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Journal articleMaslik M, Liu Y, Lande TS, et al., 2018,
Continuous-time acquisition of biosignals using a charge-based ADC topology
, IEEE Transactions on Biomedical Circuits and Systems, Vol: 12, Pages: 471-482, ISSN: 1932-4545This paper investigates continuous-time (CT) signal acquisition as an activity-dependent and nonuniform sampling alternative to conventional fixed-rate digitisation. We demonstrate the applicability to biosignal representation by quantifying the achievable bandwidth saving by nonuniform quantisation to commonly recorded biological signal fragments allowing a compression ratio of ≈5 and 26 when applied to electrocardiogram and extracellular action potential signals, respectively. We describe several desirable properties of CT sampling, including bandwidth reduction, elimination/reduction of quantisation error, and describe its impact on aliasing. This is followed by demonstration of a resource-efficient hardware implementation. We propose a novel circuit topology for a charge-based CT analogue-to-digital converter that has been optimized for the acquisition of neural signals. This has been implemented in a commercially available 0.35 μm CMOS technology occupying a compact footprint of 0.12 mm 2 . Silicon verified measurements demonstrate an 8-bit resolution and a 4 kHz bandwidth with static power consumption of 3.75 μW from a 1.5 V supply. The dynamic power dissipation is completely activity-dependent, requiring 1.39 pJ energy per conversion.
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Conference paperGantier M, Kalofonou M, Toumazou C, 2018,
A trapped charge compensation scheme for ISFET based translinear circuits
, IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEEA trapped charge compensation scheme for ISFET based translinear circuits is presented, as part of a system for prediction of cancer risk, based on DNA methylation. Each pixel is able to measure a DNA methylation ratio through pH-based measurements and by using in-pixel comparison to a tunable threshold, to output a result which indicates percentage of methylation used as a cancer score. The developed system was designed in a 0.35 μm CMOS technology and uses a novel trapped charge compensation scheme for ISFETs used in translinear circuits. The output scheme was able to compensate trapped charge of up to 380mV, with a ratio error below 5%, in a range of ratios between 50% and 80% which is generated from pH-based DNA methylation reactions.
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Conference paperKulasekeram N, Wildner K, Mirza KB, et al., 2018,
Reconfigurable Low-noise Multichannel Amplifier for Neurochemical Recording
, IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, ISSN: 0271-4302 -
Conference paperLeene L, Maslik M, Feng P, et al., 2018,
Autonomous SoC for neural local field potential recording in mm-scale wireless implants
, IEEE International Symposium on Circuits and Systems, Publisher: IEEE, Pages: 1-5, ISSN: 2379-447XNext generation brain machine interfaces fundamentally need to improve the information transfer rate and chronic consistency when observing neural activity over a long period of time. Towards this aim, this paper presents a novel System-on-Chip (SoC) for a mm-scale wireless neural recording node that can be implanted in a distributed fashion. The proposed self-regulating architecture allows each implant to operate autonomously and adaptively load the electromagnetic field to extract a precise amount of power for full-system operation. This can allow for a large number of recording sites across multiple implants extending through cortical regions without increased control overhead in the external head-stage. By observing local field potentials (LFPs) only, chronic stability is improved and good coverage is achieved whilst reducing the spatial density of recording sites. The system features a ΔΣ based instrumentation circuit that digitises high fidelity signal features at the sensor interface thereby minimising analogue resource requirements while maintaining exceptional noise efficiency. This has been implemented in a 0.35 μm CMOS technology allowing for wafer-scale post-processing for integration of electrodes, RF coil, electronics and packaging within a 3D structure. The presented configuration will record LFPs from 8 electrodes with a 825 Hz bandwidth and an input referred noise figure of 1.77μVrms. The resulting electronics has a core area of 2.1 mm2 and a power budget of 92 μW
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Conference paperWildner K, Kulasekeram N, Mirza KB, et al., 2018,
Live Demo: Reconfigurable Low-noise Multichannel Amplifier for Neurochemical Recording
, IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, ISSN: 0271-4302 -
Journal articleMirza K, Alenda A, Eftekhar A, et al., 2018,
Influence of cholecystokinin-8 on compound nerve action potentials from ventral gastric vagus in rats
