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General General

Wearable, Multimodal, Biosignal Acquisition System for Potential Critical and Emergency Applications.

In Emergency medicine international

For emergency or intensive-care units (ICUs), patients with unclear consciousness or unstable hemodynamics often require aggressive monitoring by multiple monitors. Complicated pipelines or lines increase the burden on patients and inconvenience for medical personnel. Currently, many commercial devices provide related functionalities. However, most devices measure only one biological signal, which can increase the budget for users and cause difficulty in remote integration. In this study, we develop a wearable device that integrates electrocardiography (ECG), electroencephalography (EEG), and blood oxygen machines for medical applications with the hope that it can be applied in the future. We develop an integrated multiple-biosignal recording system based on a modular design. The developed system monitors and records EEG, ECG, and peripheral oxygen saturation (SpO2) signals for health purposes simultaneously in a single setting. We use a logic level converter to connect the developed EEG module (BR8), ECG module, and SpO2 module to a microcontroller (Arduino). The modular data are then smoothly encoded and decoded through consistent overhead byte stuffing (COBS). This developed system has passed simulation tests and exhibited proper functioning of all modules and subsystems. In the future, the functionalities of the proposed system can be expanded with additional modules to support various emergency or ICU applications.

Lin Chin-Teng, Wang Chen-Yu, Huang Kuan-Chih, Horng Shi-Jinn, Liao Lun-De


General General

COVID-19 and the Clinical Phase of the Medical Doctorate Curriculum in Oman: Challenges and the way forward.

In Sultan Qaboos University medical journal

COVID-19 has gripped the world with lightning speed. Since the onset of the pandemic, activity throughout the world came to a grinding halt. However, business had to continue and people have to learn to live with the virus while the pandemic continues to rage. Medical education is no exception and may even deserve special mention, as it prepares frontline workers against the endemics of tomorrow. We discuss here the journey of medical education at the College of Medicine and Health Sciences at Sultan Qaboos University, Muscat, Oman, as the pandemic struck the world and Oman. This work suggests a roadmap for changes, discusses challenges and proposes measures to mitigate the effects of COVID-19 on medical schools.

Burney Ikram A, Abdwani Reem, Al-Hashmi Khamis, Al-Wardy Nadia, Al-Saadoon Muna


Artificial Intelligence, COVID-19, Computer Simulation, Curriculum, Medical Education, Oman

General General

Evidence of memory from brain data.

In Journal of law and the biosciences

Much courtroom evidence relies on assessing witness memory. Recent advances in brain imaging analysis techniques offer new information about the nature of autobiographical memory and introduce the potential for brain-based memory detection. In particular, the use of powerful machine-learning algorithms reveals the limits of technological capacities to detect true memories and contributes to existing psychological understanding that all memory is potentially flawed. This article first provides the conceptual foundation for brain-based memory detection as evidence. It then comprehensively reviews the state of the art in brain-based memory detection research before establishing a framework for admissibility of brain-based memory detection evidence in the courtroom and considering whether and how such use would be consistent with notions of justice. The central question that this interdisciplinary analysis presents is: if the science is sophisticated enough to demonstrate that accurate, veridical memory detection is limited by biological, rather than technological, constraints, what should that understanding mean for broader legal conceptions of how memory is traditionally assessed and relied upon in legal proceedings? Ultimately, we argue that courtroom admissibility is presently a misdirected pursuit, though there is still much to be gained from advancing our understanding of the biology of human memory.

Murphy Emily R D, Rissman Jesse

brain, court, evidence, fMRI, machine learning, memory detection

General General

Predicting mortality in hemodialysis patients using machine learning analysis.

In Clinical kidney journal

Background : Besides the classic logistic regression analysis, non-parametric methods based on machine learning techniques such as random forest are presently used to generate predictive models. The aim of this study was to evaluate random forest mortality prediction models in haemodialysis patients.

