This paper presents a MUlti-SEnsor biomedical IC (MUSEIC). It features a high-performance, low-power analog front-end (AFE) and fully integrated DSP. The AFE has three biopotential readouts, one bio-impedance readout, and support for general-purpose analog sensors The biopotential readout channels can handle large differential electrode offsets ( ${\pm} $ 400 mV), achieve high input impedance ( ${>}$ 500 M $\Omega$ ), low noise ( ${ 620 nVrms in 150 Hz), and large CMRR ( ${>}$ 110 dB) without relying on trimming while consuming only 31 $\mu$ W/channel. In addition, fully integrated real-time motion artifact reduction, based on simultaneous electrode-tissue impedance measurement, with feedback to the analog domain is supported. The bio-impedance readout with pseudo-sine current generator achieves a resolution of 9.8 m $\Omega$ / $\surd$ Hz while consuming just 58 $\mu$ W/channel. The DSP has a general purpose ARM Cortex M0 processor and an HW accelerator optimized for energy-efficient execution of various biomedical signal processing algorithms achieving 10 $\times$ or more energy savings in vector multiply-accumulate executions.

A 345 µW Multi-Sensor Biomedical SoC With Bio-Impedance, 3-Channel ECG, Motion Artifact Reduction, and Integrated DSP

This paper describes a mixed-signal ECG System-on-Chip (SoC) that is capable of implementing configurable functionality with low-power consumption for portable ECG monitoring applications. A low-voltage and high performance analog front-end extracts 3-channel ECG signals and single channel electrode-tissue-impedance (ETI) measurement with high signal quality. This can be used to evaluate the quality of the ECG measurement and to filter motion artifacts. A custom digital signal processor consisting of 4-way SIMD processor provides the configurability and advanced functionality like motion artifact removal and R peak detection. A built-in 12-bit analog-to-digital converter (ADC) is capable of adaptive sampling achieving a compression ratio of up to 7, and loop buffer integration reduces the power consumption for on-chip memory access. The SoC is implemented in 0.18 $\mu$ m CMOS process and consumes 32 $\mu$ W from a 1.2 V while heart beat detection application is running, and integrated in a wireless ECG monitoring system with Bluetooth protocol. Thanks to the ECG SoC, the overall system power consumption can be reduced significantly.

A Configurable and Low-Power Mixed Signal SoC for Portable ECG Monitoring Applications

This paper presents an ultra-low-power event-driven analog-to-digital converter (ADC) with real-time QRS detection for wearable electrocardiogram (ECG) sensors in wireless body sensor network (WBSN) applications. Two QRS detection algorithms, pulse-triggered (PUT) and time-assisted PUT (t-PUT), are proposed based on the level-crossing events generated from the ADC. The PUT detector achieves 97.63% sensitivity and 97.33% positive prediction in simulation on the MIT-BIH Arrhythmia Database. The t-PUT improves the sensitivity and positive prediction to 97.76% and 98.59% respectively. Fabricated in 0.13 $\mu {\rm m}$ CMOS technology, the ADC with QRS detector consumes only 220 nW measured under 300 mV power supply, making it the first nanoWatt compact analog-to-information (A2I) converter with embedded QRS detector.

A 300-mV 220-nW Event-Driven ADC With Real-Time QRS Detection for Wearable ECG Sensors

This paper presents an overview of the fundamentals and state of the-art in noninvasive physiological monitoring instrumentation with a focus on electrode and optrode interfaces to the body, and micropower-integrated circuit design for unobtrusive wearable applications. Since the electrode/optrode-body interface is a performance limiting factor in noninvasive monitoring systems, practical interface configurations are offered for biopotential acquisition, electrode-tissue impedance measurement, and optical biosignal sensing. A systematic approach to instrumentation amplifier (IA) design using CMOS transistors operating in weak inversion is shown to offer high energy and noise efficiency. Practical methodologies to obviate 1/f noise, counteract electrode offset drift, improve common-mode rejection ratio, and obtain subhertz high-pass cutoff are illustrated with a survey of the state-of-the-art IAs. Furthermore, fundamental principles and state-of-the-art technologies for electrode-tissue impedance measurement, photoplethysmography, functional near-infrared spectroscopy, and signal coding and quantization are reviewed, with additional guidelines for overall power management including wireless transmission. Examples are presented of practical dry-contact and noncontact cardiac, respiratory, muscle and brain monitoring systems, and their clinical applications.

