Biomedical signal processing IC for wearable healthcare system

Abstract

Wearable healthcare systems are highlighted as innovative solutions for personalized healthcare and lifestyle applications. Miniaturized and portable sensor systems can provide a comfortable and easy way to collect the data of the user’s personal health and exercise. Based on collected data, wearable healthcare systems are capable of diagnosis, treatment, and prevention of disease. In this dissertation, two different wearable healthcare systems are proposed including: 1) compact in-ear electroencephalogram (EEG) measurement system, and 2) portable electrical impedance tomography (EIT) imaging system. The compact in-ear EEG measurement system is proposed to support a new EEG measurement methodology, Ear-EEG. Ear-EEG shows small motion artifact with stable electrode contact, and provide perfect conditions to measure EEG. Therefore Ear-EEG Sensing IC is proposed to implement the compact in-ear EEG device. The Ear-EEG Sensing IC includes a power-efficient EEG readout and low-power BCC transceiver. Current Reusing Low Noise Amplifier (CRLNA) uses the current reusing technique for improving power efficiency. Bootstrapping DC Servo Loop (BDSL) can reject the high EDO that appears at the dry-contact electrodes while maintaining the low input referred noise. With CRLNA and BDSL, the proposed EEG LNA shows the state-of-the-art 8.8 PEF performance and 0.38 μV$_{rms}$ low input-referred noise even on a 350 mV EDO. In order to reduce the system power consumption, Dual Mode PGA (DMPGA) supports the dual-mode operation. In the sleep mode, the bandwidth is adjusted to 10-20 Hz for the accurate detection of wake-up signal. The power consumption of sleep mode is only 5.7 μW. The proposed IC occupies 8 mm$^2$ in 65-nm CMOS technology, and it is incorporated into the 6 g and 7 cm$^3$ earplug-size device. The peripheral electronics including an audio AFE and a speaker are also integrated for the ASSR based BCI operation. The classification accuracy of BCI was tested on 9 subjects, and the proposed system accomplished 84% accuracy on average. As a result, the first wearable BCI hardware, taking advantage of unobtrusive and robust Ear-EEG is implemented. The portable EIT imaging system can provide the only way to bring medical imaging technology into home. In this paper, the wide-band EIT imaging system is suggested for early breast cancer detection at home. To detect the small size of the tumor, EIT imaging IC is proposed satisfying the following features: wide-band operation to exploit the electrical characteristics of cancer cells, low noise impedance measurement to detect the small size of tumor, and phase compensation to reduce the image artifacts. Thanks to the Wideband Instrumentation Amplifier (WB-IA), a wide-bandwidth operation of 10MHz is achieved with a low noise level of 14.2nV/√Hz. Besides, WB-IA includes on-chip HPF using PFL, enabling the rail-to-rail DC offset rejection without increasing the noise level. Constant Voltage Source-based Driver (CVS-driver) enables a wide-bandwidth current injection by continuously updating the driving voltage with a current monitoring circuit. Phase Compensation Loop (PCL) automatically calibrates the phase errors produced by circuits to acquire accurate images without artifacts. The proposed EIT imaging IC achieves a small phase error of 4.32° at 10MHz, which is 92% lower than without PCL. EIT Imaging IC is fabricated with 65-nm CMOS technology, and the EIT measurement system is implemented with a credit card size system board. The control of system and image reconstruction are performed with a mobile environment including smartphones and tablets. 0.5 cm target object is detected in both 2D and 3D images using in-vitro measurements. Therefore, the easy and convenient home imaging system is successfully assembled and verified to utilize for early breast cancer detection.