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. Research Design and Methods: The data presented here is from 30 people with dementia, each included for 1 week, collected following our previously published multimodal data collection protocol. This observational protocol has a cross-sectional repeated measures design, encompassing data from both wearable and fixed sensors. Generalized linear mixed models were used to quantify the relationship between data from different wearable sensor modalities and agitation, as well as motor and verbal agitation specifically. Results: Several features from wearable data are significantly associated with agitation, at least the p < .05 level (absolute ß: 0.224-0.753). Additionally, different features are informative depending on the agitation type or the patient the data were collected from. Adding context with key confounding variables (time of day, movement, and temperature) allows for a clearer interpretation of feature differences when a person with dementia is agitated. Discussion and Implications: The features shown to be significantly different, across the study population, suggest possible autonomic nervous system activation when agitated. Differences when splitting the data by agitation type point toward a need for future detection models to tailor to the primary type of agitation expressed. Finally, patient-specific differences in features indicate a need for patient- or group-level model personalization. The findings reported in this study both reinforce and add to the fundamental understanding of and can be used to drive the objective quantification 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. The potentiometric channel incorporates a customized electrostatic discharge (ESD) protection circuit to achieve ultra-low input leakage in the picoampere range, while the ISFET channel employs a constant-voltage, constant-current topology for accurate pH measurement. Combined with the die temperature sensor, this IC is well-suited for monitoring bioreactions in real-time. Additionally, all channels can be time-multiplexed to a reconfigurable analog backend, facilitating the conversion of input signals into digital codes. The prototype of the IC is fabricated using 0.18 µm standard CMOS technology, and each channel is experimentally characterized. The interface IC demonstrates a peak power consumption of 22 µW.

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. This design is the first to include all three of these facets in an n > 1 study. Using a combination of fixed and wearable sensors and a custom-made app for psychological annotation, we aim to identify physiological markers and contextual triggers of agitation. Results: A discussion of both the clinical as well as the technical implementation of the to-date data collection protocol is presented, as well as initial insights into pilot study data collection. Discussion and Implications: The ongoing data collection moves us toward improved agitation quantification and subsequent prediction, eventually enabling just-in-time intervention.

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. In this paper, we propose a low-complexity LSTM-only neural network for HR estimation from a single PPG channel during intense physical activity. This work explored the trade-off between model complexity and accuracy by exploring different model dataflows, number of layers, and number of training epochs to capture the intrinsic time-dependency between PPG samples. The best model achieves a mean absolute error of 4.47 ± 3.68 bpm when evaluated on 12 IEEE SPC subjects.Clinical relevance- This work aims to improve the quality of HR inference from PPG signals using neural network, enabling continuous vital signal monitoring with little interference in daily activities from embedded monitoring devices.

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 2nd-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. An auxiliary noise shaping loop is used to shape the residual quantization noise. Moreover, a DC compensation loop is implemented to cancel the PPG signal's DC component, thus further boosting the DR. The prototype is fabricated with 0.18 µm standard CMOS and characterized experimentally. The LDC consumes 28 µW per readout channel while achieving a maximum 134 dB DR. The LDC is also validated with on-body chest PPG measurement.

A 108 dB DR Δ∑-∑M Front-End With 720 mVpp 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 2nd-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 prototype of this direct-to-digital front-end chip is fabricated in a standard 0.18-µm CMOS process and achieves a peak SNR of 105.6 dB and a dynamic range of 108.3 dB, for a maximum input range of 720 mVpp. The measured input-referred noise is 0.98 µVrms over a bandwidth of 0.5-100 Hz, and the measured CMRR is >100 dB. ECG and EEG measurements in human subjects demonstrate the capability of this architecture to acquire biopotential signals in the presence of large motion artifacts.

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. A tunable matching network (TMN) with single inductor performs impedance matching for both transmitter (TX) and receiver (RX) and TX/RX mode switching. To dynamically calibrate the antenna impedance variation over different locations and diet conditions, a loop-back power detector using self-mixing is adopted, which expands the power contour up to 4.8 VSWR. The transceiver is implemented in a 40-nm CMOS technology, occupying 2 mm2 die area. The transceiver chip and a miniature antenna are integrated in a 3.5 × 15 mm2 area prototype wireless module. It has a receiver sensitivity of -90 dBm at 200 kbps data rate and delivers up to - 25 dBm EIRP in the wireless measurement with a liquid phantom.

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. 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.

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. However, such a network topology with large number of parameters presents a challenge, towards low-complexity hardware implementation aimed at on-node processing. In this paper, we demonstrate a fully binarized network (bCorNET) topology and its corresponding algorithm-to-architecture mapping and energy-efficient implementation for HR estimation. The proposed framework achieves a MAE of 6.67 ± 5.49 bpm when evaluated on 22 IEEE SPC subjects. The design, synthesized with ST65 nm technology library achieving 3 GOPS @ 1 MHz, consumes 56.1 µJ per window with occupied 1634K NAND2 equivalent cell area and had a latency of 32 ms when estimating HR every 2 s from PPG signals.

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. The SoC includes an ECG, Bio-Impedance and a fully differential PPG readout and can interface with external sensors (like an IMU). In a typical application scenario where all sensor readouts are enabled and key features (like heart rate) are calculated on the chip and streamed over the radio, the SoC consumes only 769 µW from the regulated 1.2 V supply.

