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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 -
Conference paperLuo J, Firfilionis D, Ramezani R, et al., 2017,
Live demonstration: a closed-loop cortical brain implant for optogenetic curing epilepsy
, IEEE Biomedical Circuits and Systems (BioCAS) Conference, Publisher: IEEE, Pages: 169-169 -
Conference paperSzostak K, Mazza F, Maslik M, et al., 2017,
Microwire-CMOS Integration of mm-Scale Neural Probes for Chronic Local Field Potential Recording
, IEEE Biomedical Circuits and Systems (BioCAS) Conference, Publisher: IEEE, Pages: 492-495 -
Conference paperDe Marcellis A, Palange E, Faccio M, et al., 2017,
A 250Mbps 24pJ/bit UWB-inspired Optical Communication System for Bioimplants
, Turin, Italy, IEEE Biomedical Circuits and Systems (BioCAS) Conference, Pages: 132-135 -
Conference paperKhwaja M, Kalofonou M, Toumazou C, 2017,
A Deep Belief Network system for prediction of DNA methylation
, IEEE Biomedical Circuits and Systems Conference (BioCAS), Publisher: IEEEA Deep Belief Network architecture is proposed for prediction of DNA methylation characteristics across genetic regions. The proposed system uses an image analogous visualisation of DNA methylation features through an efficient mapping model. Implementation of this method has resulted in an accurate classification of DNA methylation for multiple CpG regions identified in cancer cell lines and has been designed to address variability in patterns found in a given human cell, regardless of their function or disease state. The proposed method is compared to time-tested supervised learning algorithms that include Support Vector Machine and Random Forest classifiers and has been validated using data from cancer cell lines. Using documented features, it achieves differentiation of DNA methylation states, while predicting distinct features with an average value of sensitivity 92%, specificity 99%, accuracy 95% and Matthew's Correlation Coefficient 0.91. The feature set coupled with the deep learning model makes the system efficient for DNA methylation prediction, while being independent of the data set used.
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Conference paperGuven O, Eftekhar A, Kindt W, et al., 2017,
Low-power real-time ECG baseline wander removal: hardware implementation
, IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, Pages: 1571-1574This paper presents a hardware realisation of a novel ECG baseline drift removal that preserves the ECG signal integrity. The microcontroller implementation detects the fiducial markers of the ECG signal and the baseline wander estimation is achieved through a weighted piecewise linear interpolation. This estimated drift is then removed to recover a “clean” ECG signal without significantly distorting the ST segment. Experimental results using real data from the MIT-BIH Arrhythmia Database (recording 100 and 101) with added baseline wander (BWM1) from the MIT-BIH Noise Stress Database show an average root mean square error of 34.3uV (mean), 30.4u V (median) and 18.4uV (standard deviation) per heart beat.
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Conference paperGao C, Ghoreishizadeh S, Liu Y, et al., 2017,
On-chip ID generation for multi-node implantable devices using SA-PUF
, IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, Pages: 678-681This paper presents a 64-bit on-chip identification system featuring low power consumption and randomness compensation for multi-node bio-implantable devices. A sense amplifier based bit-cell is proposed to realize the silicon physical unclonable function, providing a unique value whose probability has a uniform distribution and minimized influence from the temperature and supply variation. The entire system is designed and implemented in a typical 0.35 m CMOS technology, including an array of 64 bit-cells, readout circuits, and digital controllers for data interfaces. Simulated results show that the proposed bit-cell design achieved a uniformity of 50.24% and a uniqueness of 50.03% for generated IDs. The system achieved an energy consumption of 6.0 pJ per bit with parallel outputs and 17.3 pJ per bit with serial outputs.
