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294 • 2012 IEEE International Solid-State Circuits Conference ISSCC 2012 / SESSION 17 / DIAGNOSTIC & THERAPEUTIC TECHNOLOGIES FOR HEALTH / 17.2 17.2 A 259.6μW Nonlinear HRV-EEG Chaos Processor with Body Channel Communication Interface for Mental Health Monitoring Taehwan Roh, Sunjoo Hong, Hyunwoo Cho, Hoi-Jun Yoo KAIST, Daejeon, Korea Recently, there are many reports on the measurement of mental health condi- tions as derived from physiological parameters – see references in [1]. Usually, heart-rate or heart rate variability (HRV) has been used for monitoring mental health because of its strong dependency on the autonomic nervous system (ANS) [1-2]. Reference [1] reported that ECG, respiration, skin conductance and EMG data collected by means of wearable sensors was combined and analyzed to get 80% accurate detection of mental stress. Another important way to meas- ure stress is by the combination of HRV and EEG. It is claimed that HRV, which represents the effect of ANS, and EEG, which is the primary signal of the central nervous system (CNS), should be analyzed together at the same time in order to extract accurate physiological markers for mental stress and disorder. Using this approach there was a report of stress detection with 90% confidence [3]. In con- trast to the case of only HRV, however, this approach requires nonlinear analy- sis of EEG, which is possible only with a high performance computer system [4- 5]. Therefore, no mobile device for stress assessment as derived from the com- bination of the EEG and HRV has been previously reported. In this paper, we present a wearable mental health measurement system incor- porating the nonlinear analysis of physiological rhythm including HRV and EEG signals together for high accuracy. The proposed system is implemented in a 31g headband that measures scalp signals and performs nonlinear-chaotic analysis to measure the stress levels. Using a 1.2V 40mAhr coin-battery (11.7×5.35mm 2 1.7g), the proposed system is able to operate for more than 7 days. Figure 17.2.1 illustrates the proposed mental health monitoring system. It con- sists of two parts: a headband, which has a monitoring chip wire-bonded on it, and 6 dry electrodes made of planar-fashionable circuit board (P-FCB) [6]. The headband has a 2-layer P-FCB and the snap button connector to interconnect with electrodes. The ground and reference electrodes are located on the points A1 and A2, respectively. The signal electrodes are located in Fp1, O1, and below T5 and T6. As the proposed system begins to sense the physiological signals from and near the scalp, the system analyzes the acquired signals to obtain the stress index. Then it passes the stress index to the external mobile phone or smart device through the BCC for feedback to the user. Figure 17.2.2 shows the overall architecture of the mental health monitoring IC which consists of: 1) a 4 channel analog front end including a reconfigurable amplifier and 10b SAR-ADC, 2) Independent Component Analysis (ICA) acceler- ator to extract the original EEG source from measured scalp signals, 3) NCA accelerator to extract nonlinear-chaotic feature embedded in the acquired sig- nals, 4) BCC for communication between the proposed system and the external devices distant from the system, and 5) RSD for low-power operation mode without RISC operation. RSD has a routing table and 16 inter-block stream shells (ISSs) by which the RISC can configure the IC in start-up. All the periph- eral blocks can share the input/output with neighbor blocks by ISS, which is composed of input/output FIFOs and routing switches. Figure 17.2.3 is the circuit schematic diagram of the sensor front-end and ADC with its gain control characteristics. The EEG signals are acquired from P-FCB electrodes attached on the scalp. Since the P-FCB has a relatively high contact resistance, the sensor gain is designed to be greater than 70 dB, and in this design, it is measured as max. 77.12dB. The gain of the first stage is set to 40dB and the gain of the second stage is programmable from 14dB to 40dB to cover the full scale of the ADC input that follows. The measurement of the total gain is 52.69dB to 77.12dB with 0.6-105Hz of bandwidth; the discrepancy may come from variation of P-MOS pseudo-resistor values. A 10b fully-differential SAR ADC is implemented for canceling the high offset caused by noise from the P- FCB to the input of ADC. Its ENOB is measured as 8.4. Figure 17.2.4 shows the ICA accelerator for signal enhancement and data reduc- tion. The 4 channel 10b signals acquired by the sensor front-end are the mixture of several internal signals inside the