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A Low-Power On-Chip ECG Monitoring System Based on MWCNT/PDMS Dry Electrodes

Nishat T. Tasneem, Salvatore A. Pullano, Costantino Davide Critello, Antonino S. Fiorillo, Ifana Mahbub

2020IEEE Sensors Journal46 citationsDOI

Abstract

Wearable technologies have established novel strategies for continuous electrocardiogram (ECG) monitoring, exploiting recent developments in sensors and integrated circuit technology. Advances in dry electrodes fabrication improved the growth of wearable devices. Herein, we present an on-chip integrated ECG signal acquisition system along with dry electrodes for wearable and long-term monitoring. Custom electrodes are fabricated using multiwalled carbon nanotube (MWCNT) and Polydimethylsiloxane (PDMS) composite on a flexible substrate. A 1.5 μW fully-differential operational transconductance-amplifier (OTA) with a capacitive-resistive-feedback network is designed in 130 nm CMOS process achieving a mid-band gain of 43.09 dB and an input-referred noise of 2.81 μVrms in the range from 0.175 Hz to 1.636 kHz. Reconfigurability of the lower cut-off frequency is achieved by controlling the bias voltage of the triple-well nMOS transistors acting as the feedback pseudoresistor. Electrodes with different surface area and MWCNT concentrations were investigated in a real-time ECG measurement scenario. The recorded signals achieve a high signal-to-noise ratio which spans from 35.7 dB to 38.6 dB with the increasing MWCNT concentration. Experimental results demonstrate that the proposed system allows for high performance and low-power ECG signal recording for the long-term and wearable applications.

Topics & Concepts

Materials scienceTransconductanceNMOS logicCapacitive sensingAmplifierReconfigurabilityNoise (video)Electronic engineeringSIGNAL (programming language)Electrical engineeringChipCMOSOptoelectronicsComputer scienceTransistorVoltageEngineeringTelecommunicationsImage (mathematics)Programming languageArtificial intelligenceAnalog and Mixed-Signal Circuit DesignAdvanced Sensor and Energy Harvesting MaterialsNeuroscience and Neural Engineering
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