A Novel Chest-Based PPG Measurement System.

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.

Dynamic sub-array selection-based energy-efficient localization and tracking method to power implanted medical devices in scattering heterogenous media employing ultrasound.

Ultrasound (US) as a wireless power transfer methodology has drawn considerable attention from the implantable medical devices (IMD) research community. Beamforming (BF) using an external transducer array patch (ETAP) has been proposed as a robust localization scheme to find a mm-sized IMD inside the human body. However, for applications focusing on deep and shallow IMDs, optimum resource utilization at the ETAP is a major power efficiency concern for energy-constrained wearable patches.

Physiological Profiling of Agitation in Dementia: Insights From Wearable Sensor Data.

Background And Objectives: The number of people with dementia is expected to triple to 152 million in 2050, with 90% having accompanying behavioral and psychological symptoms (BPSD). Agitation is among the most critical BPSD and can lead to decreased quality of life for people with dementia and their caregivers. This study aims to explore objective quantification of agitation in people with dementia by analyzing the relationships between physiological and movement data from wearables and observational measures of agitation.

A Multichannel Electrochemical Sensor Interface IC for Bioreactor Monitoring.

This research article introduces a novel integrated circuit (IC) designed for bioreactor applications catering to multichannel electrochemical sensing. The proposed IC comprises 2x potentiometric, 2x potentiostat, 2x ISFET channels and 1x temperature channel. The potentiostat channel utilizes a current conveyor-based architecture with a programmable mirroring ratio, enabling an extensive measurement range of 114 dB.

Toward Quantification of Agitation in People With Dementia Using Multimodal Sensing.

Background And Objectives: Agitation, a critical behavioral and psychological symptom in dementia, has a profound impact on a patients' quality of life as well as their caregivers'. Autonomous and objective characterization of agitation with multimodal systems has the potential to capture key patient responses or agitation triggers.

Research Design And Methods: In this article, we describe our multimodal system design that encompasses contextual parameters, physiological parameters, and psychological parameters.

LSTM-only Model for Low-complexity HR Estimation from Wrist PPG.

Continuous and non-invasive cardiovascular monitoring has gained attention due to the miniaturization of wearable devices. Particularly, wrist-worn photoplethysmography (PPG) sensors present an alternative to electrocardiogram recording for heart rate (HR) monitoring as it is cheaper and non-intrusive for daily activities. Yet, the accuracy of PPG measurements is heavily affected by motion artifacts which are inherent to ambulatory environments.

A 134 DB Dynamic Range Noise Shaping Slope Light-to-Digital Converter for Wearable Chest PPG Applications.

This paper presents a low power, high dynamic range (DR), light-to-digital converter (LDC) for wearable chest photoplethysmogram (PPG) applications. The proposed LDC utilizes a novel 2-order noise-shaping slope architecture, directly converting the photocurrent to a digital code. This LDC applies a high-resolution dual-slope quantizer for data conversion.

A 108 dB DR Δ∑-∑M Front-End With 720 mV Input Range and >±300 mV Offset Removal for Multi-Parameter Biopotential Recording.

The recording of biopotential signals using techniques such as electroencephalography (EEG) and electrocardiography (ECG) poses important challenges to the design of the front-end readout circuits in terms of noise, electrode DC offset cancellation and motion artifact tolerance. In this paper, we present a 2-order hybrid-CTDT Δ∑-∑ modulator front-end architecture that tackles these challenges by taking advantage of the over-sampling and noise-shaping characteristics of a traditional Δ∑ modulator, while employing an extra ∑-stage in the feedback loop to remove electrode DC offsets and accommodate motion artifacts. To meet the stringent noise requirements of this application, a capacitively-coupled chopper-stabilized amplifier located in the forward path of the modulator loop serves simultaneously as an input stage and an active adder.

A Millimeter-Scale Crystal-Less MICS Transceiver for Insertable Smart Pills.

This paper presents a millimeter-scale crystal-less wireless transceiver for volume-constrained insertable pills. Operating in the 402-405 MHz medical implant communication service (MICS) band, the phase-tracking receiver-based over-the-air carrier recovery has a ±160 ppm coverage. A fully integrated adaptive antenna impedance matching solution is proposed to calibrate the antenna impedance variation inside the body.

Binary CorNET: Accelerator for HR Estimation From Wrist-PPG.

Research on heart rate (HR) estimation using wrist-worn photoplethysmography (PPG) sensors have progressed rapidly owing to the prominence of commercial sensing modules, used widely for lifestyle monitoring. Reported methodologies have been fairly successful in mitigating the effect of motion artifacts (MA) in ambulatory environment for HR estimation. Recently, a learning framework, CorNET, employing two-layer convolution neural networks (CNN) and two-layer long short-term network (LSTM) was successfully reported for estimating HR from MA-induced PPG signals.

A 119dB Dynamic Range Charge Counting Light-to-Digital Converter For Wearable PPG/NIRS Monitoring Applications.

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.

BioTranslator: Inferring R-Peaks from Ambulatory Wrist-Worn PPG Signal.

Advancements in wireless sensor networks (WSN) technology and miniaturization of wearable sensors have enabled long-term continuous pervasive biomedical signal monitoring. Wrist-worn photoplethysmography (PPG) sensors have gained popularity given their form factor. However the signal quality suffers due to motion artifacts when used in ambulatory settings, making vital parameter estimation a challenging task.

