Artificial intelligence in the anterior segment of eye diseases.

Ophthalmology is a subject that highly depends on imaging examination. Artificial intelligence (AI) technology has great potential in medical imaging analysis, including image diagnosis, classification, grading, guiding treatment and evaluating prognosis. The combination of the two can realize mass screening of grass-roots eye health, making it possible to seek medical treatment in the mode of "first treatment at the grass-roots level, two-way referral, emergency and slow treatment, and linkage between the upper and lower levels". On the basis of summarizing the AI technology carried out by scholars and their teams all over the world in the field of ophthalmology, quite a lot of studies have confirmed that machine learning can assist in diagnosis, grading, providing optimal treatment plans and evaluating prognosis in corneal and conjunctival diseases, ametropia, lens diseases, glaucoma, iris diseases, etc. This paper systematically shows the application and progress of AI technology in common anterior segment ocular diseases, the current limitations, and prospects for the future.

An Energy Efficient and Temperature Stable Digital FLL-based Wakeup Timer with Time-Domain Temperature Compensation.

This brief presents an on-chip digital intensive frequency-locked loop (DFLL)-based wakeup timer with a time-domain temperature compensation featuring a embedded temperature sensor. The proposed compensation exploits the deterministic temperature characteristics of two complementary resistors to stabilize the timer's operating frequency across the temperature by modulating the activation time window of the two resistors. As a result, it achieves a fine trimming step (± 1 ppm), allowing a small frequency error after trimming (<± 20 ppm). By reusing the DFLL structure, instead of employing a dedicated sensor, the temperature sensing operates in the background with negligible power (2 %) and hardware overhead (< 1 %). The chip is fabricated in 40 nm CMOS, resulting in 0.9 pJ/cycle energy efficiency while achieving 8 ppm/ºC from -40ºC to 80ºC.

A Spatially Diverse 2TX-3RX Galvanic-Coupled Transdural Telemetry for Tether-Less Distributed Brain-Computer Interfaces.

A near-field galvanic coupled transdural telemetry ASICs for intracortical brain-computer interfaces is presented. The proposed design features a two channels transmitter and three channels receiver (2TX-3RX) topology, which introduces spatial diversity to effectively mitigate misalignments (both lateral and rotational) between the brain and the skull and recovers the path loss by 13 dB when the RX is in the worst-case blind spot. This spatial diversity also allows the presented telemetry to support the spatial division multiplexing required for a high-capacity multi-implant distributed network. It achieves a signal-to-interference ratio of 12 dB, even with the adjacent interference node placed only 8 mm away from the desired link. While consuming only 0.33 mW for each channel, the presented RX achieves a wide bandwidth of 360 MHz and a low input referred noise of 13.21 nV/√Hz. The presented telemetry achieves a 270 Mbps data rate with a BER < 10-6 and an energy efficiency of 3.4 pJ/b and 3.7 pJ/b, respectively. The core footprint of the TX and RX modules is only 100 and 52 mm2, respectively, minimizing the invasiveness of the surgery. The proposed transdural telemetry system has been characterized ex-vivo with a 7-mm thick porcine tissue.

An Event-Based Neural Compressive Telemetry With >11× Loss-Less Data Reduction for High-Bandwidth Intracortical Brain Computer Interfaces.

Intracortical brain-computer interfaces offer superior spatial and temporal resolutions, but face challenges as the increasing number of recording channels introduces high amounts of data to be transferred. This requires power-hungry data serialization and telemetry, leading to potential tissue damage risks. To address this challenge, this paper introduces an event-based neural compressive telemetry (NCT) consisting of 8 channel-rotating Δ-ADCs, an event-driven serializer supporting a proposed ternary address event representation protocol, and an event-based LVDS driver. Leveraging a high sparsity of extracellular spikes and high spatial correlation of the high-density recordings, the proposed NCT achieves a compression ratio of >11.4×, while consumes only 1 µW per channel, which is 127× more efficient than state of the art. The NCT well preserves the spike waveform fidelity, and has a low normalized RMS error <23% even with a spike amplitude down to only 31 µV.

Mild cognitive impairment estimation based on functional near-infrared spectroscopy.

