Electrical and Electronic Engineering - Research Publications

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    Soft Pneumatic Actuators: A Review of Design, Fabrication, Modeling, Sensing, Control and Applications
    Xavier, MS ; Tawk, CD ; Zolfagharian, A ; Pinskier, J ; Howard, D ; Young, T ; Lai, J ; Harrison, SM ; Yong, YK ; Bodaghi, M ; Fleming, AJ (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2022-01-01)
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    Inferring Epistasis from Genetic Time-series Data
    Sohail, MS ; Louie, RHY ; Hong, Z ; Barton, JP ; McKay, MR ; Townsend, J (OXFORD UNIV PRESS, 2022-10-07)
    Epistasis refers to fitness or functional effects of mutations that depend on the sequence background in which these mutations arise. Epistasis is prevalent in nature, including populations of viruses, bacteria, and cancers, and can contribute to the evolution of drug resistance and immune escape. However, it is difficult to directly estimate epistatic effects from sampled observations of a population. At present, there are very few methods that can disentangle the effects of selection (including epistasis), mutation, recombination, genetic drift, and genetic linkage in evolving populations. Here we develop a method to infer epistasis, along with the fitness effects of individual mutations, from observed evolutionary histories. Simulations show that we can accurately infer pairwise epistatic interactions provided that there is sufficient genetic diversity in the data. Our method also allows us to identify which fitness parameters can be reliably inferred from a particular data set and which ones are unidentifiable. Our approach therefore allows for the inference of more complex models of selection from time-series genetic data, while also quantifying uncertainty in the inferred parameters.
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    Bayesian Detection of a Sinusoidal Signal With Randomly Varying Frequency
    Liu, C ; Suvorova, S ; Evans, RJ ; Moran, B ; Melatos, A (Institute of Electrical and Electronics Engineers (IEEE), 2022-01-01)
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    Beyond Pathogen Filtration: Possibility of Smart Masks as Wearable Devices for Personal and Group Health and Safety Management.
    Lee, P ; Kim, H ; Kim, Y ; Choi, W ; Zitouni, MS ; Khandoker, A ; Jelinek, HF ; Hadjileontiadis, L ; Lee, U ; Jeong, Y (JMIR Publications Inc., 2022-06-21)
    Face masks are an important way to combat the COVID-19 pandemic. However, the prolonged pandemic has revealed confounding problems with the current face masks, including not only the spread of the disease but also concurrent psychological, social, and economic complications. As face masks have been worn for a long time, people have been interested in expanding the purpose of masks from protection to comfort and health, leading to the release of various "smart" mask products around the world. To envision how the smart masks will be extended, this paper reviewed 25 smart masks (12 from commercial products and 13 from academic prototypes) that emerged after the pandemic. While most smart masks presented in the market focus on resolving problems with user breathing discomfort, which arise from prolonged use, academic prototypes were designed for not only sensing COVID-19 but also general health monitoring aspects. Further, we investigated several specific sensors that can be incorporated into the mask for expanding biophysical features. On a larger scale, we discussed the architecture and possible applications with the help of connected smart masks. Namely, beyond a personal sensing application, a group or community sensing application may share an aggregate version of information with the broader population. In addition, this kind of collaborative sensing will also address the challenges of individual sensing, such as reliability and coverage. Lastly, we identified possible service application fields and further considerations for actual use. Along with daily-life health monitoring, smart masks may function as a general respiratory health tool for sports training, in an emergency room or ambulatory setting, as protection for industry workers and firefighters, and for soldier safety and survivability. For further considerations, we investigated design aspects in terms of sensor reliability and reproducibility, ergonomic design for user acceptance, and privacy-aware data-handling. Overall, we aim to explore new possibilities by examining the latest research, sensor technologies, and application platform perspectives for smart masks as one of the promising wearable devices. By integrating biomarkers of respiration symptoms, a smart mask can be a truly cutting-edge device that expands further knowledge on health monitoring to reach the next level of wearables.
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    Privacy Aware Affective State Recognition From Visual Data
    Zitouni, MS ; Lee, P ; Lee, U ; Hadjileontiadis, LJ ; Khandoker, A (Institute of Electrical and Electronics Engineers (IEEE), 2022-01-01)
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    Deep learning identifies cardiac coupling between mother and fetus during gestation.
    Alkhodari, M ; Widatalla, N ; Wahbah, M ; Al Sakaji, R ; Funamoto, K ; Krishnan, A ; Kimura, Y ; Khandoker, AH (Frontiers Media SA, 2022)
    In the last two decades, stillbirth has caused around 2 million fetal deaths worldwide. Although current ultrasound tools are reliably used for the assessment of fetal growth during pregnancy, it still raises safety issues on the fetus, requires skilled providers, and has economic concerns in less developed countries. Here, we propose deep coherence, a novel artificial intelligence (AI) approach that relies on 1 min non-invasive electrocardiography (ECG) to explain the association between maternal and fetal heartbeats during pregnancy. We validated the performance of this approach using a trained deep learning tool on a total of 941 one minute maternal-fetal R-peaks segments collected from 172 pregnant women (20-40 weeks). The high accuracy achieved by the tool (90%) in identifying coupling scenarios demonstrated the potential of using AI as a monitoring tool for frequent evaluation of fetal development. The interpretability of deep learning was significant in explaining synchronization mechanisms between the maternal and fetal heartbeats. This study could potentially pave the way toward the integration of automated deep learning tools in clinical practice to provide timely and continuous fetal monitoring while reducing triage, side-effects, and costs associated with current clinical devices.