, International Journal of Neural Systems, Vol: 28, ISSN: 0129-0657Objective:Vagus Nerve Stimulation (VNS) has shown great promise as a potential therapy for anumber of conditions, such as epilepsy, depression and forNeurometabolic Therapies, especially fortreating obesity. The objective of this study was to characterise the left ventral subdiaphragmaticgastric trunk of vagus nerve (SubDiaGVN) and to analyse the influence of intravenous injection of guthormone cholecystokinin octapeptide (CCK-8) on compound nerve action potential (CNAP) observedon the same branch, with the aim of understanding the impact of hormones on VNS and incorporatingthe methods and results into closed loop implant design.Methods:The cervical region of the left vagus nerve (CerVN) of male Wistar rats was stimulatedwith electric current and the elicited CNAPs were recorded on the SubDiaGVN under four differentconditions:Control(no injection),Saline,CCK1(100 pmol/kg) andCCK2(1000 pmol/kg) injections.Results:We identified the presence of Aδ, B, C1, C2, C3 and C4 fibres with their respective velocityranges. Intravenous administration of CCKin vivoresults in selective, statistically significant reductionof CNAP components originating from A and B fibres, but with no discernible effect on the C fibresinn=7animals. The affected CNAP components exhibit statistically significant (pSaline−CCK1= 0.02andpSaline−CCK2= 0.007) higher normalized stimulation thresholds.Conclusion:This approach of characterising the vagus nerve can be used in closed loop systems todeterminewhento initiate VNS and also to tune the stimulation dose, which is patient specific andchanges over time.
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Journal articleRawson T, Moore L, Castro Sanchez E, et al., 2018,
Development of a patient-centred intervention to improve knowledge and understanding of antibiotic therapy in secondary care
, Antimicrobial Resistance and Infection Control, Vol: 7, ISSN: 2047-2994Introduction: We developed a personalised antimicrobial information module co-designed with patients. This study aimed to evaluate the potential impact of this patient-centred intervention on short-term knowledge and understanding of antimicrobial therapy in secondary care. Methods:Thirty previous patients who had received antibiotics in hospital within 12 months were recruited to co-design an intervention to promote patient engagement with infection management. Two workshops, containing five focus-groups were held. These were audio-recorded. Data were analysed using a thematic framework developed deductively based on previous work. Line-by-line coding was performed with new themes added to the framework by two researchers. This was used to inform the development of a patient information module, embedded within an electronic decision support tool (CDSS). The intervention was piloted over a four-week period at Imperial College Healthcare NHS Trust on 30 in-patients. Pre- and post-intervention questionnaires were developed and implemented to assess short term changes in patient knowledge and understanding and provide feedback on the intervention. Data were analysed using SPSS and NVIVO software. Results: Within the workshops, there was consistency in identified themes. The participants agreed upon and co-designed a personalised PDF document that could be integrated into an electronic CDSS to be used by healthcare professionals at the point-of-care. Their aim for the tool was to provide individualised practical information, signpost to reputable information sources, and enhance communication between patients and healthcare professionals.Eighteen out of thirty in-patients consented to participant in the pilot evaluation with 15/18(83%) completing the study. Median (range) age was 66(22-85) years. The majority were male (10/15;66%). Pre-intervention, patients reported desiring further information regarding their infections and antibiotic therapy, including side effects
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PatentWilliams I, Rapeaux A, Luan S, et al., 2018,
Waveform Generator
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Journal articleChen C-H, Karvela M, Sohbati M, et al., 2018,
PERSON - Personalized Expert Recommendation System for Optimized Nutrition
, IEEE Transactions on Biomedical Circuits and Systems, Vol: 12, Pages: 151-160, ISSN: 1932-4545The rise of personalized diets is due to the emergence of nutrigenetics and genetic tests services. However, the recommendation system is far from mature to provide personalized food suggestion to consumers for daily usage. The main barrier of connecting genetic information to personalized diets is the complexity of data and the scalability of the applied systems. Aiming to cross such barriers and provide direct applications, a personalized expert recommendation system for optimized nutrition is introduced in this paper, which performs direct to consumer personalized grocery product filtering and recommendation. Deep learning neural network model is applied to achieve automatic product categorization. The ability of scaling with unknown new data is achieved through the generalized representation of word embedding. Furthermore, the categorized products are filtered with a model based on individual genetic data with associated phenotypic information and a case study with databases from three different sources is carried out to confirm the system.
