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Journal articleChew DJ, Constandinou TG, Gupta I, et al., 2019,
Bioelectronic medicines: past, present and future. Highlights from The Society for Medicines Research Symposium
, Drugs of the Future, Vol: 44, Pages: 895-902, ISSN: 0377-8282On October 1, 2019, the Society for Medicines Research (SMR) held its first symposium on "Bioelectronic medicines, past, present and future" at the Royal Academy of Engineering in London. The meeting was attended by 145 participants and was supported by Galvani Bioelectronics, IEEE-CAS Society, IEEE-Brain Initiative, BIOS, Heraeus, CorTec and the IT'IS Foundation.
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Conference paperDe Marcellis A, Stanchieri GDP, Palange E, et al., 2019,
A 0.35 mu m CMOS UWB-inspired bidirectional communication system-on-chip for transcutaneous optical biotelemetry links
, IEEE Biomedical Circuits and Systems Conference (BioCAS), Publisher: IEEE, ISSN: 2163-4025In this paper we report on the fabrication, implementation and experimental characterization of an integrated bidirectional communication System-on-Chip (SoC) for transcutaneous bidirectional optical biotelemetry links. The proposed architecture implements a UWB-inspired pulsed coding technique and contains a transmitter and a receiver to achieve a simultaneous bidirectional link. The transmitter generates sub- nanosecond current pulses to directly drive offchip pulsed vertical cavity semiconductor lasers by means of a digital data coding subsystem and all the needed bias and driving circuits. The receiver interfaces to off-chip fast Si photodiodes and includes signal conditioning, detection and digital data decoding circuits to support high bit rate and energy efficient communication links. The SoC has been implemented in a commercially-available 0.35 mu m CMOS technology provided by AMS, occupying a compact silicon footprint of less than 0.13 mm2 employing 113 transistors and 1 resistor. This is evaluated using a testbench consisting of a custom PCB and a Xilinx Virtex-6 XC6VLX240T FPGA board. Preliminary experimental results validated the correct functionality of the overall integrated system demonstrating its capability to operate, also in a bidirectional mode, at bit rates up to 250 Mbps with pulse widths down to 1.2 ns and a minimum total power efficiency of about 160 pJ/bit in the conditions for which the transmitter and the receiver work simultaneously on the same chip. This demonstrated performance makes the optical biotelemetry particularly suitable for highly scalable (i.e., high bitrate, low energy per bit) implantable devices such as brain machine interfaces.
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Conference paperHan Y, Lauteslager T, Lande TS, et al., 2019,
UWB radar for non-contact heart rate variability monitoring and mental state classification.
, Annual Meeting of the IEEE Engineering in Medicine and Biology Society, Pages: 6578-6582, ISSN: 1557-170XHeart rate variability (HRV), as measured by ultra-wideband (UWB) radar, enables contactless monitoring of physiological functioning in the human body. In the current study, we verified the reliability of HRV extraction from radar data, under limited transmitter power. In addition, we conducted a feasibility study of mental state classification from HRV data, measured using radar. Specifically, arctangent demodulation with calibration and low rank approximation have been used for radar signal pre-processing. An adaptive continuous wavelet filter and moving average filter were utilized for HRV extraction. For the mental state classification task, performance of support vector machine, k-nearest neighbors and random forest classifiers have been compared. The developed system has been validated on human participants, with 10 participants for HRV extraction, and three participants for the proof-of-concept mental state classification study. The results of HRV extraction demonstrate the reliability of time-domain parameter extraction from radar data. However, frequency-domain HRV parameters proved to be unreliable under low SNR. The best average overall mental state classification accuracy achieved was 82.34%, which has important implications for the feasibility of mental health monitoring using UWB radar.
