Abstract: The present invention relates to an integrated system and method for EEG signal acquisition and interpretation, for neonatal seizure detection. The system as per the present invention comprises a control device, an integrated circuit, a wireless communication module, a visual indication means, and a power management module. The control device has a Convolutional Neural Network (CNN) embedded on it, and is programmed to configure the integrated circuit to receive a plurality of channels of EEG data from a plurality of EEG acquisition electrodes, and to amplify and digitize the received EEG data. The integrated circuit is further configured to segment the channels of EEG data to a plurality of sequential epochs of EEG data. The control device is configured to pass the sequential epochs of EEG data through the CNN which is pre-trained to output the probability of a seizure in the EEG data passed through it.

Abstract: A power estimation tool (1) receives as inputs a netlist (2) for a circuit and a library (3) of power models having values for power consumption of circuit components. It stores estimated values for components which are not randomness-preserving, and accurate values for components which are randomness-preserving. A component is randomness-preserving if input and output fixed length strings are random, in which each element in a sequence has an equal probability to occur. The values in the library (3) for randomness-preserving components are for small, low-level, components and are exact. The latter values ma for example be provided in specifications for standard low-level components such as XOR gates. The tool (1) feeds as an output average power consumption for the whole target circuit into a circuit design process (4). The input to the design process may be used in iterative cycles in which there is dynamic change of a netlist so that a target circuit can be optimized for power efficiency.