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Advancements in integrated circuit (IC) technology have accelerated the miniaturization of body-worn sensors and systems, enabling long-term health monitoring. Wearable electrocardiogram (ECG), finger photoplethysmogram (PPG), and wrist-worn PPG have shown great success and significantly improved life quality. Chest-based PPG has the potential to extract multiple vital signs but requires ultra-high dynamic range (DR) IC to read out the small PPG signal among large respiration and artifacts inherent in daily life. This paper presents a dedicated high DR system for wearable chest PPG applications with a small form factor. The whole measurement system is integrated on a 20 cm2 PCB board. We have formulated a comprehensive evaluation protocol to validate the system with on-body chest PPG measurement in the workspace environment. First, chest PPG data was obtained from 6 adults and compared to data from a standard ECG patch. This system showed an average absolute deviation (AD) of 0.41 beats per minute, achieving > 99.53% heart rate (HR) accuracy. Second, chest PPG was recorded and compared to conventional PPG finger clip and PPG wristband, also showing > 98.6% HR matching and an absolute deviation in the standard deviation of NN intervals (SDNN) of < 12.8 ms for HRV monitoring within the protocol. Moreover, it successfully derives other vital parameters such as respiration rate and blood oxygen level (SpO2), showing the advancement among all these three reference modalities. This system can pave the way for new application areas, such as chest patches, to monitor chronic heart and respiratory diseases., (© 2024 The Authors.)

Objective: Over the last two decades, radar-based contactless monitoring of vital signs (heartbeat and respiration rate) has raised increasing interest as an emerging and added value to health care. However, until now, the flaws caused by indoor multipath propagation formed a fundamental hurdle for the adoption of such technology in practical healthcare applications where reliability and robustness are crucial. Multipath reflections, originated from one person, combine with the direct signals and multipaths of other people and stationary objects, thus jeopardizing individual vital signs extraction and localization. This work focuses on tackling indoor multipath propagation., Methods: We describe a methodology, based on accurate models of the indoor multipaths and of the radar signals, that enables separating the undesired multipaths from desired signals of multiple individuals, removing a key obstacle to real-world contactless vital signs monitoring and localization., Results: We also demonstrated it by accurately measure individual heart rates, respiration rates, and absolute distances (range information) of paired volunteers in a challenging real-world office setting., Conclusion: The approach, validated using a frequency-modulated continuous wave (FMCW) radar, was shown to function in an indoor environment where radar signals are severely affected by multipath reflections., Significance: Practical applications arise for health care, assisted living, geriatric and quarantine medicine, rescue and security purposes.

The electrocardiogram (ECG) is an important diagnostic tool for identifying cardiac problems. Nowadays, new ways to record ECG signals outside of the hospital are being investigated. A promising technique is capacitively coupled ECG (ccECG), which allows ECG signals to be recorded through insulating materials. However, as the ECG is no longer recorded in a controlled environment, this inevitably implies the presence of more artefacts. Artefact detection algorithms are used to detect and remove these. Typically, the training of a new algorithm requires a lot of ground truth data, which is costly to obtain. As many labelled contact ECG datasets exist, we could avoid the use of labelling new ccECG signals by making use of previous knowledge. Transfer learning can be used for this purpose. Here, we applied transfer learning to optimise the performance of an artefact detection model, trained on contact ECG, towards ccECG. We used ECG recordings from three different datasets, recorded with three recording devices. We showed that the accuracy of a contact-ECG classifier improved between 5 and 8% by means of transfer learning when tested on a ccECG dataset. Furthermore, we showed that only 20 segments of the ccECG dataset are sufficient to significantly increase the accuracy.

This paper presents a low power, high dynamic range (DR), reconfigurable light-to-digital converter (LDC) for photoplethysmogram (PPG), and near-infrared spectroscopy (NIRS) sensor readouts. The proposed LDC utilizes a current integration and a charge counting operation to directly convert the photocurrent to a digital code, reducing the noise contributors in the system. This LDC consists of a latched comparator, a low-noise current reference, a counter, and a multi-function integrator, which is used in both signal amplification and charge counting based data quantization. Furthermore, a current DAC is used to further increase the DR by canceling the baseline current. The LDC together with LED drivers and auxiliary digital circuitry are implemented in a standard 0.18 μm CMOS process and characterized experimentally. The LDC and LED drivers consume a total power of 196 μW while achieving a maximum 119 dB DR. The charge counting clock, and the pulse repetition frequency of the LED driver can be reconfigured, providing a wide range of power-resolution trade-off. At a minimum power consumption of 87 μW, the LDC still achieves 95 dB DR. The LDC is also validated with on-body PPG and NIRS measurement by using a photodiode (PD) and a silicon photomultiplier (SIPM), respectively.