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Journal articleLauteslager T, Tommer M, Lande TS, et al., 2019,
Coherent UWB radar-on-chip for in-body measurement of cardiovascular dynamics
, IEEE Transactions on Biomedical Circuits and Systems, Vol: 13, Pages: 814-824, ISSN: 1932-4545Coherent ultra-wideband (UWB) radar-on-chip technology shows great promise for developing portable and low-cost medical imaging and monitoring devices. Particularly monitoring the mechanical functioning of the cardiovascular system is of interest, due to the ability of radar systems to track sub-mm motion inside the body at a high speed. For imaging applications, UWB radar systems are required, but there are still significant challenges with in-body sensing using low-power microwave equipment and wideband signals. Recently it was shown for the first time, on a single subject, that the arterial pulse wave can be measured at various locations in the body, using coherent UWB radar-on-chip technology. The current work provides more substantial evidence, in the form of new measurements and improved methods, to demonstrate that cardiovascular dynamics can be measured using radar-on-chip. Results across four participants were found to be robust and repeatable. Cardiovascular signals were recorded using radar-on-chip systems and electrocardiography (ECG). Through ECG-aligned averaging, the arterial pulse wave could be measured at a number of locations in the body. Pulse arrival time could be determined with high precision, and blood pressure pulse wave propagation through different arteries was demonstrated. In addition, cardiac dynamics were measured from the chest. This work serves as a first step in developing a portable and low-cost device for long-term monitoring of the cardiovascular system, and provides the fundamentals necessary for developing UWB radar-on-chip imaging systems.
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Journal articleMirza KB, Golden C, Nikolic K, et al., 2019,
Closed-loop implantable therapeutic neuromodulation systems based on neurochemical monitoring
, Frontiers in Neuroscience, Vol: 13, ISSN: 1662-4548Closed-loop or intelligent neuromodulation allows adjustable, personalised neuromodulation which usually incorporates the recording of a biomarker, followed by implementation of an algori5 thm which decides the timing (when ?) and strength (how much ?) of stimulation. Closed-loop neuromodulation has been shown to have greater benefits compared to open-loop neuromodu lation, particularly for therapeutic applications such as pharmacoresistant epilepsy, movement disorders and potentially for psychological disorders such as depression or drug addiction. How ever, an important aspect of the technique is selection of an appropriate, preferably neural biomarker. Neurochemical sensing can provide high resolution biomarker monitoring for various neurological disorders as well as offer deeper insight into neurological mechanisms. The chemicals of interest being measured, could be ions such as potassium (K+), sodium (Na+ 12 ), calcium(Ca2+), chloride (Cl−), hydrogen (H+ 13 ) or neurotransmitters such as dopamine, serotonin and glutamate. This review focusses on the different building blocks necessary for a neurochemi cal, closed-loop neuromodulation system including biomarkers, sensors and data processing algorithms. Furthermore, it also highlights the merits and drawbacks of using this biomarker modality.
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Conference paperAhmadi N, Constandinou TG, Bouganis C-S, 2019,
End-to-End Hand Kinematic Decoding from LFPs Using Temporal Convolutional Network
, IEEE Biomedical Circuits and Systems Conference (BioCAS), Publisher: IEEE, Pages: 1-4, ISSN: 2163-4025In recent years, local field potentials (LFPs) haveemerged as a promising alternative input signal for brain-machine interfaces (BMIs). Several studies have demonstratedthat LFP-based BMIs could provide long-term recording stabilityand comparable decoding performance to their spike counter-parts. Despite the compelling results, however, most LFP-basedBMIs still make use of hand-crafted features which can betime-consuming and suboptimal. In this paper, we propose anend-to-end system approach based on temporal convolutionalnetwork (TCN) to automatically extract features and decodekinematics of hand movements directly from raw LFP signals.We benchmark its decoding performance against traditionalapproach incorporating long short-term memory (LSTM) de-coders driven by hand-crafted LFP features. Experimental re-sults demonstrate significant performance improvement of theproposed approach compared to the traditional approach. Thissuggests the suitability of TCN-based end-to-end system and itspotential for providng stable and high decoding performanceLFP-based BMIs.
