A Seizure-Based Power Reduction SoC for Wearable EEG in Epilepsy

Iranmanesh, Saam; Raikos, George; Imtiaz, Syed Anas; Rodriguez‐Villegas, Esther

doi:10.1109/access.2019.2948231

Cited by 9 publications

(3 citation statements)

References 33 publications

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“…Conventional methods usually require an artificially designed approach to feature extraction and selection [ 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 ]. Guangpeng et al [ 12 ] extracted the time–frequency feature maps of interval EEG signals.…”

Section: Introductionmentioning

confidence: 99%

Detection Method of Epileptic Seizures Using a Neural Network Model Based on Multimodal Dual-Stream Networks

Wang,

Xu,

Peng

et al. 2024

Sensors

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Epilepsy is a common neurological disorder, and its diagnosis mainly relies on the analysis of electroencephalogram (EEG) signals. However, the raw EEG signals contain limited recognizable features, and in order to increase the recognizable features in the input of the network, the differential features of the signals, the amplitude spectrum and the phase spectrum in the frequency domain are extracted to form a two-dimensional feature vector. In order to solve the problem of recognizing multimodal features, a neural network model based on a multimodal dual-stream network is proposed, which uses a mixture of one-dimensional convolution, two-dimensional convolution and LSTM neural networks to extract the spatial features of the EEG two-dimensional vectors and the temporal features of the signals, respectively, and combines the advantages of the two networks, using the hybrid neural network to extract both the temporal and spatial features of the signals at the same time. In addition, a channel attention module was used to focus the model on features related to seizures. Finally, multiple sets of experiments were conducted on the Bonn and New Delhi data sets, and the highest accuracy rates of 99.69% and 97.5% were obtained on the test set, respectively, verifying the superiority of the proposed model in the task of epileptic seizure detection.

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Section: Introductionmentioning

confidence: 99%

Detection Method of Epileptic Seizures Using a Neural Network Model Based on Multimodal Dual-Stream Networks

Wang,

Xu,

Peng

et al. 2024

Sensors

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“…Numerous technological advancements in terms of power reductions [1][2][3], size [4][5][6], and cost [7][8][9] in the field of integrated circuit technology have catalyzed the rapid evolution of wearable devices. Unfortunately, these new advancements are still constrained by the current battery technology, which creates expensive, bulky, and short lifespan devices [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Flexible, Piezoelectric Aluminum-Doped Zinc Oxide Energy Harvesters with Printed Electrodes for Wearable Applications

Suhaimi

Nordin

Ralib

et al. 2022

SWCC

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Aims: Recent advancements in sensing technology and wireless communications have accelerated the development of the Internet of Things (IoT) which promote the usage of wearable sensors. An emerging trend is to develop self-sustainable wearable devices, thus eliminating the necessity of the user to carry bulky batteries. In this work, the development of a flexible piezoelectric energy harvester that is capable of harvesting energy from low frequency vibrations is presented. The target application of this energy harvester is for usage in smart shoes. Objectives: The objectives of this research is to design, fabricate and test an energy harvester on PET substrate using Aluminum Zinc Oxide as its piezoelectric layer. Methods: The energy harvester was designed as a cantilever structure using PET/AZO/Ag layers in d33 mode which can generate large output voltages with small displacements. The electrodes were designed as an interdigitated structure in which two significant design parameters were chosen, namely the effect of gap between electrodes, g and number of interdigital electrodes (IDE) pairs, N to the output voltage and resonant frequency. Results: The sputtered AZO on PET showed c-axis orientation at 002 peak with 2 values of 34.45° which indicates piezoelectric behaviour. The silver IDE pairs were screen-printed on the AZO thin film. Functionality of the device as an energy harvester was demonstrated by testing it using a shaker. The energy harvester was capable of generating 0.867 Vrms output voltage when actuated at 49.6 Hz vibrations. Conclusion: This indicates that the AZO thin films with printed silver electrodes can be used as flexible, d33 energy harvesters.

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“…difficulty in reducing the size, weight and the amount of EEG data needed to be analyzed by neurologists [2]- [4]. To alleviate above difficulties, wearable AEEG (WAEEG) has been proposed to transmit EEG data wirelessly [5], [6]. However, 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.…”

Section: Introductionmentioning

confidence: 99%

Realization of Analog Wavelet Filter Using Hybrid Genetic Algorithm for On-Line Epileptic Event Detection

Zhao

Zhang

et al. 2020

IEEE Access

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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.

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A Seizure-Based Power Reduction SoC for Wearable EEG in Epilepsy

Cited by 9 publications

References 33 publications

Detection Method of Epileptic Seizures Using a Neural Network Model Based on Multimodal Dual-Stream Networks

Detection Method of Epileptic Seizures Using a Neural Network Model Based on Multimodal Dual-Stream Networks

Flexible, Piezoelectric Aluminum-Doped Zinc Oxide Energy Harvesters with Printed Electrodes for Wearable Applications

Realization of Analog Wavelet Filter Using Hybrid Genetic Algorithm for On-Line Epileptic Event Detection

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