Algorithm for AEEG data selection leading to wireless and long term epilepsy monitoring

Casson, Alexander J.; Yates, David C.; Patel, Shyamal; Rodriguez‐Villegas, Esther

doi:10.1109/iembs.2007.4352825

Cited by 21 publications

(11 citation statements)

References 17 publications

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“…Note however that these performance metrics are not directly comparable to the work in this paper, since these algorithms focused on automatic feature detection as opposed to data selection. Furthermore, ANNs generally have high computational complexity and are not suitable for hardware implementations with stringent power budgets [21]. A Discrete Wavelet Transform (DWT) based approach, also for automatic detection as opposed to data selection, with a sensitivity of 91.7% and FPR of 10.7% was reported in [20].…”

Section: Data Reduction In Weegmentioning

confidence: 99%

A 950 nW Analog-Based Data Reduction Chip for Wearable EEG Systems in Epilepsy

Iranmanesh

Rodriguez‐Villegas

2017

IEEE J. Solid-State Circuits

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Long-term EEG monitoring is an important tool used for the diagnosis of epilepsy. Truly Wearable EEG can be considered as the future of ambulatory EEG units, which are the current standard for long-term EEG monitoring. Replacing these short lifetime, bulky units with long-lasting miniature and wearable devices which can easily be worn by patients will result in more EEG data being acquired for longer monitoring periods. This paper presents an analog based data reduction integrated circuit that would reduce the amount of power required to transmit EEG data by identifying the sections of data that are interesting for diagnostic purposes while discarding background activity. Using the data reduction system as part of a miniature wireless EEG monitoring unit would yield significant reductions in power consumption since the transmitter will only be switched on based on the data reduction system output.A system prototype chip has been fabricated in a 0.35 µm CMOS process. The system consumes 760 nA from a 1.25 V supply and is able to achieve a sensitivity of 87% while transmitting 45% of the overall EEG data.

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Section: Data Reduction In Weegmentioning

confidence: 99%

A 950 nW Analog-Based Data Reduction Chip for Wearable EEG Systems in Epilepsy

Iranmanesh

Rodriguez‐Villegas

2017

IEEE J. Solid-State Circuits

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“…A large volume of data can result in analysis delay, frustration and poor EEG interpretation. Unique tools capable of performing efficient spectral analyses (Rossetti et al, 2006;Romcy-Pereira et al, 2008;Lehmkuhle et al, 2009), seizure estimation, and spike detection (Saab and Gotman, 2005;White et al, 2006;Casson et al, 2007;Jacquin et al, 2007;Hopfengärtner et al, 2007) has been used in several studies on epilepsy. Artificial neural networks have proven to be the most reliable tool (Gabor et al, 1996;Gabor, 1998;Nigam and Graupe, 2004;Kiymik et al, 2004;Tzallas et al, 2007;Srinivasan et al, 2007;Patnaik and Manyam, 2008) but require tremendous computational power in order to be time effective when analyzing large data sets.…”

Section: Assessment Of the Long-term Eeg Changesmentioning

confidence: 99%

Use of Telemetric EEG in Brain Injury

Furtado¹,

Rossetti²,

Yourick³

2011

Modern Telemetry

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“…In this study, we develop advanced signal processing algorithms and hardware architectures to accurately solve the EEG/MEG inverse problem in real-time. Added to this complexity is the portability constraints of the device when used in the wearable ambulatory EEG (AEEG) mode that is used to improve the quality and accuracy of brain disorder tests [4,11,12,13]. In this study, we propose sensor scheduling and data compression methods to reduce the number of sensors required and thus reduce the power consumption due to these devices.…”

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confidence: 99%

Efficient Bayesian Tracking of Multiple Sources of Neural Activity: Algorithms and Real-Time FPGA Implementation

Liu

Zhang

Chakrabarti

et al. 2013

IEEE Trans. Signal Process.

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Electrical neural activity detection and tracking have many applications in medical research and brain computer interface technologies. In this thesis, we focus on the development of advanced signal processing algorithms to track neural activity and on the mapping of these algorithms onto hardware to enable real-time tracking. At the heart of these algorithms is particle filtering (PF), a sequential Monte Carlo technique used to estimate the unknown parameters of dynamic systems.First, we analyze the bottlenecks in existing PF algorithms, and we propose a new parallel PF (PPF) algorithm based on the independent Metropolis-Hastings (IMH) algorithm. We show that the proposed PPF-IMH algorithm improves the root mean-squared error (RMSE) estimation performance, and we demonstrate that a parallel implementation of the algorithm results in significant reduction in inter-processor communication. We apply our implementation on a Xilinx Virtex-5 field programmable gate array (FPGA) platform to demonstrate that, for a one-dimensional problem, the PPF-IMH architecture with four processing elements and 1,000 particles can process input samples at 170 kHz by using less than 5% FPGA resources. We also apply the proposed PPF-IMH to waveform-agile sensing to achieve real-time tracking of dynamic targets with high RMSE tracking performance.We next integrate the PPF-IMH algorithm to track the dynamic parameters in neural sensing when the number of neural dipole sources is known. We analyze the computational complexity of a PF based method and propose the use of multiple particle filtering (MPF) to reduce the complexity. We demonstrate the improved performance of MPF using numerical simulations with both synthetic and real data. We also propose an FPGA implementation of the MPF algorithm and show that the implementation supports real-time tracking.For the more realistic scenario of automatically estimating an unknown number of time-varying neural dipole sources, we propose a new approach based on the probability hypothesis density filtering (PHDF) algorithm. The PHDF is implemented using particle filtering (PF-PHDF), and it is applied in a closed-loop to first estimate the number of dipole sources and then their corresponding amplitude, location and orientation parameters. We demonstrate the improved tracking performance of the proposed PF-PHDF algorithm and map it onto a Xilinx Virtex-5 FPGA platform to show its real-time implementation potential. i Finally, we propose the use of sensor scheduling and compressive sensing techniques to reduce the number of active sensors, and thus overall power consumption, of electroencephalography (EEG) systems. We propose an efficient sensor scheduling algorithm which adaptively configures EEG sensors at each measurement time interval to reduce the number of sensors needed for accurate tracking. We combine the sensor scheduling method with PF-PHDF and implement the system on an FPGA platform to achieve real-time tracking. We also investigate the sparsity of EEG signals and integrate com...

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Algorithm for AEEG data selection leading to wireless and long term epilepsy monitoring

Cited by 21 publications

References 17 publications

A 950 nW Analog-Based Data Reduction Chip for Wearable EEG Systems in Epilepsy

A 950 nW Analog-Based Data Reduction Chip for Wearable EEG Systems in Epilepsy

Use of Telemetric EEG in Brain Injury

Efficient Bayesian Tracking of Multiple Sources of Neural Activity: Algorithms and Real-Time FPGA Implementation

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