Integrated Circuits and Electrode Interfaces for Noninvasive Physiological Monitoring

Windows into human health through wearables data analytics

A motion artifact removal method with a two-stage cascade LMS adaptive filter is proposed for an ambulatory ECG monitoring system. The first LMS stage consisting of analog feedback prevents the signal saturation to reduce the input dynamic range. An adaptive step-size LMS algorithm is introduced and employed for the second LMS stage. The adaptive step-size algorithm can achieve fast convergence to track large sudden motion artifact quickly, while preventing the distortion of the ECG component. The filtering performance is evaluated by the heart beat detection, measured by sensitivity (Se) and positive predictive value (+p), and the performance is increased by 9.8% and 6.48%, respectively, compared to the unfiltered signal at the worst case with -25dB SNR. The proposed motion artifact method is implemented on an ambulatory ECG monitoring module, and the real-time measurement shows a significant performance improvement.

Motion artifact removal using cascade adaptive filtering for ambulatory ECG monitoring system

The performance of several adaptive filter (AdF) algorithm implementations was investigated in the context of cleaning noisy ambulatory ECGs. Together with a noisy ECG signal, both body movement measured with accelerometers and skin-electrode impedance (SEI) were considered as reference signals to the AdF. ECG with artificial motion artifacts were generated by combining clean ECGs with noise signals. Several implementations and combinations of AdFs, and two reference signals (accelerometers and SEI) were investigated. Performance was measured by evaluating the output (sensitivity (Se) and positive predictivity (+P)) of a beat detection (BD) algorithm. Using AdF algorithm improved the performance of a BD algorithm as compared to non-filtering. SEI used as reference signal outperformed accelerometers. A variant of LMS, LMS sign-error, gave the best performance from all implementations considered. However, distortion observed in the filtered signal is high and therefore, these results cannot be extended to other features within the ECG.

http://cinc.mit.edu/archives/2012/pdf/0045.pdf

Adaptive filtering in ECG denoising: A comparative study

Clinical validation of a low-power and wearable ECG patch for long term full-disclosure monitoring.

Ambulatory monitoring of cardiac signals is an important diagnostic method for the detection and analysis of cardiac arrhythmia. Key technical challenges are overcoming motion artifacts, achieving low power consumption, and creating a low cost, low maintenance system. A wireless ECG monitoring system that is able to perform good quality ECG signal acquisition as well as subsequent digital signal processing for motion artifact reduction is presented. An ultra-low power integrated circuit performs acquisition of three ECG leads and electrode-tissue impedance for one lead. Also integrated is imec's CoolBio Digital Signal Processor, which is capable of performing Independent Component Analysis algorithm as well as Adaptive Filtering using electrode-tissue impedance impedance. Both algorithms, which are used for motion artifact reduction, result in a system power consumption of approximately 7.6mW. The novel contribution is the integration of a low power DSP into a wearable ECG platform, which includes lessons learnt on interfacing between low power components to reduce system power consumption.

Wireless 3-lead ECG system with on-board digital signal processing for ambulatory monitoring

Noise from motion artifacts is currently one of the main challenges in the field of ambulatory ECG recording. To address this problem, we propose the use of two different approaches. First, an adaptive filter with electrode-skin impedance as a reference signal is described. Secondly, a multi-channel ECG algorithm based on Independent Component Analysis is introduced. Both algorithms have been designed and further optimized for real-time work embedded in a dedicated Digital Signal Processor. We show that both algorithms improve the performance of a beat detection algorithm when applied in high noise conditions. In addition, an efficient way of choosing this methods is suggested with the aim of reduce the overall total system power consumption.

Di Geng

Papers

Motion artifact removal using cascade adaptive filtering for ambulatory ECG monitoring system

Adaptive filtering in ECG denoising: A comparative study

Clinical validation of a low-power and wearable ECG patch for long term full-disclosure monitoring.

Wireless 3-lead ECG system with on-board digital signal processing for ambulatory monitoring

An optimized DSP implementation of adaptive filtering and ICA for motion artifact reduction in ambulatory ECG monitoring