In this paper, an asynchronous analog-to-information conversion system is introduced for measuring the RR intervals of the electrocardiogram (ECG) signals. The system contains a modified level-crossing analog-to-digital converter and a novel algorithm for detecting the R-peaks from the level-crossing sampled data in a compressed volume of data. Simulated with MIT-BIH Arrhythmia Database, the proposed system delivers an average detection accuracy of 98.3%, a sensitivity of 98.89%, and a positive prediction of 99.4%. Synthesized in 0.13 μm CMOS technology with a 1.2 V supply voltage, the overall system consumes 622 nW with core area of 0.136 mm (2), which make it suitable for wearable wireless ECG sensors in body-sensor networks.

Abstract-In this paper, an in-depth design methodology for fully-integrated tunable low-noise amplifiers for neural recording applications is presented. In this methodology, a modified system design is proposed to optimize the area/noise/linearity performance. A novel linear pseudo-resistor with a wide range of tunability is also proposed. As a case study, a low-noise tunable and reconfigurable amplifier for neural recording applica-

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