brain and body. The ICA 1) enhances HRV extraction, 2) removes noise, and 3) achieves noise-free EEG signals. As a result of ICA, the signals are divided into 4 independent components (IC) according to its non-Gaussian characteristics. The first IC represents the ECG signal which is distorted but sufficient to extract the heart rhythm. Compared with the extracted HRV from the ECG signal, the HRV from the first IC has only a 1.84% root-mean- square difference (PRD) while the HRV from scalp signals has 4 times more PRD. The second IC is discarded as a noise source. The third and fourth ICs show the EEG sources, which will be analyzed with the nonlinear-chaotic algo- rithm. Figure 17.2.5 shows the NCA accelerator for nonlinear feature extraction from the EEG signal. The nonlinear characteristics of EEG signals are analyzed and transformed into a nonlinear index to distinguish the mental states [7]. Chaotic feature extraction is composed of 3 steps: 1) constructing an attractor, 2) scan- ning the attractor, and 3) calculating the chaotic index. The attractor scan requires the most computational overhead for nonlinear analysis. Block-set attractor memory (BAM) enlarges the parallelism of hardware for NCA to scan one vector of the attractor in a clock. As a result of BAM, the cycles required for extraction equal the size of the attractor. The maximum cycles for NCA is 1030 clocks, including the largest Lyapunov exponent (LLE) extraction or nonlinear- chaotic index, while a RISC core needs more than 1.6M cycles. The bottom of Fig. 17.2.5 shows the nonlinear chaotic analysis results when the Colorado State University dataset is input to the NCA directly by-passing the ADC [8]. The dataset is the EEG signals of 5 people while they were doing 5 tasks including the state of taking a rest. Chaotic characteristics of each task have different val- ues of LLE. Compared with other tasks, the smallest LLE is measured by NCA when the subjects take a rest. Figure 17.2.6 shows the BCC transceiver for transmitting the extracted data to the mobile device for visual feedback. In order to transmit the data with low- power consumption for multiple users, direct sequence spread spectrum (DSSS) is adopted with duty-cycled BCC transceiver. It operates in 30-120MHz band with a LC digitally controlled oscillator (LC-DCO). Its start-up time is at most 100ns. In order to communicate with only one electrode, a single-to-dif- ferential LNA is adopted. Its voltage gain is 22dB and consumes 0.91mW. On the bottom left of Fig. 17.2.6, the comparison of power consumption is shown for the various operation modes. The first mode is the raw data acquisition mode where the scalp signal is transmitted directly from sensors to BCC transceiver without any signal processing. When the NCA is activated to separate nonlinear features, the overall power dissipation is reduced to 259.6μW, which is only 1/2 of that of the data acquisition mode. Figure 17.2.7 shows the chip micrograph and its performance summary. The 5×2.35mm 2 chip is fabricated in a 0.13μm 1P8M RF CMOS process. The pro- posed health monitoring system is verified by the test of 10 subjects (8 males and 2 females, mean age of 24.8). The nonlinear-chaotic analysis where the sub- jects were taking a rest and solving problems, repeatedly, demonstrates the pre- dicted correlation among 9 subjects’ EEG signals with 90% confidence. References: [1] J. Wijsman, et al., “Towards Mental Stress Detection Using Wearable Physiological Sensors,” IEEE EMBS, pp. 1798-1801, 2011. [2] R. McCraty, et al., “The Effects of Emotions on Short-Term Power Spectrum Analysis of Heart Rate Variability,” The American Journal of Cardiology, vol. 76, pp. 1089-1093, 1995. [3] H. Abdullah, et al., “Correlation of Sleep EEG Frequency Bands and Heart Rate Variability,” IEEE EMBS, pp. 5014-5017, 2009. [4] J. Corsini, et al., “Epileptic Seizure Predictability from Scalp EEG Incorporating Constrained Blind Source Separation,” IEEE TBME, Vol. 53, No. 5, pp. 790 - 799, 2006. [5] C.J. Stam, “Nonlinear dynamical analysis of EEG and MEG: Review of an emerging field,” Clinical Neurophysiology, vol. 116, pp. 2266-2301, 2005. [6] L. Yan, et al., “A 3.9mW 25-Electrode Reconfigured Thoracic Impedance/ECG SoC with Body-Channel Transponder,” ISSCC Digest Tech. Papers, pp. 490-491, 2010. [7] X. Wang, et al., “Research on the relation of EEG signal chaos characteristics with high-level intelligence activity of human brain,” Nonlinear Biomedical Physics, vol.4 no.2, pp. 1-10, 2010. [8] C.W. Anderson and M. Kirby, “Classification of Electroencephalogram (EEG) Signals for Brain-Machine Interfaces,” http://www.cs.colostate.edu/eeg. 978-1-4673-0377-4/12/$31.00 ©2012 IEEE
