communication with an electrode; and an ASPU cell in communication with the amplifier cell. In yet another embodiment, the Classification Processor includes a DBE Channel Controller cell; a Feature Extraction Engine Processor cell, and a Classification Engine cell in communication with the Feature Extraction Engine Processor cells and the DBE Channel Controller cell.

Abstract: An integrated circuit chip and method for EEG monitoring. In one embodiment, the integrated circuit chip includes an Analog Front End cell in communication with an electrode and a Classification Processor wherein a signal received from the electrode is processed by the Classification Engine cell and designated as seizure or non-seizure. In another embodiment, the Analog Front End cell includes an amplifier cell in communication with an electrode; and an ASPU cell in communication with the amplifier cell. In yet another embodiment, the Classification Processor includes a DBE Channel Controller cell; a Feature Extraction Engine Processor cell, and a Classification Engine cell in communication with the Feature Extraction Engine Processor cells and the DBE Channel Controller cell.

Abstract: An integrated circuit chip and method for EEG monitoring. In one embodiment, the integrated circuit chip includes an Analog Front End cell in communication with an electrode and a Classification Processor wherein a signal received from the electrode is processed by the Classification Engine cell and designated as seizure or non-seizure. In another embodiment, the Analog Front End cell includes an amplifier cell in communication with an electrode; and an ASPU cell in communication with the amplifier cell. In yet another embodiment, the Classification Processor includes a DBE Channel Controller cell; a Feature Extraction Engine Processor cell, and a Classification Engine cell in communication with the Feature Extraction Engine Processor cells and the DBE Channel Controller cell.

Abstract: The present invention provides methods and systems for patient-specific seizure onset detection. In one embodiment, at least one EEG waveform of the patient is recorded, and at least one epoch (sample) of the waveform is extracted. The waveform sample is decomposed into one or more subband signals via a wavelet decomposition of the waveform sample, and one or more feature vectors are computed based on the subband signals. A seizure onset can then be identified based on classification of the feature vectors to a seizure or a non-seizure class by comparing the feature vectors with a decision measure previously computed for that patient. The decision measure can be derived based on reference seizure and non-seizure EEG waveforms of the patient. In another aspect, similar methodology is employed for automatic detection of alpha waves. In other aspects, the invention provides diagnostic and imaging systems that incorporate the above seizure-onset and alpha-wave detection methodology.