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    Woods S, Constandinou TG, 2015,

    Engineering Micro Mechanical Systems for the Next Generation Wireless Capsule Endoscopy

    , BioMed Research International, Vol: 2015, ISSN: 2314-6141

    Wireless capsule endoscopy (WCE) enables the detection and diagnosis of in ammatory bowel diseases such as Crohn's disease and ulcerative colitis. However treatment of these pathologies through the administering of therapy can only be achieved through conventional means. This paper describes the next generation wireless capsule endoscopy which has increased functionality to allow for targeted drug delivery in the small intestinal tract. A prototype microrobot fabricated in Nylon 6 is presented which is capable of resisting natural peristaltic pressure through the deployment of an integrated holding mechanism and delivering targeted therapy. The holding action is achieved by extending an \anchor" spanning an effective 60.4mm circumference, for a 11.0mm diameterWCE. This function is achieved by a mechanism that occupies only 347.0mm3 volume, including mechanics and actuator. A micro-positioning mechanism is described which utilises a single micromotor to radially position then deploy a needle 1.5mm outside the microrobot's body for the purpose of delivering a 1 ml dose of medication to a targeted site. An analysis of the mechanics required to drive the holding mechanism is presented and an overview of micro-actuators and the state of the art in WCE is discussed. The novel mechanisms offer increased functionality to WCE. It is envisaged that this increased ability to perform targeted therapy will empower the next generation of WCE to help diagnose and treat pathologies of the GI tract.

    Woods SP, Constandinou TG, 2015,

    A Novel Holding Mechanism for Next Generation Active Wireless Capsule Endoscopy

    , 37th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC), Publisher: IEEE, Pages: 1181-1185, ISSN: 1557-170X
    Zhao H, Dehkhoda F, Ramezani R, Sokolov D, Degenaar P, Liu Y, Constandinou Tet al., 2015,

    A CMOS-based Neural Implantable Optrode for Optogenetic Stimulation and Electrical Recording

    , 11th IEEE Annual Biomedical Circuits and Systems Conference (BioCAS), Publisher: IEEE, Pages: 286-289, ISSN: 2163-4025
    Barsakcioglu D, Liu Y, Bhunjun P, Navajas J, Eftekhar A, Jackson A, Quian Quiroga R, Constandinou TGet al., 2014,

    An Analogue Front-End Model for Developing Neural Spike Sorting Systems

    , IEEE Transactions on Biomedical Circuits and Systems, Vol: 8, Pages: 216-227
    Demarchou E, Georgiou J, Nicolaou N, Constandinou Tet al., 2014,

    Anesthetic-induced changes in EEG activity: a graph theoretical approach

    , IEEE Biomedical Circuits and Systems Conference (BioCAS), Publisher: IEEE, Pages: 45-48, ISSN: 2163-4025
    Eftekhar A, Juffali W, El-Imad J, Constandinou TG, Toumazou Cet al., 2014,

    Ngram-Derived Pattern Recognition for the Detection and Prediction of Epileptic Seizures

    , PLOS ONE, Vol: 9, ISSN: 1932-6203
    Guven O, Eftekhar A, Hoshyar R, Frattini G, Kindt W, Constandinou TGet al., 2014,

    Realtime ECG Baseline Removal: An Isoelectric Point Estimation Approach

    , IEEE Biomedical Circuits and Systems Conference (BioCAS), Publisher: IEEE, Pages: 29-32, ISSN: 2163-4025
    Leene LB, Constandinou TG, 2014,

    Ultra-low power design strategy for two-stage amplifier topologies

    , ELECTRONICS LETTERS, Vol: 50, Pages: 583-584, ISSN: 0013-5194
    Luan S, Constandinou TG, 2014,

    A charge-metering method for voltage-mode neural stimulation

    , JOURNAL OF NEUROSCIENCE METHODS, Vol: 224, Pages: 39-47, ISSN: 0165-0270
    Luan S, Williams I, Nikolic K, Constandinou TGet al., 2014,

    Neuromodulation: present and emerging methods.

    , Front Neuroeng, Vol: 7, ISSN: 1662-6443

    Neuromodulation has wide ranging potential applications in replacing impaired neural function (prosthetics), as a novel form of medical treatment (therapy), and as a tool for investigating neurons and neural function (research). Voltage and current controlled electrical neural stimulation (ENS) are methods that have already been widely applied in both neuroscience and clinical practice for neuroprosthetics. However, there are numerous alternative methods of stimulating or inhibiting neurons. This paper reviews the state-of-the-art in ENS as well as alternative neuromodulation techniques-presenting the operational concepts, technical implementation and limitations-in order to inform system design choices.

    Navajas J, Barsakcioglu D, Eftekhar A, Jackson A, Constandinou TG, Quian Quiroga Ret al., 2014,

    Minimum Requirements for Accurate and Efficient Real-Time On-Chip Spike Sorting

    , Journal of Neuroscience Methods, Pages: 51-64
    Paraskevopoulou SE, Wu D, Eftekhar A, Constandinou TGet al., 2014,

    Hierarchical Adaptive Means (HAM) Clustering for Hardware-Efficient, Unsupervised and Real-time Spike Sorting.