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Conference paperHaci D, Liu Y, Constandinou TG, 2017,
32-channel ultra-low-noise arbitrary signal generation platform for biopotential emulation
, IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, Pages: 698-701This paper presents a multichannel, ultra-low-noise arbitrary signal generation platform for emulating a wide range of different biopotential signals (e.g. ECG, EEG, etc). This is intended for use in the test, measurement and demonstration of bioinstrumentation and medical devices that interface to electrode inputs. The system is organized in 3 key blocks for generating, processing and converting the digital data into a parallel high performance analogue output. These blocks consist of: (1) a Raspberry Pi 3 (RPi3) board; (2) a custom Field Programmable Gate Array (FPGA) board with low-power IGLOO Nano device; and (3) analogue board including the Digital-to-Analogue Converters (DACs) and output circuits. By implementing the system this way, good isolation can be achieved between the different power and signal domains. This mixed-signal architecture takes in a high bitrate SDIO (Secure Digital Input Output) stream, recodes and packetizes this to drive two multichannel DACs, with parallel analogue outputs that are then attenuated and filtered. The system achieves 32-parallel output channels each sampled at 48kS/s, with a 10kHz bandwidth, 110dB dynamic range and uV-level output noise.
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Conference paperMaslik M, Liu Y, Lande TS, et al., 2017,
A charge-based ultra-low power continuous-time ADC for data driven neural spike processing
, IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, Pages: 1420-1423The paper presents a novel topology of a continuous-time analogue-to-digital converter (CT-ADC) featuring ultra-low static power consumption, activity-dependent dynamic consumption, and a compact footprint. This is achieved by utilising a novel charge-packet based threshold generation method, that alleviates the requirement for a conventional feedback DAC. The circuit has a static power consumption of 3.75uW, with dynamic energy of 1.39pJ/conversion level. This type of converter is thus particularly well-suited for biosignals that are generally sparse in nature. The circuit has been optimised for neural spike recording by capturing a 3kHz bandwidth with 8-bit resolution. For a typical extracellular neural recording the average power consumption is in the order of ~4uW. The circuit has been implemented in a commercially available 0.35um CMOS technology with core occupying a footprint of 0.12 sq.mm
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Conference paperLeene L, Constandinou TG, 2017,
A 0.5V time-domain instrumentation circuit with clocked and unclocked ΔΣ operation
, IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, Pages: 2619-2622, ISSN: 2379-447XThis paper presents a time-domain instrumentation circuit with exceptional noise efficiency directed at using nanometre CMOS for next generation neural interfaces. Current efforts to realize closed loop neuromodulation and high fidelity BMI prosthetics rely extensively on digital processing which isnot well integrated with conventional analogue instrumentation. The proposed time-domain topology employs a differential ring oscillator that is put into feedback using a chopper stabilized low noise transconductor and capacitive feedback. This realization promises better digital integration by extensively using time encoded digital signals and seamlessly allows both clocked & unclocked ΔΣ behavior which is useful on-chip characterizationand interfacing with synchronous systems. A 0.5V instrumentation system is implemented using a 65nm TSMC technology to realize a highly compact footprint that is 0.006mm2 in size. Simulation results demonstrate an excess of 55 dB dynamic range with 3.5 Vrms input referred noise for the given 810nW total system power budget corresponding to an NEF of 1.64.
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Conference paperMoser N, Rodriguez-Manzano J, Yu L-S, et al., 2017,
Live Demonstration: A CMOS-Based ISFET Array for Rapid Diagnosis of the Zika Virus
, IEEE International Symposium on Circuits and Systems (ISCAS) 2017, ISSN: 2379-447XWe demonstrate a diagnostics platform which integrates an ISFET array and a temperature control loop for isothermal DNA detection. The controller maintains a temperature of 63◦C to perform nucleic acid amplification which is detected by the on-chip sensors. The 32x32 ISFET array is first calibrated to cancel trapped charge and then measures the change in the pH of the reaction. The sensor data is sent to a microcontroller and the reaction is monitored in real-time using a MATLAB interface. Experiments confirm a change of 0.9 pH when tested for the presence of RNA associated with the Zika virus.
