7/26/2019 a 172uW comprssive sampling

1/3386 2016 IEEE International Solid-State Circuits Conference

ISSCC 2016 / SESSION 22 / SYSTEM AND INSTRUMENTS FOR HUMAN-MACHINE INTERFACES / 22.4

22.4 A 172W Compressive SamplingPhotoplethysmographic Readout with EmbeddedDirect Heart-Rate and Variability Extraction fromCompressively Sampled Data

Pamula Venkata Rajesh1,2, Jose Manuel Valero-Sarmiento3,Long Yan1, Alper Bozkurt3, Chris Van Hoof1,2, Nick Van Helleputte1,Refet Firat Yazicioglu1, Marian Verhelst2

1imec, Leuven, Belgium, 2KU Leuven, Leuven, Belgium,3North Carolina State University, Raleigh, NC

Heart rate (HR) and its variability (HRV) provide critical information about anindividuals cardiovascular and mental health state. In either application, long-term observation is crucial to arrive at conclusive decisions and provide usefuldiagnostic feedback [1]. Photoplethysmographic (PPG) estimation of HR and HRVhas emerged as an attractive alternative to ECG, as it provides electrode-freeoperation increasing patient comfort. However, PPG monitoring systems robustto low ambient light conditions and low perfusion conditions require a LED as alight source, which strongly dominates the power consumption of the completesystem. Compressive sampling (CS) based PPG readouts promise to mitigate thisLED power consumption [2], yet require large computational power to recoverthe signal, hindering real-time embedded processing on energy-scarce wearabledevices. This paper presents a fully integrated, low-power PPG readout ASIC,completely integrating a single-channel readout front-end (AFE) and a 12b SARADC and a digital back-end (DBE) for embedded energy-efficient real-timeinformation extraction, that advances the state-of-the-art on the following fronts:1) By smartly duty-cycling all system components synchronously on a sparsenon-uniform CS sampling pulse stream, the LED driver power is reduced up to30x, without significant loss of information. 2) Moreover, the necessity of wirelessoff-loading, or for computationally intensive embedded signal reconstruction, iscircumvented by enabling the direct extraction of HR and HRV information fromthe compressed data in real-time on the ASIC, while consuming only 172W forthe complete system.

Figure 22.4.1 shows the architecture of the developed PPG acquisition system.In a conventional acquisition system, the signal is sampled uniformly at afrequency fs,N. The LED driver is synchronized with the sampling clock and has aduty cycle (D) of TON fs,N, which determines its power consumption for a givendrive current (ILED). The minimum value of the LED on time, TON is set by thebandwidth of the front-end and the settling requirements, limiting the minimum

achievable power consumption. However, since the PPG signal is sparse in thefrequency domain, CS can enable a strong reduction of the LED duty cycle bynon-uniformly sub-sampling the signal [2]. This strongly reduces the averagesampling frequency fs,CS, proportionally reducing D and hence the LED driverpower consumption. The challenge here however lies in the required tightsynchronization of all system components, which is achieved in our ASICimplementation by fully integrating the AFE, ADC and a DBE, all controlled by thenon-uniform pulse generator for maximal power savings.

Figure 22.4.2 shows the architecture of the fully integrated AFE that includes atransimpedance amplifier (TIA) with programmable gain as the first stage followedby switched integrator (SI). Both TIA and SI use a two-stage Miller compensatedOTA with resistive and switched-capacitor feedback, respectively. The nmos activeload in the first stage is degenerated to minimize its noise contribution. Since thePPG signal, measured as the current at the AFE input, is characterized by a largestatic component and relatively small pulsatile component (AC) (typically 1%-4%

of the static component), a 5b current DAC (IDAC), capable of sourcing up to10A of current, removes the static component of the photocurrent at the TIAinput, thereby improving the channel dynamic range. The output of the TIA isintegrated on to a SI with programmable feedback capacitor to provide furthergain programmability and limit the noise aliasing, thereby improving the SNR.Both TIA and SI are enabled with an optional power-down mode to turn them offbetween successive sampling instants to further reduce power consumption. Amixed-signal feedback loop, comprised of a switched-capacitor low-pass filter(SC-LPF), comparators and an up-down counter, tracks the output DC level ofthe SI. A 5b control code, to control the LED drive current, is generated by thefeedback loop such that the DC output of the SI stays within the threshold values(Vrefmin and Vrefmax). A 12b SAR ADC samples the output of the SI at non-uniformsampling instants as defined by the DBE for further digital processing. Anintegrated sub-1V bandgap reference provides the necessary analog bias voltagesand currents.