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Journal articleCork SC, Eftekhar A, Mirza KB, et al., 2018,
Extracellular pH monitoring for use in closed-loop vagus nerve stimulation
, Journal of Neural Engineering, Vol: 15, Pages: 1-11, ISSN: 1741-2552Objective: Vagal nerve stimulation (VNS) has shown potential benefits for obesity treatment; however, current devices lack physiological feedback, which limit their efficacy. Changes in extracellular pH (pHe) have shown to be correlated with neural activity, but have traditionally been measured with glass microelectrodes, which limit their in vivo applicability. Approach. Iridium oxide has previously been shown to be sensitive to fluctuations in pH and is biocompatible. Iridium oxide microelectrodes were inserted into the subdiaphragmatic vagus nerve of anaesthetised rats. Introduction of the gut hormone cholecystokinin (CCK) or distension of the stomach was used to elicit vagal nerve activity. Main results. Iridium oxide microelectrodes have sufficient pH sensitivity to readily detect changes in pHe associated with both CCK and gastric distension. Furthermore, a custom-made Matlab script was able to use these changes in pHe to automatically trigger an implanted VNS device. Significance. This is the first study to show pHe changes in peripheral nerves in vivo. In addition, the demonstration that iridium oxide microelectrodes are sufficiently pH sensitive as to measure changes in pHe associated with physiological stimuli means they have the potential to be integrated into closed-loop neurostimulating devices.
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Journal articleLiu Y, Pereira J, Constandinou TG, 2018,
Event-driven processing for hardware-efficient neural spike sorting
, Journal of Neural Engineering, Vol: 15, Pages: 1-14, ISSN: 1741-2552Objective. The prospect of real-time and on-node spike sorting provides a genuine opportunity to push the envelope of large-scale integrated neural recording systems. In such systems the hardware resources, power requirements and data bandwidth increase linearly with channel count. Event-based (or data-driven) processing can provide here a new efficient means for hardware implementation that is completely activity dependant. In this work, we investigate using continuous-time level-crossing sampling for efficient data representation and subsequent spike processing. Approach. (1) We first compare signals (synthetic neural datasets) encoded with this technique against conventional sampling. (2) We then show how such a representation can be directly exploited by extracting simple time domain features from the bitstream to perform neural spike sorting. (3) The proposed method is implemented in a low power FPGA platform to demonstrate its hardware viability. Main results. It is observed that considerably lower data rates are achievable when using 7 bits or less to represent the signals, whilst maintaining the signal fidelity. Results obtained using both MATLAB and reconfigurable logic hardware (FPGA) indicate that feature extraction and spike sorting accuracies can be achieved with comparable or better accuracy than reference methods whilst also requiring relatively low hardware resources. Significance. By effectively exploiting continuous-time data representation, neural signal processing can be achieved in a completely event-driven manner, reducing both the required resources (memory, complexity) and computations (operations). This will see future large-scale neural systems integrating on-node processing in real-time hardware.
