A 10-bit 1kS/s-30kS/s successive approximation register analog-to-digital converter for biological signal acquisition

Geng, Wenliang; Wang, Guoxing; Lin, K. Y.; Tang, Kea-Tiong

doi:10.1109/bmei.2013.6746972

Cited by 7 publications

(2 citation statements)

References 8 publications

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“…With the goal to reduce the power consumption and reduce noise to improve the signal-to-noise ratio, this paper presents a new of architecture of SAR ADC in [3]. The accuracy is determined by the DAC which is its core component, hence an energy efficient binary capacitor array has been used in this design and the capacitance is almost halved compared to the conventional DAC.…”

Section: Successive Approximation +Register Adcmentioning

confidence: 99%

“…General block diagram of a bioelectric signal acquisition system [3] It is observed from Table 1 that different neural signals are usually spread over a narrow range of amplitudes and frequencies ranging from few Hz to 200Hz and μV to mV except for spike signal which can have the maximum frequency of 7 kHz, whereas ECG is a very weak signal with an amplitude range of 1-5 mV at very low frequencies of 0.05-100 Hz [4,5]. Since these signals are low level hence they require low noise operation which makes it a difficult task to design a biomedical sensor interfaces for these biophysiological signals.…”

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confidence: 99%

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Feasibility of Successive Approximation Register ADC in Ultra Low Power Biomedical Applications

Ahmed¹,

Bashir²,

Bilal³

et al. 2017

IJET

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Abstract-Analog-to-Digital Converter (ADC) is a critical block of the sensing unit of an implant and for measurements of various biophysiological signals, such as Electrocardiogram (ECG), Electroencephalogram (EEG) and Electromyogram (EMG) that covers the distinct portions of the frequency spectrum and signal bandwidths. ADC consumes about 30~35% of the total power of the device which is very high. Hence, for biomedical implants which require ultra low power consumption and low complexity to reduce the size and cost of the devices, there is a need of energy-efficient ADCs that conform to these restraints. This paper presents a study on the feasibility of ultra low power Successive Approximation Register (SAR) ADC in these biomedical applications. Various SAR ADC architectures proposed in the past decade to achieve ultra low power consumption have been investigated and their performances have been compared with respect to the parameters such as resolution, sampling frequency, signal-to-noise and distortion ratio, figure of merit among others. This paper also discusses the different switching schemes proposed to reduce the switching power consumed by the DAC. Finally, the future research scope and areas to further achieve ultra low power consumption without degrading the overall performance of the ADC has also been presented.

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Section: Successive Approximation +Register Adcmentioning

confidence: 99%

mentioning

confidence: 99%

Feasibility of Successive Approximation Register ADC in Ultra Low Power Biomedical Applications

Ahmed¹,

Bashir²,

Bilal³

et al. 2017

IJET

View full text Add to dashboard Cite

show abstract

Analysis design of area efficient segmentation digital to analog converter for ultra-low power successive approximation analog to digital converter

Sharuddin

Lee

Yusof

2016

Microelectronics Journal

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A Wearable sEMG Pattern-Recognition Integrated Interface Embedding Analog Pseudo-Wavelet Preprocessing

et al. 2019

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This paper presents a wearable wireless surface electromyogram (sEMG) integrated interface that utilizes a proposed analog pseudo-wavelet preprocessor (APWP) for signal acquisition and pattern recognition. The APWP is integrated into a readout integrated circuit (ROIC), which is fabricated in a 0.18-µm complementary metal-oxide-semiconductor (CMOS) process. Based on this ROIC, a wearable device module and its wireless system prototype are implemented to recognize five kinds of real-time handgesture motions, where the power consumption is further reduced by adopting low-power components. Real-time measurements of sEMG signals and APWP data through this wearable interface are wirelessly transferred to a laptop or a sensor hub, and then they are further processed to implement the pseudo-wavelet transform under the MATLAB environment. The resulting APWP-augmented pattern-recognition algorithm was experimentally verified to improve the accuracy by 7 % with a real-time frequency analysis.

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A 10-bit 1kS/s-30kS/s successive approximation register analog-to-digital converter for biological signal acquisition

Cited by 7 publications

References 8 publications

Feasibility of Successive Approximation Register ADC in Ultra Low Power Biomedical Applications

Feasibility of Successive Approximation Register ADC in Ultra Low Power Biomedical Applications

Analysis design of area efficient segmentation digital to analog converter for ultra-low power successive approximation analog to digital converter

A Wearable sEMG Pattern-Recognition Integrated Interface Embedding Analog Pseudo-Wavelet Preprocessing

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