A fully integrated DBE (Fig. 22.4.3) generates the control signals required for LEDdriver, AFE and the ADC and processes the CS data to extract HR and HRV. Thenon-uniform sampling instants corresponding to each compression ratio (CR)are stored in a lookup table (LUT) and are accessed at run-time for control-signalgeneration. A DMA transfers the incoming data from the ADC output into one ofthe two 512x12b banks of the data memory (DMEM) in a ping-pong manner.Every 4s, the feature extraction unit (FEU) is woken up to perform direct HR andHRV analysis on the buffered data, using Lomb-Scargle Periodogram (LSP) forleast squares spectral estimation in the compressed domain. The resulting outputspectrogram reveals the average HR over a period of 4s without preceding signal

reconstruction. For efficient execution, an 8-way multiply-accumulator (Fig.22.4.3) is implemented to accelerate the modified fixed-point 64-bin LSP. Afrequency range of 0.5-to-3.5Hz is covered for the spectral estimation with aresolution of 0.047Hz, thereby covering a range of 30-to-210bpm in HR with aresolution of 3bpm, conforming to the ANSI-AAMI standards for heart rate meters.A linear search is performed on the power spectral density (PSD) obtained fromthe LSP to determine the peak, which is further processed to extract the averageHR over the current 4s interval. HRV is obtained by monitoring the variation inthe HR over consecutive 4s intervals.

To characterize the ASIC, an external LED is modulated by a sine wave offrequency 1.2Hz (corresponding to 72bpm) to mimic a PPG signal (since PPGsignals are extremely sparse on frequency basis [2]) and the resultingphotocurrent is acquired for CRs of 8x and 30x (bottom of Fig. 22.4.4.), validatingthe timing controller of the DBE. Thanks to the presence of IDAC and autonomousmixed-signal feedback loop, the AFE can recover from channel saturation in the

event of increased optical coupling (for example due to motion) (top of Fig.22.4.4.). To characterize feature extraction performance of the DBE, the frequencyof the sine wave is swept from 0.5-to-3.4Hz and the feature extraction isperformed at 8x, 10x and 30x CRs. Figure 22.4.5 shows the extracted HR has aworst-case error of 10bpm at 30x compression for a nominal HR of 96bpm, whichis conformant to ANSI-AAMI accuracy specifications. An in-vivo acquisition ofPPG is performed with uniform sampling, through transmission pulseoximetryon the index finger (bottom left of Fig. 22.4.5, low pass filtered with 5Hz BW) andthe power breakdown for different CRs is shown in Fig. 22.4.5. It can be seen thatat higher CRs, the AFE power dominates the system power consumption, whichis fundamentally limited due to the noise requirements.

Figure 22.4.7 shows the die micrograph of the ASIC, implemented in a 0.18mCMOS process, which measures 4mm2.5mm. Compared to the state-of-the art(Fig. 22.4.6), the presented ASIC implements CS to reduce the relative LED powerconsumption up to 30X (from 1200W to 43W), while retaining relevant signal

information. Moreover, it integrates a full DBE capable of extracting HR and HRVdirectly from the CS data with minimum power penalty (7.2W), hence avoidingthe energy penalty of wireless transmission and/or embedded signalreconstruction. This unique combination of a fully-integrated non-uniformlysampled PPG readout, with embedded information extraction from CS data pavesthe way towards truly autonomous low-power PPG-based HR and HRV analyzersfor personal medical care.

Acknowledgements:J.M.V.S. and A.B. thank NSF NERC ASSIST (EEC-1160483) for partial funding.

References:[1] J. Wijsman et al., Towards Mental Stress Detection Using WearablePhysiological Sensors, IEEE EMBS, pp. 1798-1801, 2011.[2] V. R. Pamula et al., Computationally-Efficient Compressive Sampling for Low-Power Pulseoximeter System, IEEE Trans. BioCAS, pp. 69-72, Oct. 2014.

[3] M. Tavakoli et al., An ultra-low-power pulse oximeter implemented with anenergy-efficient transimpedance amplifier, IEEE Trans. BioCAS, vol. 4, no. 1, pp.27-38, Feb. 2010.[4] M. Alhawari et al., A 0.5V

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Figure 22.4.1: System overview of the compressive sampling (CS) based PPGreadout for heart rate and variability monitoring. The implemented ASICsupports uniform sampling mode (1x) along with 8x, 10x and 30x non-uniformsampling compression modes.

Figure 22.4.2: Architecture of the Analog Front End and detailed schematic ofthe OTA used in TIA and SI.

Figure 22.4.3: (Top) High-level block diagram of the Digital Back End withtiming controller. (Bottom) Overview of the feature extraction unit.

Figure 22.4.5: (Top) Measured heart rate from the ASIC with the input frequencyswept from 0.5Hz-to-3.4Hz and (Bottom) the measured PPG signal throughtransmission pulseoximetry (left) and the measured power breakdown of theASIC and off-chip LED driver for different CRs (right). Figure 22.4.6: Comparison of the implemented ASIC with the state-of-the-art.

Figure 22.4.4: (Top) Measured channel saturation recovery using a sinusoidalinput and (Bottom) the measured AFE output with 8x compression (left) and 30xcompression (right).

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ISSCC 2016 PAPER CONTINUATIONS

Figure 22.4.7: Chip micrograph of the PPG ASIC with Integrated FeatureExtraction (4mm 2.5mm).