A predictive quantization based compressive sensing SAR ADC for ECG signal

Compressive sensing (CS) theory states that sparse signals can be sampled at a sub-Nyquist rate without information loss. The combination of CS and analog-to-digital converter (ADC) has demonstrated an effective reduction in the conversion rate. However, the majority of prior CS ADCs have not been successful in efficiently optimizing the quantization process. This paper proposes the predictive quantization method for the CS successive approximation register ADC. This approach not only reduces the conversion rate but also decreases the number of conversion bits. The pseudo-random sequence used in random demodulation, along with the previous value, serves as the basis for prediction to address the challenge that results from the randomness by mixing. With predictive quantization, the reduction in the number of bits per conversion contributes to improving overall power saving. The prototype circuit is designed in a 0.18-µm CMOS process. The simulation results indicate that the proposed ADC successfully reduces the average number of conversion bits to 6.97 bits for ECG signals when the resolution of ADC is 10 bits. There is approximately a 30% drop in the number of conversion bits.