Theoretical study and optimisation of a standard deviation estimator circuit for adaptive threshold spike detection

Rummens, François; Ygorra, S.; Boussamba, Hol C. Mayiss; Renaud, Sylvie; Lewis, Noëlle

doi:10.1002/cta.2192

Cited by 4 publications

(5 citation statements)

References 18 publications

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“…Random noise representation in the electrical circuits is a major consideration for modeling and simulation 22,23 . Similar interest occurs in biomedical applications where the noise level of bioelectrical recordings 24 is analyzed in great detail. Such important studies stimulated the emergence of the generalized probability distribution method.…”

Section: Applicationsmentioning

confidence: 99%

Generalized probability density function and applications to the experimental data in electrical circuits and systems

Özyapıcı

Bilgehan²,

Sensoy

2020

Circuit Theory & Apps

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Summary The mathematical sciences, and particularly probabilistic and statistical methods, are key to understanding the dependencies of the systems. The purpose of this paper is to encourage a wider recognition by engineers of a new generalized principle which in its mathematical form is a powerful instrument for the solution of practical problems. Generalized probability density function was introduced to permit analysis without pre‐knowledge of the source of the data. The fundamental principles are extended to apply the most related engineering applications without the need to know the type of source generating the data. The generalized model presented eliminates preliminary work in engineering problems. The proposed model introduces an exponential density function to produce a direct solution to randomly varying data. The exponential density function is fully compatible with applications containing randomly distributed data. The success of the generalized model presented is due to the calculated parameters in the exponential density function. The method is applied to various problems chosen from the field of engineering with great success.

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Section: Applicationsmentioning

confidence: 99%

Generalized probability density function and applications to the experimental data in electrical circuits and systems

Özyapıcı

Bilgehan²,

Sensoy

2020

Circuit Theory & Apps

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show abstract

“…The RMS measurement of the neural signal should be frequently updated owing to the nonstationary nature of neural signals and their time variant statistical characteristics (eg, RMS value). The V THR is usually set above the RMS value of the neural signal ( V rms ) as follows:

V_{THR} = K \times V_{rms},

where K is a constant, and its typical value is between 3 and 7 . The value of K affects the accuracy of AP detection, choosing a low value for K results in false AP detections and reduced energy efficiency.…”

Section: Proposed Adaptive Ct Iς∆ Adcmentioning

confidence: 99%

“…where K is a constant, and its typical value is between 3 and 7. 22 The value of K affects the accuracy of AP detection, choosing a low value for K results in false AP detections and reduced energy efficiency. On the other hand, selecting a high value for K will prevent high-amplitude B-noise from being detected as the AP; however, the ADC will digitize low-amplitude APs with low resolution improperly.…”

Section: Automatic Ap Detectormentioning

confidence: 99%

An adaptive continuous‐time incremental Σ∆ ADC for neural recording implants

Barati

Yavari

2018

Circuit Theory & Apps

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Summary In this paper, an analog‐to‐digital converter (ADC) with adaptive resolution is presented for wireless neural recording implants. The resolution of the ADC is changed according to the neural signal content, and for this purpose, a continuous‐time (CT) incremental sigma‐delta (IΣ∆) modulator is employed. The ADC digitizes the action potential (AP) and background noise (B‐noise) with 8‐bit and 3‐bit resolutions, respectively. An automatic AP detector is used to separate the APs from the B‐noise in order to select one of the two proportional resolutions. The power dissipation and output data rate of the ADC are reduced by using this technique. Analytical calculations and behavioral simulation results are provided to evaluate the performance of the proposed ADC. To further confirm its efficiency, the circuit‐level implementation of the CT IΣ∆ ADC is presented in Taiwan Semiconductor Manufacturing Company (TSMC) 90‐nm complementary metal‐oxide semiconductor (CMOS) process. According to the simulation results, the proposed ADC achieves 8‐bit or 3‐bit resolution adaptively with 10 kHz bandwidth while the average power consumption is less than 1.89 μW from a single 1‐V power supply.

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“…A commonly used solution in electrophysiology consists of setting the detection threshold to a multiple of the standard deviation of individual channel signals [ 23 ]. Therefore, we implemented digital models [ 22 ] of analog real-time standard deviation estimators [ 24 ]. The chosen model is a closed-loop regulator based on noise distribution hypotheses.…”

Section: System Architecture and Fpga Designmentioning

confidence: 99%

Multimed: An Integrated, Multi-Application Platform for the Real-Time Recording and Sub-Millisecond Processing of Biosignals

Pirog

Bornat

Perrier

et al. 2018

Sensors

Self Cite

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Enhanced understanding and control of electrophysiology mechanisms are increasingly being hailed as key knowledge in the fields of modern biology and medicine. As more and more excitable cell mechanics are being investigated and exploited, the need for flexible electrophysiology setups becomes apparent. With that aim, we designed Multimed, which is a versatile hardware platform for the real-time recording and processing of biosignals. Digital processing in Multimed is an arrangement of generic processing units from a custom library. These can freely be rearranged to match the needs of the application. Embedded onto a Field Programmable Gate Array (FPGA), these modules utilize full-hardware signal processing to lower processing latency. It achieves constant latency, and sub-millisecond processing and decision-making on 64 channels. The FPGA core processing unit makes Multimed suitable as either a reconfigurable electrophysiology system or a prototyping platform for VLSI implantable medical devices. It is specifically designed for open- and closed-loop experiments and provides consistent feedback rules, well within biological microseconds timeframes. This paper presents the specifications and architecture of the Multimed system, then details the biosignal processing algorithms and their digital implementation. Finally, three applications utilizing Multimed in neuroscience and diabetes research are described. They demonstrate the system’s configurability, its multi-channel, real-time processing, and its feedback control capabilities.

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Theoretical study and optimisation of a standard deviation estimator circuit for adaptive threshold spike detection

Cited by 4 publications

References 18 publications

Generalized probability density function and applications to the experimental data in electrical circuits and systems

Generalized probability density function and applications to the experimental data in electrical circuits and systems

An adaptive continuous‐time incremental Σ∆ ADC for neural recording implants

Multimed: An Integrated, Multi-Application Platform for the Real-Time Recording and Sub-Millisecond Processing of Biosignals

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