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Journal articleHadjipanayi C, Yin M, Bannon A, et al., 2024,
Remote gait analysis using ultra-wideband radar technology based on joint range-Doppler-time representation
, IEEE Transactions on Biomedical Engineering, Vol: 71, Pages: 2854-2865, ISSN: 0018-9294Objective: In recent years, radar technology has been extensively utilized in contactless human behavior monitoring systems. The unique capabilities of ultra-wideband (UWB) radars compared to conventional radar technologies, due to time-of-flight measurements, present new untapped opportunities for in-depth monitoring of human movement during overground locomotion. This study aims to investigate the deployability of UWB radars in accurately capturing the gait patterns of healthy individuals with no known walking impairments.Methods: A novel algorithm was developed that can extract ten clinical spatiotemporal gait features using the Doppler information captured from three monostatic UWB radar sensors during a 6-meter walking task. Key gait events are detected from lower-extremity movements based on the joint range-Doppler-time representation of recorded radar data. The estimated gait parameters were validated against a gold-standard optical motion tracking system using 12 healthy volunteers.Results: On average, nine gait parameters can be consistently estimated with 90-98% accuracy, while capturing 94.5% of participants' gait variability and 90.8% of inter-limb symmetry. Correlation and Bland-Altman analysis revealed a strong correlation between radar-based parameters and the ground-truth values, with average discrepancies consistently close to 0.Conclusion: Results prove that radar sensing can provide accurate biomarkers to supplement clinical human gait assessment, with quality similar to gold standard assessment.Significance: Radars can potentially allow a transition from expensive and cumbersome lab-based gait analysis tools toward a completely unobtrusive and affordable solution for in-home deployment, enabling continuous long-term monitoring of individuals for research and healthcare applications.
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Journal articleMiao A, Luo T, Hsieh B, et al., 2024,
Brain clearance is reduced during sleep and anesthesia
, Nature Neuroscience, Vol: 27, Pages: 1046-1050, ISSN: 1097-6256It has been suggested that the function of sleep is to actively clear metabolites and toxins from the brain. Enhanced clearance is also said to occur during anesthesia. Here, we measure clearance and movement of fluorescent molecules in the brains of male mice and show that movement is, in fact, independent of sleep and wake or anesthesia. Moreover, we show that brain clearance is markedly reduced, not increased, during sleep and anesthesia.
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Journal articleKhan S, Anderson W, Constandinou T, 2024,
Surgical Implantation of Brain Computer Interfaces
, JAMA SURGERY, Vol: 159, Pages: 219-220, ISSN: 2168-6254 -
Journal articleAlexandrou G, Mantikas K-T, Allsopp R, et al., 2023,
The evolution of affordable technologies in liquid biopsy diagnostics: the key to clinical implementation
, Cancers, Vol: 15, ISSN: 2072-6694Cancer remains a leading cause of death worldwide, despite many advances in diagnosis and treatment. Precision medicine has been a key area of focus, with research providing insights and progress in helping to lower cancer mortality through better patient stratification for therapies and more precise diagnostic techniques. However, unequal access to cancer care is still a global concern, with many patients having limited access to diagnostic tests and treatment regimens. Noninvasive liquid biopsy (LB) technology can determine tumour-specific molecular alterations in peripheral samples. This allows clinicians to infer knowledge at a DNA or cellular level, which can be used to screen individuals with high cancer risk, personalize treatments, monitor treatment response, and detect metastasis early. As scientific understanding of cancer pathology increases, LB technologies that utilize circulating tumour DNA (ctDNA) and circulating tumour cells (CTCs) have evolved over the course of research. These technologies incorporate tumour-specific markers into molecular testing platforms. For clinical translation and maximum patient benefit at a wider scale, the accuracy, accessibility, and affordability of LB tests need to be prioritized and compared with gold standard methodologies in current use. In this review, we highlight the range of technologies in LB diagnostics and discuss the future prospects of LB through the anticipated evolution of current technologies and the integration of emerging and novel ones. This could potentially allow a more cost-effective model of cancer care to be widely adopted.