This paper presents a sub-mW ASIC for multimodal brain monitoring. The ASIC is co-integrated with electrode(s) and optodes (i.e., optical source and detector) as an active sensor to measure electroencephalography (EEG), bio-impedance (BioZ), and near-infrared spectroscopy (NIRS) on scalp. The target is to build a wearable EEG-NIRS headset for low-cost functional brain imaging. The proposed NIRS readout utilizes the near-infrared light to measure the pulse oximetry and blood oxygen saturation (SpO 2 ). While traditional photodiodes are supported, the readout also allows the use of silicon photomultipliers (SiPMs) as optical detectors. The SiPM improves optical sensitivity while significantly reducing the average power of two LEDs to 150 μW. On circuit level, a SAR-based calibration compensates maximum 40 μA current from ambient light, while digital DC-servo loops reduces the baseline static SiPM current up to 400 μA, leading to an overall dynamic range of 87 dB. The EEG readout exhibits 720 MΩ input impedance at 50 Hz. The BioZ readout has 3 mΩ/√(Hz) impedance sensitivity by employing dynamic circuit techniques. When EEG, BioZ, and NIRS are enabled at the same time, one ASIC consumes 665 μW including the power of LEDs.

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 μ 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 low-power analog signal processing IC is presented for the low-power heart rhythm analysis. The ASIC features 3 identical, but independent intra-ECG readout channels each equipping an analog QRS feature extractor for low-power consumption and fast diagnosis of the fatal case. A 16-level digitized sine-wave synthesizer together with a synchronous readout circuit can measure bio-impedance in the range of 0.1-4.4 kΩ with 33 mΩ(rms) resolution and higher than 97% accuracy. The proposed 25 mm² ASIC consumes only 13 μA from 2.2 V. It is a highly integrated solution offering all the functionality of acquiring multiple high quality intra-cardiac signals, requiring only a few limited numbers of external passives.

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.

This paper describes a mixed-signal ECG processing platform with an 12-bit ADC architecture that can adapt its sampling rate according to the input signals rate of change. This enables the sampling of ECG signals with significantly reduced data rate without loss of information. The presented adaptive sampling scheme reduces the ADC power consumption, enables the processing of ECG signals with lower power consumption, and reduces the power consumption of the radio while streaming the ECG signals. The test results show that running a CWT-based R peak detection algorithm using the adaptively sampled ECG signals consumes only 45.6 μW and it leads to 36% less overall system power consumption.

Recent advances in low-power wireless technologies for health are instrumental in bringing EEG monitoring from the hospital to the home environment. This talk provides an overview of imec's research on low-power wireless EEG monitoring. Enabling technologies, integrated systems and remaining challenges are discussed.

An ECG signal processing method with quad level vector (QLV) is proposed for the ECG holter system. The ECG processing consists of the compression flow and the classification flow, and the QLV is proposed for both flows to achieve better performance with low-computation complexity. The compression algorithm is performed by using ECG skeleton and the Huffman coding. Unit block size optimization, adaptive threshold adjustment, and 4-bit-wise Huffman coding methods are applied to reduce the processing cost while maintaining the signal quality. The heartbeat segmentation and the R-peak detection methods are employed for the classification algorithm. The performance is evaluated by using the Massachusetts Institute of Technology-Boston's Beth Israel Hospital Arrhythmia Database, and the noise robust test is also performed for the reliability of the algorithm. Its average compression ratio is 16.9:1 with 0.641% percentage root mean square difference value and the encoding rate is 6.4 kbps. The accuracy performance of the R-peak detection is 100% without noise and 95.63% at the worst case with -10-dB SNR noise. The overall processing cost is reduced by 45.3% with the proposed compression techniques.

Recent advances in ultra-low-power circuits and energy harvesters are making self-powered body sensor nodes a reality. Power optimization at the system and application level is crucial in achieving ultra-low-power consumption for the entire system. This paper reviews system-level power optimization techniques, and illustrates their impact on the case of autonomous wireless EMG monitoring. The resulting prototype, an Autonomous wireless EMG sensor power by PV-cells, is presented.

This paper discusses ultra-low-power wireless sensor nodes intended for wearable biopotential monitoring. Specific attention is given to mixed-signal design approaches and their impact on the overall system power dissipation. Examples of trade-offs in power dissipation between analog front-ends and digital signal processing are also given. It is shown how signal filtering can further reduce the internal power consumption of a node. Such power saving approaches are indispensable as real-life tests of custom wireless ECG patches reveal the need for artifact detection and correction. The power consumption of such additional features has to come from power savings elsewhere in the system as the overall power budget cannot increase.