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PatentToumazou C, Baig Mirza K, Rodriguez Manzano J, 2019,
Molecule detection using aptamer nucleic acid duplex
, WO2019138255A1The present invention relates to the detection of molecules of a target type in a sample. A method of detecting molecules of a target type in a sample comprises providing an aptamer-nucleic acid duplex, contacting the sample with the aptamer-nucleic acid duplex, wherein the aptamer is capable of selectively dissociating from the nucleic acid to selectively bind to a molecule of the target type in the sample, amplifying any dissociated nucleic acid and detecting any amplified nucleic acid. The method further comprises using the detected result to indicate the presence of molecules of the target type and/or quantify an amount of molecules of the target type. Also provided is a system for detecting molecules of a target type in a sample.
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Conference paperLiu Y, Constandinou TG, Georgiou P, 2019,
A 32 x 32 ISFET array with in-pixel digitisation and column-wise TDC for ultra-fast chemical sensing
, IEEE International Symposium on Circuits and Systems (IEEE ISCAS), Publisher: IEEE, ISSN: 0271-4302This paper presents a 32×32 ISFET sensing array with in-pixel digitisation for pH sensing. The in-pixel digitisation is achieved using an inverter-based sensing pixel that is controlled by a triangular waveform. This converts the pH response of the ISFET into a time-domain signal whilst also increasing dynamic range and thus the ability to tolerate sensor offset. The pixels are interfaced to a 15-bit asynchronous column-wise time-to-digital converter (TDC), enabling fast sensor readout whilst using minimal silicon area. Parallel output of 32 TDC interfaces are serialised to achieve fast data though-put. This system is implemented in a standard 0.18 μm standard CMOS technology, with a pixel size of 26 μm × 26 μm and a TDC of 26 μm × 180 μm. Simulation results demonstrate that chemical sampling of up to 5k frames per second can be achieved with a clock frequency of 160 MHz and a TDC resolution of 190 ps. The total power consumption of the overall system is 7.34 mW.
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Conference paperMirza KB, Kulasekeram N, Liu Y, et al., 2019,
System on chip for closed loop neuromodulation based on dual mode biosignals
, 2019 IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: Institute of Electrical and Electronics Engineers (IEEE), ISSN: 2158-1525Closed loop neuromodulation, where the stimulation is controlled autonomously based on physiological events, has been more effective than open loop techniques. In the few existing closed loop implementations which have a feedback, indirect non-neurophysiological biomarkers have been typically used (e.g. heart rate, stomach distension). Although these biomarkers enable automatic initiation of neural stimulation, they do not enable intelligent control of stimulation dosage. In this paper, we present a novel closed loop neuromodulation System-on-Chip (SoC) based on a dual signal mode that is detecting both electrical and chemical signatures of neural activity. We use vagus nerve stimulation (VNS) as a design case here. Vagal chemical (pH) signal is detected and used for initiating VNS and vagal compound nerve action potential (CNAP) signals are used to determine the stimulation dosage and pattern. Although we used the paradigm of appetite control and neurometabolic therapies for developing the algorithms for neurostimulation control, the SoC described here can be utilised for other types of closed loop neuromodulation implants.
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Conference paperCavuto ML, Constandinou TG, 2019,
Investigation of insertion method to achieve chronic recording stability of a semi-rigid implantable neural probe
, 9th IEEE/EMBS International Conference on Neural Engineering (NER), Publisher: IEEE, Pages: 665-669, ISSN: 1948-3546Brain machine interfaces notoriously face difficulties in achieving long term implanted recording stability. It has been shown that damage and inflammation, caused during insertion by electrodes that are too large and stiff, provoke a sustained inflammatory tissue response. This is commonly referred to as the foreign body response, resulting in encapsulation and thus increased electrode impedance over time. Accordingly, neural interfaces with ever smaller and more flexible electrodes are continually in development, but unfortunately face challenges of their own, first and foremost of which is buckling and bending during insertion. This work presents the development of a prototype insertion method, comprising an insertion device and novel probe architecture, that promotes straight insertion without buckling, while simultaneously minimizing the insertion force for multi-microwire electrode probes. When compared against insertion of probes with unsupported free electrodes, the prototype method achieved significantly straighter electrode insertion, resulting in both a smaller distance between electrode recording tips and a greater average insertion depth. While achieving less straight insertion than probes with sucrose coated electrodes, a common technique for promoting reliable insertion without buckling, the tested method was able to maintain significantly lower insertion forces.