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Journal articleTossell K, Yu X, Giannos P, et al., 2023,
Somatostatin neurons in prefrontal cortex initiate sleep preparatory behavior and sleep via the preoptic and lateral hypothalamus
, Nature Neuroscience, Vol: 26, Pages: 1805-1819, ISSN: 1097-6256The prefrontal cortex (PFC) enables mammals to respond to situations, including internal states, with appropriate actions. One such internal state could be ‘tiredness’. Here, using activity tagging in the mouse PFC, we identified particularly excitable, fast-spiking, somatostatin-expressing, γ-aminobutyric acid (GABA) (PFCSst-GABA) cells that responded to sleep deprivation. These cells projected to the lateral preoptic (LPO) hypothalamus and the lateral hypothalamus (LH). Stimulating PFCSst-GABA terminals in the LPO hypothalamus caused sleep-preparatory behavior (nesting, elevated theta power and elevated temperature), and stimulating PFCSst-GABA terminals in the LH mimicked recovery sleep (non-rapid eye-movement sleep with higher delta power and lower body temperature). PFCSst-GABA terminals had enhanced activity during nesting and sleep, inducing inhibitory postsynaptic currents on diverse cells in the LPO hypothalamus and the LH. The PFC also might feature in deciding sleep location in the absence of excessive fatigue. These findings suggest that the PFC instructs the hypothalamus to ensure that optimal sleep takes place in a suitable place.
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Journal articleZhang Z, Feng P, Oprea A, et al., 2023,
Calibration-free and hardware-efficient neural spike detection for brain machine interfaces
, IEEE Transactions on Biomedical Circuits and Systems, Vol: 17, Pages: 725-740, ISSN: 1932-4545Recent translational efforts in brain-machine interfaces (BMI) are demonstrating the potential to help people with neurological disorders. The current trend in BMI technology is to increase the number of recording channels to the thousands, resulting in the generation of vast amounts of raw data. This in turn places high bandwidth requirements for data transmission, which increases power consumption and thermal dissipation of implanted systems. On-implant compression and/or feature extraction are therefore becoming essential to limiting this increase in bandwidth, but add further power constraints – the power required for data reduction must remain less than the power saved through bandwidth reduction. Spike detection is a common feature extraction technique used for intracortical BMIs. In this paper, we develop a novel firing-rate-based spike detection algorithm that requires no external training and is hardware efficient and therefore ideally suited for real-time applications. Key performance and implementation metrics such as detection accuracy, adaptability in chronic deployment, power consumption, area utilization, and channel scalability are benchmarked against existing methods using various datasets. The algorithm is first validated using a reconfigurable hardware (FPGA) platform and then ported to a digital ASIC implementation in both 65 nm and 0.18MU m CMOS technologies. The 128-channel ASIC design implemented in a 65 nm CMOS technology occupies 0.096 mm2 silicon area and consumes 4.86MU W from a 1.2 V power supply. The adaptive algorithm achieves a 96% spike detection accuracy on a commonly used synthetic dataset, without the need for any prior training.
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Journal articleCavallo FR, Toumazou C, 2023,
Personalised lifestyle recommendations for type 2 diabetes: Design and simulation of a recommender system on UK Biobank Data.
, PLOS Digit Health, Vol: 2Mobile health applications, which employ wireless technology for healthcare, can aid behaviour change and subsequently improve health outcomes. Mobile health applications have been developed to increase physical activity, but are rarely grounded on behavioural theory and employ simple techniques for personalisation, which has been proven effective in promoting behaviour change. In this work, we propose a theoretically driven and personalised behavioural intervention delivered through an adaptive knowledge-based system. The behavioural system design is guided by the Behavioural Change Wheel and the Capability-Opportunity-Motivation behavioural model. The system exploits the ever-increasing availability of health data from wearable devices, point-of-care tests and consumer genetic tests to issue highly personalised physical activity and sedentary behaviour recommendations. To provide the personalised recommendations, the system firstly classifies the user into one of four diabetes clusters based on their cardiometabolic profile. Secondly, it recommends activity levels based on their genotype and past activity history, and finally, it presents the user with their current risk of developing cardiovascular disease. In addition, leptin, a hormone involved in metabolism, is included as a feedback biosignal to personalise the recommendations further. As a case study, we designed and demonstrated the system on people with type 2 diabetes, since it is a chronic condition often managed through lifestyle changes, such as physical activity increase and sedentary behaviour reduction. We trained and simulated the system using data from diabetic participants of the UK Biobank, a large-scale clinical database, and demonstrate that the system could help increase activity over time. These results warrant a real-life implementation of the system, which we aim to evaluate through human intervention.
