Nick Van Helleputte

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

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 μm CMOS process and consumes 32 μW 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 345 µW Multi-Sensor Biomedical SoC With Bio-Impedance, 3-Channel ECG, Motion Artifact Reduction, and Integrated DSP

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 ( ±400 mV), achieve high input impedance ( >500 M Ω), low noise ( 620 nVrms in 150 Hz), and large CMRR ( >110 dB) without relying on trimming while consuming only 31 μW/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 Ω/ √Hz while consuming just 58 μW/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 × or more energy savings in vector multiply-accumulate executions.

A reconfigurable, 0.13µm CMOS 110pJ/pulse, fully integrated IR-UWB receiver for communication and sub-cm ranging

Wireless sensor networks have recently attracted increased research interest. An important enabler is a specialized energy-efficient, scalable radio. Typically modest data rates (≪1Mb/s) over a short radio range (≪10m) with accurate indoor ranging to identify and locate sensing nodes, are required at an extremely low energy consumption. A high degree of flexibility is needed at a small energy overhead in order to tailor the power-performance trade-off for each application specifically and to adapt optimally to the operating conditions.

18.3 A multi-parameter signal-acquisition SoC for connected personal health applications

Connected personal healthcare, or Telehealth, requires smart, miniature wearable devices that can collect and analyze physiological and environmental parameters during a users daily routine. To truly support emerging applications (Fig. 18.3.1), a generic platform is needed that can acquire a multitude of sensor modalities and has generic energy-efficient signal processing capabilities. SoC technology gives significant advantages for miniaturization. But meeting low-power, medical grade signal quality, multi-sensor support and generic signal processing is still a challenge. For instance, [1] demonstrated a multi-sensor interface but it lacks support for efficient on-chip signal processing and doesnt have a high performance AFE. [2] showed a very low power signal processor but without support for multi-sensor interfacing. [3] presented a highly integrated SoC but lacking power efficiency. This paper demonstrates a highly integrated low-power SoC with enough flexibility to support many emerging applications. A wide range of sensor modalities are supported including 3-lead ECG and bio-impedance via high-performance and low-power AFE. The ARM Cortex™ M0 processor and matrix-multiply-accumulate accelerator can execute numerous biomedical signal processing algorithms (e.g. Independent Component Analysis (ICA), Principal Component Analysis (PCA,) CWT, feature extraction/classification, etc.) in an energy efficient way without sacrificing flexibility. The diversity in supported modalities and the generic processing capabilities, all provided in a single-chip low-power solution, make the proposed SoC a key enabler for emerging personal health applications (Fig. 18.3.1).

Motion artifact reduction in ambulatory ECG monitoring: an integrated system approach

Recent advances in low-power micro-electronics are revolutionizing ECG monitoring. Wearable patches now allow comfortable monitoring over several days. Achieving reliable and high integrity recording however remains a challenge, especially under daily-life activities. In this paper we present a system approach to motion artifact reduction in ambulatory recordings. A custom ultra-low-power ECG analog front-end read-out for simultaneous measurement of ECG and electrode-tissue impedance, from the same electrode, is reported. Integrating this front-end, we describe a wireless patch for the monitoring of 3-lead ECG, electrode electrical artifact and 3D-acceleration. Beyond ECG monitoring, this wireless patch provides the additional necessary data to filter out motion artifact. Two algorithm methods are tested. The first method applies ICA for de-noising multi-lead ECG recordings. The second method is an adaptive filter that uses skin/electrode impedance as the measurement of noise. Algorithms, circuits and system provide a platform for reliable ECG monitoring on-the-move.

An ECG patch combining a customized ultra-low-power ECG SoC with Bluetooth low energy for long term ambulatory monitoring

This paper presents the development of an ECG patch aiming at long term patient monitoring. The use of the recently standardized Bluetooth Low Energy (BLE) technology, together with a customized ultra-low-power ECG System on Chip (ECG SoC). including Digital Signal Processing (DSP) capabilities, enables the design of ultra low power systems able to continuously monitor patients, performing on board signal processing such as filtering, data compression, beat detection and motion artifact removal along with all the advantages provided by a standard radio technology such as Bluetooth. Early tests show how combining the ECG SoC and BLE leads to a total current consumption of only 500μA at 3.7V, while computing beat detection and transmitting heart rate remotely via BLE. This allows up to one month lifetime with a 400mAh Li-Po battery.

22.4 A 172µW compressive sampling photoplethysmographic readout with embedded direct heart-rate and variability extraction from compressively sampled data

Heart rate (HR) and its variability (HRV) provide critical information about an individuals cardiovascular and mental health state. In either application, long-term observation is crucial to arrive at conclusive decisions and provide useful diagnostic feedback [1]. Photoplethysmographic (PPG) estimation of HR and HRV has emerged as an attractive alternative to ECG, as it provides electrode-free operation increasing patient comfort. However, PPG monitoring systems robust to low ambient light conditions and low perfusion conditions require a LED as a light source, which strongly dominates the power consumption of the complete system. Compressive sampling (CS) based PPG readouts promise to mitigate this LED power consumption [2], yet require large computational power to recover the signal, hindering real-time embedded processing on energy-scarce wearable devices. This paper presents a fully integrated, low-power PPG readout ASIC, completely integrating a single-channel readout front-end (AFE) and a 12b SAR ADC and a digital back-end (DBE) for embedded energy-efficient real-time information extraction, that advances the state-of-the-art on the following fronts: 1) By smartly duty-cycling all system components synchronously on a sparse non-uniform CS sampling pulse stream, the LED driver power is reduced up to 30x, without significant loss of information. 2) Moreover, the necessity of wireless off-loading, or for computationally intensive embedded signal reconstruction, is circumvented by enabling the direct extraction of HR and HRV information from the compressed data in real-time on the ASIC, while consuming only 172μW for the complete system.

Real time digitally assisted analog motion artifact reduction in ambulatory ECG monitoring system

This paper proposes a real time digitally assisted analog motion artifact reduction ASIC with ECG measurement simultaneously. It features one ECG monitoring and in- and quad-phase electrode-skin impedance measurement, which are used to estimate motion artifacts. The implemented ASIC is capable of actual motion artifact reduction in the analog domain before final amplification.

28.4 A battery-powered efficient multi-sensor acquisition system with simultaneous ECG, BIO-Z, GSR, and PPG

This paper reports a battery-powered, multi-parameter recording platform with built-in support for concurrent ECG, Bio-Impedance (BIO-Z), Galvanic Skin Response (GSR) and Photoplethysmogram (PPG). The expanded list of dedicated sensor modalities provides a more accurate, more reliable and broader health assessment in wearable electronics. Since data is collected on one chip, precise synchronization between data streams is possible, allowing to use correlation techniques between the data streams. It supports, e.g., research on blood pressure estimation by combining ECG and PPG measurements through pulse arrival time analysis. Combining different sensing modalities like ECG, PPG, and BIO-Z can result in better estimation of hemodynamic parameters, as well as heartbeat and heart-rate variability.

A 160μA biopotential acquisition ASIC with fully integrated IA and motion-artifact suppression

There exists a growing interest in wearable/portable biopotential monitoring systems. These systems have very strict requirements in terms of power dissipation, high signal quality, small area (minimal use of externals) and robust operation during ambulatory use. The latter is emerging as an especially important problem since in real-life ambulatory conditions, motion artifacts can disturb and potentially saturate the readout channel. In addition, requirements for multimodal information acquisition require even more functionality with minimal power dissipation.