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Conference paperDávila-Montero S, Barsakcioglu DY, Jackson A, et al., 2017,
Real-time clustering algorithm that adapts to dynamic changes in neural recordings
, IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, Pages: 690-693This work presents a computationally efficient real-time adaptive clustering algorithm that recognizes and adapts to dynamic changes observed in neural recordings. The algorithm consists of an off-line training phase that determines initial cluster positions, and an on-line operation phase that continuously tracks drifts in clusters and periodically verifies acute changes in cluster composition. Analysis of chronic recordings from non-human primates shows that adaptive clustering achieves an improvement of 14% in classification accuracy and demonstrates an ability to recognize acute changes with 78% accuracy, with up to 29% computational efficiency compared to the state-of-the-art. The presented algorithm is suitable for long-term chronic monitoring of neural activity in various applications such as neuroscience research and control of neural prosthetics and assistive devices.
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Conference paperMirza KB, Zuliani C, Hou B, et al., 2017,
Injection moulded microneedle sensor for real-time wireless pH monitoring
, 39th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC), Publisher: IEEE, Pages: 189-192, ISSN: 1094-687XThis paper describes the development of an array of individually addressable pH sensitive microneedles using injection moulding and their integration within a portable device for real-time wireless recording of pH distributions in biological samples. The fabricated microneedles are subjected to gold patterning followed by electrodeposition of iridium oxide to sensitize them to 0.07 units of pH change. Miniaturised electronics suitable for the sensors readout, analog-to-digital conversion and wireless transmission of the potentiometric data are embodied within the device, enabling it to measure real-time pH of soft biological samples such as muscles. In this paper, real-time recording of the cardiac pH distribution, during ischemia followed by reperfusion cycles in cardiac muscles of male Wistar rats has been demonstrated by using the microneedle array.
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Conference paperMirza KB, Kulasekeram N, Toumazou C, 2017,
Current feedback neural amplifier with real time electrode offset suppression
, International Midwest Symposium on Circuits and Systems (MWSCAS), Publisher: IEEE, Pages: 1077-1080, ISSN: 1548-3746This paper describes a direct coupled neural amplifier with active electrode offset suppression in order to avoid large coupling capacitors and complex chopper circuits. It describes a novel feedback scheme, where a low pass current mode feedback is applied to a regulated telescopic cascode amplifier, at the cascode nodes by using a modified transconductance block. This solution leads to fully differential input-differential output direct coupled neural amplifier, achieving a DC offset suppression range of ±200 mV, a chip area of 0.078 mm 2 per channel and an input referred noise of 2.5 μV rms over 1 Hz-5kHz bandwidth.
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Journal articleGhoreishizadeh S, Haci D, Liu Y, et al., 2017,
Four-Wire Interface ASIC for a Multi-Implant Link
, IEEE Transactions on Circuits and Systems I: Regular Papers, Vol: 64, Pages: 3056-3067, ISSN: 1549-8328This paper describes an on-chip interface for recovering power and providing full-duplex communication over an AC-coupled 4-wire lead between active implantable devices. The target application requires two modules to be implanted in the brain (cortex) and upper chest; connected via a subcutaneous lead. The brain implant consists of multiple identical ‘optrodes’ that facilitate a bidirectional neural interface (electrical recording, optical stimulation), and chest implant contains the power source (battery) and processor module. The proposed interface is integrated within each optrode ASIC allowing full-duplex and fully-differential communication based on Manchester encoding. The system features a head-to-chest uplink data rate(up to 1.6 Mbps) that is higher than that of the chest-to-head downlink (100 kbps) which is superimposed on a power carrier. On-chip power management provides an unregulated 5V DC supply with up to 2.5mA output current for stimulation, and two regulated voltages (3.3V and 3V) with 60 dB PSRR for recording and logic circuits. The 4-wire ASIC has been implemented in a 0.35 um CMOS technology, occupying 1.5mm2 silicon area,and consumes a quiescent current of 91.2u A. The system allows power transmission with measured efficiency of up to 66% from the chest to the brain implant. The downlink and uplink communication are successfully tested in a system with two optrodes and through a 4-wire implantable lead.