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Journal articleZhang Z, Constandinou TG, 2023,
Firing-rate-modulated spike detection and neural decoding co-design
, JOURNAL OF NEURAL ENGINEERING, Vol: 20, ISSN: 1741-2560 -
Journal articleMartinez S, Veirano F, Constandinou TGG, et al., 2023,
Trends in Volumetric-Energy Efficiency of Implantable Neurostimulators: A Review From a Circuits and Systems Perspective
, IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS, Vol: 17, Pages: 2-20, ISSN: 1932-4545 -
Journal articleZhang Z, Constandinou TG, 2023,
Firing-rate-modulated spike detection and neural decoding co-design
<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>Translational efforts on spike-signal-based implantable brain-machine interfaces (BMIs) are increasingly aiming to minimise bandwidth while maintaining decoding performance. Developing these BMIs requires advances in neuroscience and electronic technology, as well as using low-complexity spike detection algorithms and high-performance machine learning models. While some state-of-the-art BMI systems jointly design spike detection algorithms and machine learning models, it remains unclear how the detection performance affects decoding.</jats:p></jats:sec><jats:sec><jats:title>Approach</jats:title><jats:p>We propose the co-design of the neural decoder with an ultra-low complexity spike detection algorithm. The detection algorithm is designed to attain a target firing rate, which the decoder uses to modulate the input features preserving statistical invariance.</jats:p></jats:sec><jats:sec><jats:title>Main results</jats:title><jats:p>We demonstrate a multiplication-free fixed-point spike detection algorithm with nearly perfect detection accuracy and the lowest complexity among studies we have seen. By co-designing the system to incorporate statistically invariant features, we observe significantly improved long-term stability, with decoding accuracy degrading by less than 10% after 80 days of operation. Our analysis also reveals a nonlinear relationship between spike detection and decoding performance. Increasing the detection sensitivity improves decoding accuracy and long-term stability, which means the activity of more neurons is beneficial despite the detection of more noise. Reducing the spike detection sensitivity still provides acceptable decoding accuracy whilst reducing the bandwidth by at least 30%.</jats:p></jats:sec><jats:sec><jats:title>Signifi
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Conference paperHyanda MH, Ahmadi N, Charlton PH, et al., 2023,
A Comparative Evaluation of Video Codecs for rPPG-based Heart Rate Estimation
, Asia-Pacific-Signal-and-Information-Processing-Association Annual Summit and Conference (APSIPA ASC), Publisher: IEEE, Pages: 243-247, ISSN: 2309-9402 -
Conference paperWong SS, Radford J, Faccio D, et al., 2023,
Multielectrode Multiplexing for Bioimpedance Surface Topography Mapping
, IEEE BioSensors Conference (BioSensors), Publisher: IEEE -
Conference paperMeimandi A, Feng P, Carminati M, et al., 2023,
Implantable Biosensor for Brain Dopamine using Microwire-Based Electrodes
, IEEE BioSensors Conference (BioSensors), Publisher: IEEE -
Conference paperWong SS, Radford J, Binner P, et al., 2023,
Multimodal Approaches for Real-time Mesoscopic Tissue Differentiation
, IEEE BioSensors Conference (BioSensors), Publisher: IEEE -
Conference paperOzbek B, Constandinou TG, 2023,
An Autonomous Zero-Mask Unique ID Generation System for Next-Generation Neural Interfaces
, 21st IEEE Interregional NEWCAS Conference (NEWCAS), Publisher: IEEE, ISSN: 2472-467X -
Journal articleWong SS, Malik A, Ekanayake J, et al., 2023,
Towards Real-time Multiplexed Bioimpedance Tumour-Tissue Margin Analysis
, 2023 45TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY, EMBC, ISSN: 1557-170X -
Conference paperAlexandrou G, Moser N, Ali S, et al., 2023,
Distinguishing <i>PIK3CA</i> p.E545K mutational status from pseudogene DNA with a next-generation ISFET sensor array
, 56th IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, ISSN: 0271-4302 -
Conference paperNairac Z, Constandinou TG, 2023,
Design of a Novel, Low-Cost System for Neural Electrical Impedance Tomography
, 11th International IEEE EMBS Conference on Neural Engineering (IEEE/EMBS NER), Publisher: IEEE, ISSN: 1948-3546 -
Conference paperMifsud A, Constandinou TG, 2023,
Towards a CMOS-Process-Portable ReRAM PDK
, 21st IEEE Interregional NEWCAS Conference (NEWCAS), Publisher: IEEE, ISSN: 2472-467X -
Journal articleStanchieri GDP, De Marcellis A, Battisti G, et al., 2022,
A Multilevel Synchronized Optical Pulsed Modulation for High Efficiency Biotelemetry
, IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS, Vol: 16, Pages: 1313-1324, ISSN: 1932-4545- Author Web Link
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