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 mentalhealth because of its strong dependency on the autonomic nervous system(ANS) [1-2]. Reference [1] reported that ECG, respiration, skin conductance andEMG data collected by means of wearable sensors was combined and analyzedto 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, whichrepresents the effect of ANS, and EEG, which is the primary signal of the centralnervous system (CNS), should be analyzed together at the same time in order toextract accurate physiological markers for mental stress and disorder. Using thisapproach 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 EEGsignals together for high accuracy. The proposed system is implemented in a31g headband that measures scalp signals and performs nonlinear-chaoticanalysis to measure the stress levels. Using a 1.2V 40mAhr coin-battery(11.7×5.35mm2 1.7g), the proposed system is able to operate for more than 7days.

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]. Theheadband has a 2-layer P-FCB and the snap button connector to interconnectwith electrodes. The ground and reference electrodes are located on the pointsA1 and A2, respectively. The signal electrodes are located in Fp1, O1, and belowT5 and T6. As the proposed system begins to sense the physiological signalsfrom and near the scalp, the system analyzes the acquired signals to obtain thestress index. Then it passes the stress index to the external mobile phone orsmart device through the BCC for feedback to the user.

Figure 17.2.2 shows the overall architecture of the mental health monitoring ICwhich consists of: 1) a 4 channel analog front end including a reconfigurableamplifier and 10b SAR-ADC, 2) Independent Component Analysis (ICA) acceler-ator to extract the original EEG source from measured scalp signals, 3) NCAaccelerator to extract nonlinear-chaotic feature embedded in the acquired sig-nals, 4) BCC for communication between the proposed system and the externaldevices distant from the system, and 5) RSD for low-power operation modewithout RISC operation. RSD has a routing table and 16 inter-block streamshells (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 iscomposed of input/output FIFOs and routing switches.

Figure 17.2.3 is the circuit schematic diagram of the sensor front-end and ADCwith its gain control characteristics. The EEG signals are acquired from P-FCBelectrodes attached on the scalp. Since the P-FCB has a relatively high contactresistance, the sensor gain is designed to be greater than 70 dB, and in thisdesign, it is measured as max. 77.12dB. The gain of the first stage is set to 40dBand the gain of the second stage is programmable from 14dB to 40dB to coverthe full scale of the ADC input that follows. The measurement of the total gain is52.69dB to 77.12dB with 0.6-105Hz of bandwidth; the discrepancy may comefrom variation of P-MOS pseudo-resistor values. A 10b fully-differential SARADC 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 mixtureof 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 resultof ICA, the signals are divided into 4 independent components (IC) according toits non-Gaussian characteristics. The first IC represents the ECG signal which isdistorted but sufficient to extract the heart rhythm. Compared with the extractedHRV 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 morePRD. The second IC is discarded as a noise source. The third and fourth ICsshow 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 fromthe EEG signal. The nonlinear characteristics of EEG signals are analyzed andtransformed into a nonlinear index to distinguish the mental states [7]. Chaoticfeature extraction is composed of 3 steps: 1) constructing an attractor, 2) scan-ning the attractor, and 3) calculating the chaotic index. The attractor scanrequires the most computational overhead for nonlinear analysis. Block-setattractor memory (BAM) enlarges the parallelism of hardware for NCA to scanone vector of the attractor in a clock. As a result of BAM, the cycles required forextraction equal the size of the attractor. The maximum cycles for NCA is 1030clocks, including the largest Lyapunov exponent (LLE) extraction or nonlinear-chaotic index, while a RISC core needs more than 1.6M cycles. The bottom ofFig. 17.2.5 shows the nonlinear chaotic analysis results when the Colorado StateUniversity dataset is input to the NCA directly by-passing the ADC [8]. Thedataset is the EEG signals of 5 people while they were doing 5 tasks includingthe 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 NCAwhen the subjects take a rest.

Figure 17.2.6 shows the BCC transceiver for transmitting the extracted data tothe 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 30-120MHzband with a LC digitally controlled oscillator (LC-DCO). Its start-up time is atmost 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 thebottom left of Fig. 17.2.6, the comparison of power consumption is shown forthe various operation modes. The first mode is the raw data acquisition modewhere the scalp signal is transmitted directly from sensors to BCC transceiverwithout any signal processing. When the NCA is activated to separate nonlinearfeatures, the overall power dissipation is reduced to 259.6µW, which is only 1/2of that of the data acquisition mode.

Figure 17.2.7 shows the chip micrograph and its performance summary. The5×2.35mm2 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 malesand 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 WearablePhysiological Sensors,” IEEE EMBS, pp. 1798-1801, 2011.[2] R. McCraty, et al., “The Effects of Emotions on Short-Term Power SpectrumAnalysis 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 HeartRate Variability,” IEEE EMBS, pp. 5014-5017, 2009.[4] J. Corsini, et al., “Epileptic Seizure Predictability from Scalp EEGIncorporating 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 anemerging field,” Clinical Neurophysiology, vol. 116, pp. 2266-2301, 2005.[6] L. Yan, et al., “A 3.9mW 25-Electrode Reconfigured Thoracic Impedance/ECGSoC 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 characteristicswith high-level intelligence activity of human brain,” Nonlinear BiomedicalPhysics, 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

295DIGEST OF TECHNICAL PAPERS •

ISSCC 2012 / February 21, 2012 / 2:00 PM

Figure 17.2.1: Wearable mental health monitoring headband system.Figure 17.2.2: Overall block diagram and Inter-block Stream Shell (ISS) forlow-power operation.

Figure 17.2.3: Schematics of reconfigurable gain two-stage analog front-endand fully differential SAR ADC.

Figure 17.2.5: BAM-based nonlinear-chaotic analysis and its performance.Figure 17.2.6: LC-DCO based duty-cycled BCC transceiver and the systempower consumption.

Figure 17.2.4: Block diagram of independent component analysis and its performance.

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• 2012 IEEE International Solid-State Circuits Conference 978-1-4673-0377-4/12/$31.00 ©2012 IEEE

ISSCC 2012 PAPER CONTINUATIONS

Figure 17.2.7: Chip micrograph and its performance summary.