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Book chapterWilliams I, Leene L, Constandinou TG, 2018,
Next Generation Neural Interface Electronics
, Circuit Design Considerations for Implantable Devices, Editors: Cong, Publisher: River Publishers, Pages: 141-178, ISBN: 978-87-93519-86-2 -
PatentCavuto ML, Winter AG, Constandinou T, 2018,
Apparatus and Method for Inserting Electrode-based Probes into Biological Tissue
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Journal articleLiu Y, Luan S, Williams I, et al., 2017,
A 64-Channel Versatile Neural Recording SoC with Activity Dependant Data Throughput
, IEEE Transactions on Biomedical Circuits and Systems, Vol: 11, Pages: 1344-1355, ISSN: 1932-4545Modern microtechnology is enabling the channel count of neural recording integrated circuits to scale exponentially. However, the raw data bandwidth of these systems is increasing proportionately, presenting major challenges in terms of power consumption and data transmission (especially for wireless systems). This paper presents a system that exploits the sparse nature of neural signals to address these challenges and provides a reconfigurable low-bandwidth event-driven output. Specifically, we present a novel 64-channel low noise (2.1μVrms, low power (23μW per analogue channel) neural recording system-on-chip (SoC). This features individually-configurable channels, 10-bit analogue-to-digital conversion, digital filtering, spike detection, and an event-driven output. Each channel's gain, bandwidth & sampling rate settings can be independently configured to extract Local Field Potentials (LFPs) at a low data-rate and/or Action Potentials (APs) at a higher data rate. The sampled data is streamed through an SRAM buffer that supports additional on-chip processing such as digital filtering and spike detection. Real-time spike detection can achieve ~2 orders of magnitude data reduction, by using a dual polarity simple threshold to enable an event driven output for neural spikes (16-sample window). The SoC additionally features a latency-encoded asynchronous output that is critical if used as part of a closed-loop system. This has been specifically developed to complement a separate on-node spike sorting co-processor to provide a real-time (low latency) output. The system has been implemented in a commercially-available 0.35μm CMOS technology occupying a silicon area of 19.1mm² (0.3mm² gross per channel), demonstrating a low power & efficient architecture which could be further optimised by aggressive technology and supply voltage scaling.
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Journal articleHernandez Perez B, Herrero Viñas P, Miles Rawson T, et al., 2017,
Supervised Learning for Infection Risk Inference Using Pathology Data
, BMC Medical Informatics and Decision Making, Vol: 17, ISSN: 1472-6947Background: Antimicrobial Resistance is threatening our ability to treat common infectious diseases and overuse of antimicrobials to treat human infections in hospitals is accelerating this process. Clinical Decision Support Systems (CDSSs) have been proven to enhance quality of care by promoting change in prescription practices through antimicrobial selection advice. However, bypassing an initial assessment to determine the existence of an underlying disease that justifies the need of antimicrobial therapy might lead to indiscriminate and often unnecessary prescriptions.Methods: From pathology laboratory tests, six biochemical markers were selected and combined with microbiology outcomes from susceptibility tests to create a unique dataset with over one and a half million daily profiles to perform infection risk inference. Outliers were discarded using the inter-quartile range rule and several sampling techniques were studied to tackle the class imbalance problem. The first phase selects the most effective and robust model during training using four-fold stratified cross-validation. The second phase evaluates the final model after isotonic calibration in scenarios with missing inputs and imbalanced class distributions. Results: More than 50\% of infected profiles have daily requested laboratory tests for the six biochemical markers with very promising infection inference results: area under the receiver operating characteristic curve (0.80-0.83), sensitivity (0.64-0.75) and specificity (0.92-0.97). Standardization consistently outperforms normalization and sensitivity is enhanced by using the SMOTE sampling technique. Furthermore, models operated without noticeable loss in performance if at least four biomarkers were available.Conclusion: The selected biomarkers comprise enough information to perform infection risk inference with a high degree of confidence even in the presence of incomplete and imbalanced data. Since they are commonly available in hospitals, Clini
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Journal articleSzostak K, Grand L, Constandinou TG, 2017,
Neural interfaces for intracortical recording: requirements, fabrication methods, and characteristics
, Frontiers in Neuroscience, Vol: 11, ISSN: 1662-4548Implantable neural interfaces for central nervous system research have been designed with wire, polymer or micromachining technologies over the past 70 years. Research on biocompatible materials, ideal probe shapes and insertion methods has resulted in building more and more capable neural interfaces. Although the trend is promising, the long-term reliability of such devices has not yet met the required criteria for chronic human application. The performance of neural interfaces in chronic settings often degrades due to foreign body response to the implant that is initiated by the surgical procedure, and related to the probe structure, and material properties used in fabricating the neural interface. In this review, we identify the key requirements for neural interfaces for intracortical recording, describe the three different types of probes- microwire, micromachined and polymer-based probes; their materials, fabrication methods, and discuss their characteristics and related challenges.