Patients with mild cognitive impairment (MCI) are at a high risk of developing future dementia. However, early identification and active intervention could potentially reduce its morbidity and the incidence of dementia. Functional near-infrared spectroscopy (fNIRS) has been proposed as a noninvasive modality for detecting oxygenation changes in the time-varying hemodynamics of the prefrontal cortex. This study sought to provide an effective method for detecting patients with MCI using fNIRS and the Wisconsin card sorting test (WCST) to evaluate changes in blood oxygenation. The results revealed that all groups with a lower mini-mental state examination grade had a higher increase in HHb concentration during a modified WCST (MCST). The increase in the change in oxygenated hemoglobin concentration in the stroke group was smaller than that in the normal group due to weak cerebrovascular reactivity.

Migraine classification by machine learning with functional near-infrared spectroscopy during the mental arithmetic task.

Migraine is a common and complex neurovascular disorder. Clinically, the diagnosis of migraine mainly relies on scales, but the degree of pain is too subjective to be a reliable indicator. It is even more difficult to diagnose the medication-overuse headache, which can only be evaluated by whether the symptom is improved after the medication adjustment. Therefore, an objective migraine classification system to assist doctors in making a more accurate diagnosis is needed. In this research, 13 healthy subjects (HC), 9 chronic migraine subjects (CM), and 12 medication-overuse headache subjects (MOH) were measured by functional near-infrared spectroscopy (fNIRS) to observe the change of the hemoglobin in the prefrontal cortex (PFC) during the mental arithmetic task (MAT). Our model shows the sensitivity and specificity of CM are 100% and 75%, and that of MOH is 75% and 100%.The results of the classification of the three groups prove that fNIRS combines with machine learning is feasible for the migraine classification.

An Energy-Efficient and High-Data-Rate IR-UWB Transmitter for Intracortical Neural Sensing Interfaces.

This paper presents an implantable impulse-radio ultra-wideband (IR-UWB) wireless telemetry system for intracortical neural sensing interfaces. A 3-dimensional (3-D) hybrid impulse modulation that comprises phase shift keying (PSK), pulse position modulation (PPM) and pulse amplitude modulation (PAM) is proposed to increase modulation order without significantly increasing the demodulation requirement, thus leading to a high data rate of 1.66 Gbps and an increased air-transmission range. Operating in 6 - 9 GHz UWB band, the presented transmitter (TX) supports the proposed hybrid modulation with a high energy efficiency of 5.8 pJ/bit and modulation quality (EVM< -21 dB). A low-noise injection-locked ring oscillator supports 8-PSK with a phase error of 2.6°. A calibration free delay generator realizes a 4-PPM with only 115 µW and avoids potential cross-modulation between PPM and PSK. A switch-cap power amplifier with an asynchronous pulse-shaping performs 4-PAM with high energy efficiency and linearity. The TX is implemented in 28 nm CMOS technology, occupying 0.155mm2 core area. The wireless module including a printed monopole antenna has a module area of only 1.05 cm2. The transmitter consumes in total 9.7 mW when transmitting -41.3 dBm/MHz output power. The wireless telemetry module has been validated ex-vivo with a 15-mm multi-layer porcine tissue, and achieves a communication (air) distance up to 15 cm, leading to at least 16× improvement in distance-moralized energy efficiency of 45 pJ/bit/meter compared to state-of-the-art.

Galvanic-coupled Trans-dural Data Transfer for High-bandwidth Intra-cortical Neural Sensing.

A digital-impulse galvanic coupling as a new high-speed trans-dural (from cortex to the skull) data transmission method has been presented in this paper. The proposed wireless telemetry replaces the tethered wires connected in between implants on the cortex and above the skull, allowing the brain implant to be "free-floating" for minimizing brain tissue damage. Such trans-dural wireless telemetry must have a wide channel bandwidth for high-speed data transfer and a small form factor for minimum invasiveness. To investigate the propagation property of the channel, a finite element model is developed and a channel characterization based on a liquid phantom and porcine tissue is performed. The results show that the trans-dural channel has a wide frequency response of up to 250 MHz. Propagation loss due to micro-motion and misalignments is also investigated in this work. The result indicates that the proposed transmission method is relatively insensitive to misalignment. It has approximately 1 dB extra loss when there is a horizontal misalignment of 1mm. A pulse-based transmitter ASIC and a miniature PCB module are designed and validated ex-vivo with a 10-mm thick porcine tissue. This work demonstrates a high-speed and miniature in-body galvanic-coupled pulse-based communication with a data rate up to 250 Mbps with an energy efficiency of 2 pJ/bit, and has a small module area of only 26 mm2.

An Implantable Neuromorphic Sensing System Featuring Near-sensor Computation and Send-on-Delta Transmission for Wireless Neural Sensing of Peripheral Nerves.