Methods : Data were acquired from incident haemodialysis patients between 1995 and 2015. Prediction of mortality at 6 months, 1 year and 2 years of haemodialysis was calculated using random forest and the accuracy was compared with logistic regression. Baseline data were constructed with the information obtained during the initial period of regular haemodialysis. Aiming to increase accuracy concerning baseline information of each patient, the period of time used to collect data was set at 30, 60 and 90 days after the first haemodialysis session.

Results : There were 1571 incident haemodialysis patients included. The mean age was 62.3 years and the average Charlson comorbidity index was 5.99. The mortality prediction models obtained by random forest appear to be adequate in terms of accuracy [area under the curve (AUC) 0.68-0.73] and superior to logistic regression models (ΔAUC 0.007-0.046). Results indicate that both random forest and logistic regression develop mortality prediction models using different variables.

Conclusions : Random forest is an adequate method, and superior to logistic regression, to generate mortality prediction models in haemodialysis patients.

Garcia-Montemayor Victoria, Martin-Malo Alejandro, Barbieri Carlo, Bellocchio Francesco, Soriano Sagrario, Pendon-Ruiz de Mier Victoria, Molina Ignacio R, Aljama Pedro, Rodriguez Mariano


haemodialysis, machine learning, mortality, predictive models, random forest

Public Health Public Health

Digital health and primary care: Past, pandemic and prospects.

In Journal of global health

This article reflects on the breadth of digital developments seen in primary care over time, as well as the rapid and significant changes prompted by the COVID-19 crisis. Recent research and experience have shone further light on factors influencing the implementation and usefulness of these approaches, as well as unresolved challenges and unintended consequences. These are considered in relation to not only digital technology and infrastructure, but also wider aspects of health systems, the nature of primary care work and culture, patient characteristics and inequalities, and ethical issues around data privacy, inclusion, empowerment, empathy and trust. Implications for the future direction and sustainability of these approaches are discussed, taking account of novel paradigms, such as artificial intelligence, and the growing capture of primary care data for secondary uses. Decision makers are encouraged to think holistically about where value is most likely to be added, or risks being taken away, when judging which innovations to carry forward. It concludes that, while responding to this public health emergency has created something of a digital 'big bang' for primary care, an incremental, adaptive, patient-centered strategy, focused on augmenting rather than replacing existing services, is likely to prove most fruitful in the longer term.

Pagliari Claudia


Surgery Surgery

Intraoperative Quantitative Measurements for Bradykinesia Evaluation during Deep Brain Stimulation Surgery Using Leap Motion Controller: A Pilot Study.

In Parkinson's disease

Deep brain stimulation (DBS) has shown a remarkably high effectiveness for Parkinson's disease (PD). In many PD patients during DBS surgery, the therapeutic effects of the stimulation test are estimated by assessing changes in bradykinesia as the stimulation voltage is increased. In this study, we evaluated the potential of the leap motion controller (LMC) to quantify the motor component of bradykinesia in PD during DBS surgery, as this could make the intraoperative assessment of bradykinesia more accurate. Seven participants with idiopathic PD receiving chronic bilateral subthalamic nucleus deep brain stimulation (DBS) therapy were recruited. The motor tasks of finger tapping (FT), hand opening and closing (OC), and hand pronation and supination (PS) were selected pre- and intraoperatively in accordance with the Movement Disorder Society revision of the Unified Parkinson's Disease Rating Scale. During the test, participants performed these tasks in sequence while being simultaneously monitored by the LMC and two professional clinicians. Key kinematic parameters differed between the preoperative and intraoperative conditions. We suggest that the average velocity ( V ¯ ) and average amplitude ( A ¯ ) of PS isolate the bradykinetic feature from that movement to provide a measure of the intraoperative state of the motor system. The LMC achieved promising results in evaluating PD patients' hand and finger bradykinesia during DBS surgery.

Wu Jingchao, Yu Ningbo, Yu Yang, Li Haitao, Wu Fan, Yang Yuchen, Lin Jianeng, Han Jianda, Liang Siquan