A 769 μW Battery-Powered Single-Chip SoC With BLE for Multi-Modal Vital Sign Monitoring Health Patches.

An all-in-one battery powered low-power SoC for measuring multiple vital signs with wearables is proposed. All functionality needed in a typical wearable use case scenario, including dedicated readouts, power management circuitry, digital signal processing and wireless communication (BLE) is integrated in a single die. This high level of integration allows an unprecedented level of miniaturization leading to smaller component count which reduces cost and improves comfort and signal integrity.

Neuropixels Data-Acquisition System: A Scalable Platform for Parallel Recording of 10 000+ Electrophysiological Signals.

Although CMOS fabrication has enabled a quick evolution in the design of high-density neural probes and neural-recording chips, the scaling and miniaturization of the complete data-acquisition systems has happened at a slower pace. This is mainly due to the complexity and the many requirements that change depending on the specific experimental settings. In essence, the fundamental challenge of a neural-recording system is getting the signals describing the largest possible set of neurons out of the brain and down to data storage for analysis.

A Compact Quad-Shank CMOS Neural Probe With 5,120 Addressable Recording Sites and 384 Fully Differential Parallel Channels.

Large-scale in vivo electrophysiology requires tools that enable simultaneous recording of multiple brain regions at single-neuron level. This calls for the design of more compact neural probes that offer even larger arrays of addressable sites and high channel counts. With this aim, we present in this paper a quad-shank approach to integrate as many as 5,120 sites on a single probe.

Motion Artifact Reduction for Wrist-Worn Photoplethysmograph Sensors Based on Different Wavelengths.

Long-term heart rate (HR) monitoring by wrist-worn photoplethysmograph (PPG) sensors enables the assessment of health conditions during daily life with high user comfort. However, PPG signals are vulnerable to motion artifacts (MAs), which significantly affect the accuracy of estimated physiological parameters such as HR. This paper proposes a novel modular algorithm framework for MA removal based on different wavelengths for wrist-worn PPG sensors.

A 400 GΩ Input-Impedance Active Electrode for Non-Contact Capacitively Coupled ECG Acquisition With Large Linear-Input-Range and High CM-Interference-Tolerance.

Electrocardiogram (ECG) is one of the major physiological vital signs and an effective monitoring method for patients with cardiovascular diseases. However, existing ECG recordings require a galvanic body contact, which is unpractical in daily life. This paper presents the design of an ECG chip that facilitates non-contact ECG recording through capacitive coupling.

CorNET: Deep Learning Framework for PPG-Based Heart Rate Estimation and Biometric Identification in Ambulant Environment.

Advancements in wireless sensor network technologies have enabled the proliferation of miniaturized body-worn sensors, capable of long-term pervasive biomedical signal monitoring. Remote cardiovascular monitoring has been one of the beneficiaries of this development, resulting in non-invasive, photoplethysmography (PPG) sensors being used in ambulatory settings. Wrist-worn PPG, although a popular alternative to electrocardiogram, suffers from motion artifacts inherent in daily life.

A 665 μW Silicon Photomultiplier-Based NIRS/EEG/EIT Monitoring ASIC for Wearable Functional Brain Imaging.

This paper presents a sub-mW ASIC for multimodal brain monitoring. The ASIC is co-integrated with electrode(s) and optodes (i.e.

A 36 μW 1.1 mm2 Reconfigurable Analog Front-End for Cardiovascular and Respiratory Signals Recording.

This paper presents a 1.2 V 36 μW reconfigurable analog front-end (R-AFE) as a general-purpose low-cost IC for multiple-mode biomedical signals acquisition. The R-AFE efficiently reuses a reconfigurable preamplifier, a current generator (CG), and a mixed signal processing unit, having an area of 1.

Time Multiplexed Active Neural Probe with 1356 Parallel Recording Sites.

We present a high electrode density and high channel count CMOS (complementary metal-oxide-semiconductor) active neural probe containing 1344 neuron sized recording pixels (20 µm × 20 µm) and 12 reference pixels (20 µm × 80 µm), densely packed on a 50 µm thick, 100 µm wide, and 8 mm long shank. The active electrodes or pixels consist of dedicated in-situ circuits for signal source amplification, which are directly located under each electrode. The probe supports the simultaneous recording of all 1356 electrodes with sufficient signal to noise ratio for typical neuroscience applications.

A 172 $\mu$W Compressively Sampled Photoplethysmographic (PPG) Readout ASIC With Heart Rate Estimation Directly From Compressively Sampled Data.

A compressive sampling (CS) photoplethysmographic (PPG) readout with embedded feature extraction to estimate heart rate (HR) directly from compressively sampled data is presented. It integrates a low-power analog front end together with a digital back end to perform feature extraction to estimate the average HR over a 4 s interval directly from compressively sampled PPG data. The application-specified integrated circuit (ASIC) supports uniform sampling mode (1x compression) as well as CS modes with compression ratios of 8x, 10x, and 30x.

A Neural Probe With Up to 966 Electrodes and Up to 384 Configurable Channels in 0.13 $\mu$m SOI CMOS.

In vivo recording of neural action-potential and local-field-potential signals requires the use of high-resolution penetrating probes. Several international initiatives to better understand the brain are driving technology efforts towards maximizing the number of recording sites while minimizing the neural probe dimensions. We designed and fabricated (0.

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.