This paper presents a bio-inspired event-driven neuromorphic sensing system (NSS) capable of performing on-chip feature extraction and "send-on-delta" pulse-based transmission, targeting peripheral-nerve neural recording applications. The proposed NSS employs event-based sampling which, by leveraging the sparse nature of electroneurogram (ENG) signals, achieves a data compression ratio of >125×, while maintaining a low normalized RMS error of 4% after reconstruction. The proposed NSS consists of three sub-circuits. A clockless level-crossing (LC) ADC with background offset calibration has been employed to reduce the data rate, while maintaining a high signal to quantization noise ratio. A fully synthesized spiking neural network (SNN) extracts temporal features of compound action potential signals consumes only 13 µW. An event-driven pulse-based body channel communication (Pulse-BCC) with serialized address-event representation encoding (AER) schemes minimizes transmission energy and form factor. The prototype is fabricated in 40-nm CMOS occupying a 0.32-mm2 active area and consumes in total 28.2 µW and 50 µW power in feature extraction and full diagnosis mode, respectively. The presented NSS also extracts temporal features of compound action potential signals with 10-µs precision.

Enabling Robust Radar-Based Localization and Vital Signs Monitoring in Multipath Propagation Environments.

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.

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 Direct Phase-Tracking Doppler Radar Using Wavelet Independent Component Analysis for Non-Contact Respiratory and Heart Rate Monitoring.

A continuous wave Doppler radar, operating as a phase-locked-loop in phase demodulator configuration, is proposed and in vivo demonstrated for noncontact vital signs monitoring. The radar architecture exhibits a unique precision in tracking the phase modulation caused by human cardiopulmonary activity from which heartbeat and respiration can simultaneously be extracted. The single mixer architecture is immune to the null point and does not require small-angle approximation conditions, which distinguishes it from pre-existing other approaches. This enables the proposed radar to behave highly linear, with very precise detection of phase modulations induced by any kind of movement, independently from amplitude and speed. After simulations and technical tests to validate functionality and safety of the proposed architecture, a practical setup was demonstrated on human volunteers. Wavelet independent component analysis was applied to successfully retrieve respiratory and heart rate information from the radar baseband signal.

Frequency-Tracking CW Doppler Radar Solving Small-Angle Approximation and Null Point Issues in Non-Contact Vital Signs Monitoring.

A Doppler radar operating as a Phase-Locked-Loop (PLL) in frequency demodulator configuration is presented and discussed. The proposed radar presents a unique architecture, using a single channel mixer, and allows to detect contactless vital signs parameters while solving the null point issue and without requiring the small angle approximation condition. Spectral analysis, simulations, and experimental results are presented and detailed to demonstrate the feasibility and the operational principle of the proposed radar architecture.

An Energy-Efficient Antenna Impedance Detection Using Electrical Balance for Single-Step On-Chip Tunable Matching in Wearable/Implantable Applications.

A low-power impedance detection method using a hybrid transformer and its analysis are presented. A balun is reused for the hybrid transformer to achieve area-efficiency. The complex impedance detection enables faster optimization on a tunable matching network and its calibration technique increases the detection accuracy. The proposed impedance detection method fabricated in a 40-nm CMOS process consumes only 0.83 mW and demonstrates the accuracy of less than 18° and 0.1 on the Γ detection up to of 0.5 in ISM2.4 GHz band. After single-step matching network tuning, PA power and RX noise figure are improved by up to 1.2 dB and 1.3 dB, respectively.

A 0.33 nJ/bit IEEE802.15.6/Proprietary MICS/ISM Wireless Transceiver With Scalable Data Rate for Medical Implantable Applications.

This paper presents an ultra-low power wireless transceiver specialized for but not limited to medical implantable applications. It operates at the 402-405-MHz medical implant communication service band, and also supports the 420-450-MHz industrial, scientific, and medical band. Being IEEE 802.15.6 standard compliant with additional proprietary modes, this highly configurable transceiver achieves date rates from 11 kb/s to 4.5 Mb/s, which covers the requirements of conventional implantable applications. The phase-locked loop-based transmitter architecture is adopted to support various modulation schemes with limited power budget. The zero-IF receiver has programmable gain and bandwidth to accommodate different operation modes. Fabricated in 40-nm CMOS technology with 1-V supply, this transceiver only consumes 1.78 mW for transmission and 1.49 mW for reception. The ultra-low power consumption together with the 15.6-compliant performance in term of modulation accuracy, sensitivity, and interference robustness make this transceiver competent for various implantable applications.