Abstract
As the evolution of traditional electroencephalogram (EEG) monitoring unit for epilepsy diagnosis, wearable ambulatory EEG (WAEEG) system transmits EEG data wirelessly, and can be made miniaturized, discrete and social acceptable. To prolong the battery lifetime, analog wavelet filter is used for epileptic event detection in WAEEG system to achieve on-line data reduction. For mapping continuous wavelet transform to analog filter implementation with low-power consumption and high approximation accuracy, this paper proposes a novel approximation method to construct the wavelet base in analog domain, in which the approximation process in frequency domain is considered as an optimization problem by building a mathematical model with only one term in the numerator. The hybrid genetic algorithm consisting of genetic algorithm and quasi-Newton method is employed to find the globally optimum solution, taking required stability into account. Experiment results show that the proposed method can give a stable analog wavelet base with simple structure and higher approximation accuracy compared with existing method, leading to a better spike detection accuracy. The fourth-order Marr wavelet filter is designed as an example using Gm-C filter structure based on LC ladder simulation, whose power consumption is only 33.4 pW at 2.1Hz. Simulation results show that the design method can be used to facilitate low power and small volume implementation of on-line epileptic event detector.
Highlights
Epilepsy is a group of neurological disorders characterized by unprovoked and recurrent seizures, which affects approximately 50 million people worldwide
The wavelet scale is realized by setting Ibias = 6.67 pA to achieve the centre frequency of 2.1 Hz, corresponding to the ultra-low power consumption of 33.4 pW
Simulation results have shown that the dynamic range and signal-to-noise ratio (SNR) are 46 dB and 43dB respectively, which meet the requirements by EEG analysis [13]
Summary
Epilepsy is a group of neurological disorders characterized by unprovoked and recurrent seizures, which affects approximately 50 million people worldwide. Electroencephalograph (EEG) is the key tool for clinical epilepsy diagnosis by means of detecting the epileptic events, e.g. interictal spikes and spike-and-waves [1]. Ambulatory EEG (AEEG) is widely used for long-term monitoring which allows to be performed in patients’ home environment. Wearable AEEG (WAEEG) has been proposed to transmit EEG data wirelessly [5], [6]. Long-term monitoring can generate huge amounts of EEG data, and make wireless transmission very powerhungry, that is unsuitable for the battery powered WAEEG which has stringent power budget. As a solution to the aforementioned problem, online data reduction for WAEEG system has been presented, in which epileptic event detection algorithm (EEDA) is used to reduce the amount of wirelessly transmitted EEG data, and the power dissipation [7], [8]. As a precondition for saving power by data reduction strategy, EEDA should be implemented at a very low power
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