    , Journal of Neuroscience Methods, Vol: 235, Pages: 145-156, ISSN: 1872-678X

    This work presents a novel unsupervised algorithm for real-time adaptive clustering of neural spike data (spike sorting). The proposed Hierarchical Adaptive Means (HAM) clustering method combines centroid-based clustering with hierarchical cluster connectivity to classify incoming spikes using groups of clusters. It is described how the proposed method can adaptively track the incoming spike data without requiring any past history, iteration or training and autonomously determines the number of spike classes. Its performance (classification accuracy) has been tested using multiple datasets (both simulated and recorded) achieving a near-identical accuracy compared to k-means (using 10-iterations and provided with the number of spike classes). Also, its robustness in applying to different feature extraction methods has been demonstrated by achieving classification accuracies above 80% across multiple datasets. Last but crucially, its low complexity, that has been quantified through both memory and computation requirements makes this method hugely attractive for future hardware implementation.

    Reverter F, Prodromakis T, Liu Y, Georgiou P, Nikolic K, Constandinou TGet al., 2014,

    Design Considerations for a CMOS Lab-on-Chip Microheater Array to Facilitate the in vitro Thermal Stimulation of Neurons

    , IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, Pages: 630-633
    Shepherd LM, Constandinou TG, Toumazou C, 2014,

    Towards ultra-low power bio-inspired processing

    , Body Sensor Networks, Publisher: Springer London, Pages: 273-299, ISBN: 9781447163732

    The natural world is analogue and yet the modern microelectronic world with which we interact represents real world data using discrete quantities manipulated by logic. In the human space, we are entering a new wave of body-worn biosensor technology for medical diagnostics and therapy. This new trend is beginning to see the processing interface move back to using continuous quantities, which are more or less in line with the biological processes. We label this computational paradigm “bio-inspired” because of the ability of silicon chip technology which enables the use of inherent device physics, allowing us to approach the computational efficiencies of biology. From a conceptual viewpoint, this has led to a number of more specific morphologies including neuromorphic and retinomorphic processing. These have led scientists to model biological systems such as the cochlea and retina and gain not only superior computational resource efficiency (to conventional hearing aid or camera technology), but also an increased understanding of biological and neurological processes.

    Williams I, Constandinou TG, 2014,

    Computationally efficient modeling of proprioceptive signals in the upper limb for prostheses: a simulation study

    Yang Y, Boling S, Eftekhar A, Paraskevopoulou SE, Constandinou TG, Mason AJet al., 2014,

    Computationally Efficient Feature Denoising Filter and Selection of Optimal Features for Noise Insensitive Spike Sorting

    , 36th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC), Publisher: IEEE, Pages: 1251-1254, ISSN: 1557-170X
    Yoshizaki S, Serb A, Liu Y, Constandinou TGet al., 2014,

    Octagonal CMOS Image Sensor with Strobed RGB LED Illumination for Wireless Capsule Endoscopy

    , IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, Pages: 1857-1860, ISSN: 0271-4302
    Zheng L, Leene LB, Liu Y, Constandinou TGet al., 2014,

    An Adaptive 16/64 kHz, 9-bit SAR ADC with Peak-Aligned Sampling for Neural Spike Recording

    , IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, Pages: 2385-2388, ISSN: 0271-4302
    Barsakcioglu DY, Eftekhar A, Constandinou TG, 2013,

    Design Optimisation of Front-End Neural Interfaces for Spike Sorting Systems

    , IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, Pages: 2501-2504, ISSN: 0271-4302
    Koutsos E, Paraskevopoulou SE, Constandinou TG, 2013,

    A 1.5μW NEO-based Spike Detector with Adaptive-Threshold for Calibration-free Multichannel Neural Interfaces

    , IEEE International Symposium on Circuits and Systems (ISCAS)

    This paper presents a novel front-end circuit for detecting action potentials in extracellular neural recordings. By implementing a real-time, adaptive algorithm to determine an effective threshold for robustly detecting a spike, the need for calibration and/or external monitoring is eliminated. The input signal is first pre-processed by utilising a non-linear energy operator (NEO) to effectively boost the signal-to-noise ratio (SNR) of the spike feature of interest. The spike detection threshold is then determined by tracking the peak NEO response and applying a non-linear gain to realise an adaptive response to different spike amplitudes and background noise levels. The proposed algorithm and its implementation is shown to achieve both accurate and robust spike detection, by minimising falsely detected spikes and/or missed spikes. The system has been implemented in a commercially available 0.18μm technology requiring a total power consumption of 1.5μW from a 1.8V supply and occupying a compact footprint of only 0.03$\,$mm$^2$ silicon area. The proposed circuit is thus ideally suited for high-channel count, calibration-free, neural interfaces.

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