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Conference paperAhmadi N, Cavuto ML, Feng P, et al., 2019,
Towards a distributed, chronically-implantable neural interface
, 9th IEEE/EMBS International Conference on Neural Engineering (NER), Publisher: IEEE, Pages: 719-724, ISSN: 1948-3546We present a platform technology encompassing a family of innovations that together aim to tackle key challenges with existing implantable brain machine interfaces. The ENGINI (Empowering Next Generation Implantable Neural Interfaces) platform utilizes a 3-tier network (external processor, cranial transponder, intracortical probes) to inductively couple power to, and communicate data from, a distributed array of freely-floating mm-scale probes. Novel features integrated into each probe include: (1) an array of niobium microwires for observing local field potentials (LFPs) along the cortical column; (2) ultra-low power instrumentation for signal acquisition and data reduction; (3) an autonomous, self-calibrating wireless transceiver for receiving power and transmitting data; and (4) a hermetically-sealed micropackage suitable for chronic use. We are additionally engineering a surgical tool, to facilitate manual and robot-assisted insertion, within a streamlined neurosurgical workflow. Ongoing work is focused on system integration and preclinical testing.
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Conference paperLeene LB, Constandinou TG, 2019,
A 3rd order time domain delta sigma modulator with extended-phase detection
, IEEE International Symposium on Circuits and Systems (IEEE ISCAS), Publisher: IEEE, ISSN: 0271-4302This paper presents a novel analogue to digital converter using an oscillator-based loop filter for high-dynamic range bio-sensing applications. This is the first third-order feedforward ΔΣ modulator that strictly uses time domain integration for quantisation noise shaping. Furthermore we propose a new asynchronous extended-phase detection technique that increases the resolution of the 4 bit phase quantiser by another 5 bits to significantly improve both dynamic range and reduce the noise-shaping requirements. Preliminary simulation results show that this type of loop-filter can virtually prevent integrator saturation and achieves a peak 88 dB SNDR for kHz signals. The proposed system has been implemented using a 180 nm CMOS technology occupying 0.102 mm 2 and consumes 13.7 μW of power to digitise the 15 kHz signal bandwidth using a 2 MHz sampling clock.
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Journal articleRawson TM, Hernandez B, Moore L, et al., 2019,
Supervised machine learning for the prediction of infection on admission to hospital: a prospective observational cohort study
, Journal of Antimicrobial Chemotherapy, Vol: 74, Pages: 1108-1115, ISSN: 0305-7453BackgroundInfection diagnosis can be challenging, relying on clinical judgement and non-specific markers of infection. We evaluated a supervised machine learning (SML) algorithm for diagnosing bacterial infection using routinely available blood parameters on presentation to hospital.MethodsAn SML algorithm was developed to classify cases into infection versus no infection using microbiology records and six available blood parameters (C-reactive protein, white cell count, bilirubin, creatinine, ALT and alkaline phosphatase) from 160 203 individuals. A cohort of patients admitted to hospital over a 6 month period had their admission blood parameters prospectively inputted into the SML algorithm. They were prospectively followed up from admission to classify those who fulfilled clinical case criteria for a community-acquired bacterial infection within 72 h of admission using a pre-determined definition. Predictive ability was assessed using receiver operating characteristics (ROC) with cut-off values for optimal sensitivity and specificity explored.ResultsOne hundred and four individuals were included prospectively. The median (range) cohort age was 65 (21–98) years. The majority were female (56/104; 54%). Thirty-six (35%) were diagnosed with infection in the first 72 h of admission. Overall, 44/104 (42%) individuals had microbiological investigations performed. Treatment was prescribed for 33/36 (92%) of infected individuals and 4/68 (6%) of those with no identifiable bacterial infection. Mean (SD) likelihood estimates for those with and without infection were significantly different. The infection group had a likelihood of 0.80 (0.09) and the non-infection group 0.50 (0.29) (P < 0.01; 95% CI: 0.20–0.40). ROC AUC was 0.84 (95% CI: 0.76–0.91).ConclusionsAn SML algorithm was able to diagnose infection in individuals presenting to hospital using routinely available blood parameters.