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. Compact fully-differential recording channels were designed using a single-gain-stage neural amplifier with a 14-bit ADC, achieving a mean input-referred noise of 7.44 µVrms in the action-potential band and 7.65 µVrms in the local-field-potential band, a mean total harmonic distortion of 0.17% at 1 kHz and a mean input-referred offset of 169 µV. The probe base incorporates 384 channels with on-chip power management, reference-voltage generation and digital control, thus achieving the highest level of integration in a neural probe and excellent channel-to-channel uniformity. Therefore, no calibration or external circuitry are required to achieve the above-mentioned performance. With a total area of 2.2 × 8.67 mm2 and a power consumption of 36.5 mW, the presented probe enables full-system miniaturization for acute or chronic use in small rodents.

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. This requires a complete system optimization that considers the physical, electrical, thermal and signal-processing requirements, while accounting for available technology, manufacturing constraints and budget. Here we present a scalable and open-standards-based open-source data-acquisition system capable of recording from over 10,000 channels of raw neural data simultaneously. The components and their interfaces have been optimized to ensure robustness and minimum invasiveness in small-rodent electrophysiology.

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. The framework uses a green PPG signal for HR monitoring and an infrared PPG signal as the motion reference. The proposed framework includes four main steps: motion detection, motion removal using continuous wavelet transform, approximate HR estimation and signal reconstruction. The proposed algorithm is evaluated against an electrocardiogram (ECG) in terms of HR error for a dataset of 6 healthy subjects performing 21 types of motion. The proposed MA removal method reduced the average error in HR estimation from 4.3, 3.0 and 3.8 bpm to 0.6, 1.0 and 2.1 bpm in periodic, random, and continuous non-periodic motion situations, respectively.

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. With the input impedance boosting techniques, as well as an active driven-right-leg (DRL) which boosts common-mode rejection ratio to 70 dB, the single-ended capacitive feedback active electrode (AE) achieves ultra-high input impedance of 400 GΩ (< 0.5 Hz), a large common-mode interference tolerance (2.8 VPP), and a high linear-input-range (220 m VPP). Implemented in 0.18 µm 5V CMOS process, the prototype occupies an area of 1.23 mm2, and consumes 18 µA and 13 µA for the AE and DRL, respectively. Real life non-contact capacitively coupled ECG acquisition has been demonstrated, obtaining ECG waves and heart rate in the presence of motion artifacts as well as ambient interference.

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. Hence, in this paper, we present a novel deep learning framework (CorNET) to efficiently estimate heart rate (HR) information and perform biometric identification (BId) using only a wrist-worn, single-channel PPG signal collected in ambulant environment. We have formulated a completely personalized data-driven approach, using a four-layer deep neural network. Two convolution neural network layers are used in conjunction with two long short-term memory layers, followed by a dense output layer for modeling the temporal sequence inherent within the pulsatile signal representative of cardiac activity. The final dense layer is customized with respect to the application, functioning as: regression layer-having a single neuron to predict HR; classification layer-two neurons that identify a subject among a group. The proposed network was evaluated on the TROIKA dataset having 22 PPG records collected during various physical activities. We achieve a mean absolute error of 1.47 ± 3.37 beats per minute for HR estimation and an average accuracy of 96% for BId on 20 subjects. CorNET was further evaluated successfully in an ambulant use-case scenario with custom sensors for two subjects.

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. In this paper, we present a novel deep learning framework, BioTranslator, for computing the instantaneous heart rate (IHR), using wrist-worn PPG signals collected during physical activity. Using one-dimensional Convolution-Deconvolution Network, we translate a single channel PPG signal to an electrocardiogram(ECG)-like time series signal, from which relevant R-peak information can be inferred enabling IHR measures. The proposed network configuration was evaluated on 12 subjects of the TROIKA dataset, involved in physical activity. The proposed network identifies 92.8% of R-peaks, besides achieving a mean absolute error of 51±6.3ms with respect to reference ECG-derived IHR.

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., 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 (SpO2). 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.

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 1.1 mm2 per R-AFE while supporting five acquisition modes to record different forms of cardiovascular and respiratory signals. The R-AFE can interface with voltage-, current-, impedance-, and light-sensors and hence can measure electrocardiography (ECG), bio-impedance (BioZ), photoplethysmogram (PPG), galvanic skin response (GSR), and general-purpose analog signals. Thanks to the chopper preamplifier and the low-noise CG utilizing dynamic element matching, the R-AFE mitigates ${\text{1}}\text{/}f$ noise from both the preamplifier and the CG for improved measurement sensitivity. The IC achieves competitive performance compared to the state-of-the-art dedicated readout ICs of ECG, BioZ, GSR, and PPG, but with approximately 1.4×-5.3× smaller chip area per channel.

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 (0.13- µm SOI Al CMOS) a 384-channel configurable neural probe for large-scale in vivo recording of neural signals. Up to 966 selectable active electrodes were integrated along an implantable shank (70 µm wide, 10 mm long, 20  µm thick), achieving a crosstalk of [Formula: see text] dB. The probe base (5 × 9 mm 2 ) implements dual-band recording and a 171.6 Mbps digital interface. Measurement results show a total input-referred noise of 6.4 µ V rms and a total power consumption of 49.1  µW/channel.

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 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.