24.3 An implantable 64nW ECG-monitoring mixed-signal SoC for arrhythmia diagnosis

Jeon, D.; Chen, Yen‐Po; Lee, Yoonmyung; Kim, Yejoong; Foo, Zhiyoong; Kruger, Grant H.; Oral, Hakan; Berenfeld, Omer; Zhang, Zhengya; Blaauw, David; Sylvester, Dennis

doi:10.1109/isscc.2014.6757494

Cited by 48 publications

(25 citation statements)

References 3 publications

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“…With each change in computing platforms, the number of devices per person has also grown, opening wide the semiconductor market. Based on the extrapolation of this observed trend, we expect wide-spread use of mm-scale computing platforms for environmental monitoring, medical implants and surveillance in the next decade [2][3][4].…”

Section: Introductionmentioning

confidence: 90%

Millimeter-scale computing platform for next generation of Internet of Things

Jang

Choi

Shi

et al. 2016

2016 IEEE International Conference on RFID (RFID)

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The internet of things has enabled the communication of "smart" physical objects and improved the quality of machine service to humans. These achievements are based on the synergy of processing information collected by different sources. This trend will continue with the next class of computing platforms enabled by millimeter-scale sensors, diversifying the signal sources including biomedical, climate and surveillance targets. The key challenge of such systems is to achieve small die size, ultra-low power consumption, sufficient communication distance with the limited antenna size and accurate system duty-cycling.

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Section: Introductionmentioning

confidence: 90%

Millimeter-scale computing platform for next generation of Internet of Things

Jang

Choi

Shi

et al. 2016

2016 IEEE International Conference on RFID (RFID)

Self Cite

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“…The battery consumption depends on a number of factors including but not limited to the age of the pacemaker, the pacemaker work load and power settings. Typically the pacemaker consumes 10 -30 µW [75] and lithium battery replacement requires an open heart surgery, which results in significant surgical cost and risk to the patient. Thus, miniaturized glucose biofuel cells implanted inside the heart will utilize the glucose present in the blood stream to produce bioelectricity to power the pacemaker circuit.…”

Section: Conclusion and Future Outlook Application Of In Vivo Glucosementioning

confidence: 99%

Enzymatic Glucose Biofuel Cell and its Application

Slaughter¹,

Kulkarni²

2015

J Biochip Tissue Chip

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“…The entire system power efficiency can be further improved to implement a custom DSP like a SoC [20] V. CONCLUSION A sub-ECG acquisition IC integrating analog feature extraction is presented for a single-chamber leadless pacemaker application. The ASIC advances the state-of-the-art ECG readout front-end by consuming only 680 nA without compromising important performances such as CMRR 90 dB, PSRR 80 dB, and SNR 70 dB.…”

Section: Heartbeat Detection System Evaluationmentioning

confidence: 99%

A 680 nA ECG Acquisition IC for Leadless Pacemaker Applications

Yan

Harpe

Pamula

et al. 2014

IEEE Trans. Biomed. Circuits Syst.

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Abstract-A sub-ECG acquisition IC is presented for a single-chamber leadless pacemaker applications. It integrates a low-power, wide dynamic-range ECG readout front end together with an analog QRS-complex extractor. To save ASIC power, a current-multiplexed channel buffer is introduced to drive a 7 b-to-10 b self-synchronized SAR ADC which utilizes 4 fF/unit capacitors. The ASIC consumes only 680nA and achieves CMRR 90 dB, PSRR 80 dB, an input-referred noise of 4.9 in a 130 Hz bandwidth, and has rail-to-rail DC offset rejection. Low-power heartbeat detections are evaluated with the help of the ASIC acquiring nearly 20,000 beats across 10 different records from the MIT-BIH arrhythmia database. In the presence of muscle noise, both the average Sensitivity (Se) and Positive Predictivity (PP) show more than 90% when the input SNR 6 dB.

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24.3 An implantable 64nW ECG-monitoring mixed-signal SoC for arrhythmia diagnosis

Cited by 48 publications

References 3 publications

Millimeter-scale computing platform for next generation of Internet of Things

Millimeter-scale computing platform for next generation of Internet of Things

Enzymatic Glucose Biofuel Cell and its Application

A 680 nA ECG Acquisition IC for Leadless Pacemaker Applications

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