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    Testing for a Random Walk Structure in the Frequency Evolution of a Tone in Noise.
    Abramson, S ; Moran, W ; Evans, R ; Melatos, A (MDPI AG, 2022-08-15)
    Inference and hypothesis testing are typically constructed on the basis that a specific model holds for the data. To determine the veracity of conclusions drawn from such data analyses, one must be able to identify the presence of the assumed structure within the data. In this paper, a model verification test is developed for the presence of a random walk-like structure in the variations in the frequency of complex-valued sinusoidal signals measured in additive Gaussian noise. This test evaluates the joint inference of the random walk hypothesis tests found in economics literature that seek random walk behaviours in time series data, with an additional test to account for how the random walk behaves in frequency space.
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    Model-based estimation of QT intervals of mouse fetal electrocardiogram.
    Widatalla, N ; Funamoto, K ; Kawataki, M ; Yoshida, C ; Funamoto, K ; Saito, M ; Kasahara, Y ; Khandoker, A ; Kimura, Y (Springer Science and Business Media LLC, 2022-06-29)
    BACKGROUND: Abnormal prolongation in the QT interval or long QT syndrome (LQTS) is associated with several cardiac complications such as sudden infant death syndrome (SIDS). LQTS is believed to be linked to genetic mutations which can be understood by using animal models, such as mice models. Nevertheless, the research related to fetal QT interval in mice is still limited because of challenges associated with T wave measurements in fetal electrocardiogram (fECG). Reliable measurement of T waves is essential for estimating their end timings for QT interval assessment. RESULTS: A mathematical model was used to estimate QT intervals. Estimated QT intervals were validated with Q-aortic closure (Q-Ac) intervals of Doppler ultrasound (DUS) and comparison between both showed good agreement with a correlation coefficient higher than 0.88 (r > 0.88, P < 0.05). CONCLUSION: Model-based estimation of QT intervals can help in better understanding of QT intervals in fetal mice.
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    Machine Learning for Screening Microvascular Complications in Type 2 Diabetic Patients Using Demographic, Clinical, and Laboratory Profiles.
    Rashid, M ; Alkhodari, M ; Mukit, A ; Ahmed, KIU ; Mostafa, R ; Parveen, S ; Khandoker, AH (MDPI AG, 2022-02-09)
    Microvascular complications are one of the key causes of mortality among type 2 diabetic patients. This study was sought to investigate the use of a novel machine learning approach for predicting these complications using only the patient demographic, clinical, and laboratory profiles. A total of 96 Bangladeshi participants with type 2 diabetes were recruited during their routine hospital visits. All patient profiles were assessed by using a chi-squared (χ2) test to statistically determine the most important markers in predicting three microvascular complications: cardiac autonomic neuropathy (CAN), diabetic peripheral neuropathy (DPN), and diabetic retinopathy (RET). A machine learning approach based on logistic regression, random forest (RF), and support vector machine (SVM) algorithms was then developed to ensure automated clinical testing for microvascular complications in diabetic patients. The highest prediction accuracies were obtained by RF using diastolic blood pressure, albumin-creatinine ratio, and gender for CAN testing (98.67%); microalbuminuria, smoking history, and hemoglobin A1C for DPN testing (67.78%); and hemoglobin A1C, microalbuminuria, and smoking history for RET testing (84.38%). This study suggests machine learning as a promising automated tool for predicting microvascular complications in diabetic patients using their profiles, which could help prevent those patients from further microvascular complications leading to early death.
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    Progress in Brain Computer Interface: Challenges and Opportunities.
    Saha, S ; Mamun, KA ; Ahmed, K ; Mostafa, R ; Naik, GR ; Darvishi, S ; Khandoker, AH ; Baumert, M (Frontiers Media SA, 2021)
    Brain computer interfaces (BCI) provide a direct communication link between the brain and a computer or other external devices. They offer an extended degree of freedom either by strengthening or by substituting human peripheral working capacity and have potential applications in various fields such as rehabilitation, affective computing, robotics, gaming, and neuroscience. Significant research efforts on a global scale have delivered common platforms for technology standardization and help tackle highly complex and non-linear brain dynamics and related feature extraction and classification challenges. Time-variant psycho-neurophysiological fluctuations and their impact on brain signals impose another challenge for BCI researchers to transform the technology from laboratory experiments to plug-and-play daily life. This review summarizes state-of-the-art progress in the BCI field over the last decades and highlights critical challenges.