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Journal articleLeene L, Constandinou TG, 2017,
Time domain processing techniques using ring oscillator-based filter structures
, IEEE Transactions on Circuits and Systems Part 1: Regular Papers, Vol: 64, Pages: 3003-3012, ISSN: 1549-8328The ability to process time-encoded signals with high fidelity is becoming increasingly important for the time domain (TD) circuit techniques that are used at the advanced nanometer technology nodes. This paper proposes a compact oscillator-based subsystem that performs precise filtering of asynchronous pulse-width modulation encoded signals and makes extensive use of digital logic, enabling low-voltage operation. First- and second-order primitives are introduced that can be used as TD memory or to enable analogue filtering of TD signals. These structures can be modeled precisely to realize more advanced linear or nonlinear functionality using an ensemble of units. This paper presents the measured results of a prototype fabricated using a 65-nm CMOS technology to realize a fourth- order low-pass Butterworth filter. The system utilizes a 0.5-V supply voltage with asynchronous digital control for closed-loop operation to achieve a 73-nW power budget. The implemented filter achieves a maximum signal to noise and distortion ratio of 53 dB with a narrow 5-kHz bandwidth resulting in an figure- of-merit of 8.2 fJ/pole. With this circuit occupying a compact 0.004-mm2 silicon footprint, this technique promises a substantial reduction in size over conventional Gm-C filters, whilst addition- ally offering direct integration with digital systems.
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PatentConstandinou TG, Jackson A, 2017,
Implantable Neural Interface
A neural interface arrangement comprising: a plurality of probes for subdural implantation into or onto a human brain, each probe including at least one sensing electrode, a coil for receiving power via inductive coupling, signal processing circuitry coupled to the sensing electrode(s), and means for wirelessly transmitting data-carrying signals arising from the sensing electrode(s); an array of coils for implantation above the dura, beneath the skull, the array of coils being for inductively coupling with the coil of each of the plurality of probes, for transmitting power to the probes; and a primary (e.g. subcutaneous) coil connected to the array of coils, the primary coil being for inductively coupling with an external transmitter device, for receiving power from the external transmitter device; wherein, in use, the primary coil is operable to receive power from the external transmitter device by inductive coupling and to cause the array of coils to transmit power to the plurality of probes by inductive coupling; and wherein, in use, the plurality of probes are operable to wirelessly transmit data-carrying signals arising from the sensing electrodes.
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Conference paperFeng P, Constandinou TG, Yeon P, et al., 2017,
Millimeter-Scale Integrated and Wirewound Coils for Powering Implantable Neural Microsystems
, IEEE Biomedical Circuits and Systems (BioCAS) Conference, Pages: 488-491 -
Conference paperMifsud A, Haci D, Ghoreishizadeh S, et al., 2017,
Adaptive Power Regulation and Data Delivery for Multi-Module Implants
, IEEE Biomedical Circuits and Systems (BioCAS) Conference, Publisher: IEEE, Pages: 584-587
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