Complementary metal oxide semiconductor electrocardiogram amplifier for low power wearable cardiac screening

• Main Author: Ow, Tze Weng
• Format: Thesis
• Language: English
• Published: 2016
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://eprints.utm.my/id/eprint/78391/1/OwTzeWengMFKE2016.pdf; http://eprints.utm.my/id/eprint/78391/; http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:94014
• Institution: Universiti Teknologi Malaysia

Cardiovascular disease is the number one killer disease in Malaysia. Although sudden cardiac arrest is the main cause of death, the Malaysian awareness of towards cardiovascular disease is still low. The trend of health care screening devices in the world is increasingly towards the favor of portability and wearability, especially in the most common electrocardiogram (ECG) monitoring system. This is because these wearable screening devices are not restricting the patient’s freedom and daily activities. While the demand of low power and low cost biomedical system on chip (SoC) is increasing in exponential way, the front end ECG amplifiers are still suffering from flicker noise for low frequency cardiac signal acquisition, 50 Hz power line electromagnetic interference, and the large unstable input offsets due to the electrodeskin interface is not attached properly. In this project, a high performance ECG amplifier that suitable for low power wearable cardiac screening is proposed. The amplifier adopts the highly stable folded cascode topology and later being implemented into RC feedback circuit for low frequency DC offset cancellation. By using 0.13 µm CMOS technology from Silterra, the simulation results show that this front end circuit can achieve a very low input referred noise of 1 pV=pHz and high common mode rejection ratio (CMRR) of 174.05 dB. It also gives voltage gain of 75.45 dB with good power supply rejection ratio (PSSR) of 92.12 dB. The total power consumption is only 3 µW and thus suitable to be implemented with further signal processing and classification back end for low power biomedical SoC.