Low Noise, High Input Impedance Digital-Analog Hybrid Offset Suppression Amplifier for Wearable Dry Electrode ECG Monitoring

Abstract

The portable real-time electrocardiogram (ECG) is a convenient and promising electronic device for cardiovascular diseases patients. However, unlike wet gel electrodes in traditional clinical applications, dry electrodes are competent for comfortable long-time wearing and can prevent skin ulceration. Its ultra-high source impedance and electrode offset (EOS) make traditional chopper amplifiers with low input impedance and limited EOS range difficult to apply to this area. To overcome these challenges, this paper proposes a novel chopper amplifier topology. This architecture includes a gain control loop, a ripple reduction loop, and a DC-servo loop (DSL). The proposed sampling input stage and digital-analog hybrid DSL are employed to boost input impedance and extend the EOS handing range. Designed with a 0.18 µm 1P6M 1.8 V CMOS salicide process, the proposed chopper capacitively coupled instrumentation amplifier achieves an ultra-high input impedance of 120 GΩ (<0.05 Hz) or 2.1 GΩ (0.6~250 Hz), an EOS handing range of ±325 mV and a low noise of 1.9 μVrms at 0.6~250 Hz. It occupies an area of 0.36 mm2 and only consumes a quiescent current of 11 μA.

A Tunable Gain and Bandwidth Low-Noise Amplifier with 1.44 NEF for EMG and EOG Biopotential Signal

“…To further reduce the noise levels, other LNAs use chopper modulation techniques [9,[11][12][13]15,20]. By implementing choppers, the 1/f noise and dc offset are attenuated.…”

“…The most common way to increase the input impedance is by using an impedance boosting loop [9] or a positive feedback loop [12,18]. An innovative form was developed in [20], which uses an input sampling input stage and digital-analog hybrid dc servo loop. As for chopping ripple suppression, it usually used a ripple reduction loop [12,20]; alternatively, a strategy can be the implementation of an auto-zero offset cancelation loop [18].…”

Wearable Ecg Device and Machine Learning for Heart Monitoring

“…Several approaches have been offered in some researches to reduce the noise in wearable ECG systems. For example, digital filters [14,15] are employed to remove unwanted noise components, including high -frequency interference (electrical interference from household appliances) low -frequency artifacts as patient's movement or respiratory activity. The paper [16] offered methods for eliminating basic lines to remove slowly changing signals caused by electrodes, signal issues and other sources of artifacts.…”