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Journal articleTroiani F, Nikolic K, Constandinou TG, 2019,
Correction: Simulating optical coherence tomography for observing nerve activity: a finite difference time domain bi-dimensional model
, PLoS ONE, Vol: 14, ISSN: 1932-6203[This corrects the article DOI: 10.1371/journal.pone.0200392.].
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Journal articleRawson TM, Ahmad R, Toumazou C, et al., 2019,
Artificial intelligence can improve decision-making in infection management
, Nature Human Behaviour, Vol: 3, Pages: 543-545, ISSN: 2397-3374Antibiotic resistance is an emerging global danger. Reaching responsible prescribing decisions requires the integration of broad and complex information. Artificial intelligence tools could support decision-making at multiple levels, but building them needs a transparent co-development approach to ensure their adoption upon implementation.
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Conference paperAhmadi N, Constandinou TG, Bouganis C-S, 2019,
Decoding Hand Kinematics from Local Field Potentials Using Long Short-Term Memory (LSTM) Network
, 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER 2019), Publisher: IEEE, Pages: 415-419Local field potential (LFP) has gained increasing interest as an alternativeinput signal for brain-machine interfaces (BMIs) due to its informativefeatures, long-term stability, and low frequency content. However, despitethese interesting properties, LFP-based BMIs have been reported to yield lowdecoding performances compared to spike-based BMIs. In this paper, we propose anew decoder based on long short-term memory (LSTM) network which aims toimprove the decoding performance of LFP-based BMIs. We compare offline decodingperformance of the proposed LSTM decoder to a commonly used Kalman filter (KF)decoder on hand kinematics prediction tasks from multichannel LFPs. We alsobenchmark the performance of LFP-driven LSTM decoder against KF decoder drivenby two types of spike signals: single-unit activity (SUA) and multi-unitactivity (MUA). Our results show that LFP-driven LSTM decoder achievessignificantly better decoding performance than LFP-, SUA-, and MUA-driven KFdecoders. This suggests that LFPs coupled with LSTM decoder could provide highdecoding performance, robust, and low power BMIs.
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Conference paperZhao Z, Li K, Toumazou C, et al., 2019,
A computational model for anti-cancer drug sensitivity prediction
, IEEE Biomedical Circuits and Systems Conference (BioCAS), Publisher: IEEE, ISSN: 2163-4025 -
Book chapterChen C-H, Toumazou C, 2019,
Personalized Expert Recommendation Systems for Optimized Nutrition
, TRENDS IN PERSONALIZED NUTRITION, Editors: Galanakis, Publisher: ACADEMIC PRESS LTD-ELSEVIER SCIENCE LTD, Pages: 309-338, ISBN: 978-0-12-816403-7- Author Web Link
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- Citations: 4
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Conference paperAhmadi N, Cavuto ML, Feng P, et al., 2019,
Towards a Distributed, Chronically-Implantable Neural Interface.
, Publisher: IEEE, Pages: 719-724 -
Conference paperLauteslager T, Tommer M, Lande TS, et al., 2018,
Cross-body UWB radar sensing of arterial pulse propagation and ventricular Dynamics
, IEEE Biomedical Circuits and Systems (BioCAS) Conference, Publisher: IEEE, Pages: 165-168Single-chip UWB radar systems have enormous potential for the development of portable, low-cost and easy-to-use devices for monitoring the cardiovascular system. Using body coupled antennas, electromagnetic energy can be directed into the body to measure arterial pulsation and cardiac motion, and estimate arterial stiffness as well as blood pressure. In the current study we validate that heart rate signals, obtained using multiple UWB radar-on-chip modules and body coupled antennas, do indeed originate from arterial pulsation. Through ECG-aligned averaging, pulse arrival time at a number of locations in the body could be measured with high precision, and arterial pulse propagation through the femoral and carotid artery was demonstrated. In addition, cardiac dynamics were measured from the chest. Onset and offset of ventricular systole were clearly distinguishable, as well as onset of atrial systole. Although further validation is required, these results show that UWB radar-on-chip is highly suitable for monitoring of vascular health as well as the heart's mechanical functioning.
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