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Conference paperMoser N, Panteli C, Ma D, et al., 2017,
Improving the pH Sensitivity of ISFET Arrays withReactive Ion Etching
, BioCAS 2017, Publisher: IEEEIn this paper, we report a method to improvesensitivity for CMOS ISFET arrays using Reactive Ion Etching(RIE) as a post-processing technique. The process etches awaythe passivation layers of the commercial CMOS process, using anoxygen (O2) and sulfur hexafluoride (SF6) plasma. The resultingattenuation and pH sensitivity are characterised for five diesetched for 0 to 15 minutes, and we demonstrate that capacitiveattenuation is reduced by 196% and pH sensitivity increasedby 260% compared to the non-etched equivalent. The spread oftrapped charge is also reduced which relaxes requirements on theanalogue front-end. The technique significantly improves the performanceof the fully-integrated sensing system for applicationssuch as DNA detection.
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Conference paperTroiani F, Nikolic K, Constandinou TG, 2017,
Optical coherence tomography for compound action potential detection: a computational study
, SPIE/OSA European Conferences on Biomedical Optics (ECBO), Publisher: Optical Society of America / SPIE, Pages: 1-3The feasibility of using time domain optical coherence tomography (TD-OCT) to detect compound action potential in a peripheral nerve and the setup characteristics, are studied through the use of finite-difference time-domain (FDTD) technique.
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Conference paperLuo JW, Firfilionis D, Ramezani R, et al., 2017,
Live demonstration: A closed-loop cortical brain implant for optogenetic curing epilepsy
A closed-loop optogenetic system for curing epilepsy is presented in this work. As it shown at figure 1, the system consists of a cortical brain implant with LEDs and recording electrodes, a customer designed CMOS chip[1][2][3] and a controller. The brain activities are recorded by the implant with recording electronics in a CMOS chip, the signals are processed by the controller, and the results are send back to the CMOS chip for delivering LED stimulation commands.
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Journal articleHerrero P, Bondia J, Adewuyi O, et al., 2017,
Enhancing automatic closed-loop glucose control in type 1 diabetes with an adaptive meal bolus calculator - in silico evaluation under intra- day variability
, Computer Methods and Programs in Biomedicine, Vol: 146, Pages: 125-131, ISSN: 0169-2607Background and ObjectiveCurrent prototypes of closed-loop systems for glucose control in type 1 diabetes mellitus, also referred to as artificial pancreas systems, require a pre-meal insulin bolus to compensate for delays in subcutaneous insulin absorption in order to avoid initial post-prandial hyperglycemia. Computing such a meal bolus is a challenging task due to the high intra-subject variability of insulin requirements. Most closed-loop systems compute this pre-meal insulin dose by a standard bolus calculation, as is commonly found in insulin pumps. However, the performance of these calculators is limited due to a lack of adaptiveness in front of dynamic changes in insulin requirements. Despite some initial attempts to include adaptation within these calculators, challenges remain.MethodsIn this paper we present a new technique to automatically adapt the meal-priming bolus within an artificial pancreas. The technique consists of using a novel adaptive bolus calculator based on Case-Based Reasoning and Run-To-Run control, within a closed-loop controller. Coordination between the adaptive bolus calculator and the controller was required to achieve the desired performance. For testing purposes, the clinically validated Imperial College Artificial Pancreas controller was employed. The proposed system was evaluated against itself but without bolus adaptation. The UVa-Padova T1DM v3.2 system was used to carry out a three-month in silico study on 11 adult and 11 adolescent virtual subjects taking into account inter-and intra-subject variability of insulin requirements and uncertainty on carbohydrate intake.ResultsOverall, the closed-loop controller enhanced by an adaptive bolus calculator improves glycemic control when compared to its non-adaptive counterpart. In particular, the following statistically significant improvements were found (non-adaptive vs. adaptive). Adults: mean glucose 142.2 ± 9.4 vs. 131.8 ± 4.2 mg/dl; perce
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