A Hidden Markov Factor Analysis Framework for Seizure Detection in Epilepsy Patients

Approximately 1% of the world population suffers from epilepsy. Continuous long-term electroencephalographic (EEG) monitoring is the gold-standard for recording epileptic seizures and assisting in the diagnosis and treatment of patients with epilepsy. Detection of seizure from the recorded EEG is a laborious, time consuming and expensive task. In this study, we propose an automated seizure detection framework to assist electroencephalographers and physicians with identification of seizures in recorded EEG signals. In addition, an automated seizure detection algorithm can be used for treatment through automatic intervention during the seizure activity and on time triggering of the injection of a radiotracer to localize the seizure activity. In this study, we developed and tested a hidden Markov factor analysis (HMFA) framework for automated seizure detection based on different features such as total effective inflow which is calculated based on connectivity measures between different sites of the brain. The algorithm was tested on long-term (2.4-7.66 days) continuous sEEG recordings from three patients and a total of 16 seizures, producing a mean sensitivity of 96.3% across all seizures, a mean specificity of 3.47 false positives per hour, and a mean latency of 3.7 seconds form the actual seizure onset. The latency was negative for a few of the seizures which implies the proposed method detects the seizure prior to its onset. This is an indication that with some extension the proposed method is capable of seizure prediction

Preventive Maintenance Decision Modeling in Health and Service Systems

This dissertation focuses on the preventive maintenance decision modeling in healthcare and service systems. In the first part of this dissertation, some issues in preventive health decisions for breast cancer are addressed, and in the second part, the required characteristics for preventive maintenance of an unreliable queuing system are derived. Adherence to cancer screening is the first issue that is addressed in this dissertation. Women’s adherence or compliance with mammography screening remained low in the recent years. In this dissertation, we first develop a design-based logistic regression model to quantify the probability of adherence to screening schedules based on women’s characteristics. In Chapter 3, we develop a randomized finite-horizon partially observable Markov chain to evaluate and compare different mammography screening strategies for women with different adherence behaviors in terms of quality adjusted life years (QALYs) and lifetime breast cancer mortality risk. The results imply that for the general population, the American Cancer Society (ACS) policy is an efficient frontier policy. In Chapter 4, the problem of overdiagnosis in cancer screening is addressed. Overdiagnosis is a side effect of screening and is defined as the diagnosis of a disease that will never cause symptoms or death during a patient\u27s lifetime. We develop a mathematical framework to quantify the lifetime overdiagnosis and mortality risk for different screening policies, and derive the (near) optimal policies with minimum overdiagnosis risk. In the second part, we consider an unreliable queuing system with servers stored in a shared stack. In such a system, servers have heterogeneous transient usage since servers on the top of the stack are more likely to be used. We develop a continuous-time Markov chain model to derive the utilization and usage time of servers in the system. These quantities are critical for the decision maker for deriving a maintenance policy

Framework of Intelligent Systems to Support Project Scope

Non

Longitudinal optical imaging technique to visualize progressive axonal damage after brain injury in mice reveals responses to different minocycline treatments

A high-resolution, three-dimensional, optical imaging technique for the murine brain was developed to identify the effects of different therapeutic windows for preclinical brain research. This technique tracks the same cells over several weeks. We conducted a pilot study of a promising drug to treat diffuse axonal injury (DAI) caused by traumatic brain injury, using two different therapeutic windows, as a means to demonstrate the utility of this novel longitudinal imaging technique. DAI causes immediate, sporadic axon damage followed by progressive secondary axon damage. We administered minocycline for three days commencing one hour after injury in one treatment group and beginning 72 hours after injury in another group to demonstrate the method’s ability to show how and when the therapeutic drug exerts protective and/or healing effects. Fewer varicosities developed in acutely treated mice while more varicosities resolved in mice with delayed treatment. For both treatments, the drug arrested development of new axonal damage by 30 days. In addition to evaluation of therapeutics for traumatic brain injury, this hybrid microlens imaging method should be useful to study other types of brain injury and neurodegeneration and cellular responses to treatment

Using artificial intelligence to detect human errors in nuclear power plants: A case in operation and maintenance

Human error (HE) is an important concern in safety-critical systems such as nuclear power plants (NPPs). HE has played a role in many accidents and outage incidents in NPPs. Despite the increased automation in NPPs, HE remains unavoidable. Hence, the need for HE detection is as important as HE prevention efforts. In NPPs, HE is rather rare. Hence, anomaly detection, a widely used machine learning technique for detecting rare anomalous instances, can be repurposed to detect potential HE. In this study, we develop an unsupervised anomaly detection technique based on generative adversarial networks (GANs) to detect anomalies in manually collected surveillance data in NPPs. More specifically, our GAN is trained to detect mismatches between automatically recorded sensor data and manually collected surveillance data, and hence, identify anomalous instances that can be attributed to HE. We test our GAN on both a real-world dataset and an external dataset obtained from a testbed, and we benchmark our results against state-of-the-art unsupervised anomaly detection algorithms, including one-class support vector machine and isolation forest. Our results show that the proposed GAN provides improved anomaly detection performance. Our study is promising for the future development of artificial intelligence based HE detection systems

Application of Artificial Intelligence in Detection and Mitigation of Human Factor Errors in Nuclear Power Plants: A Review

Human factors and ergonomics have played an essential role in increasing the safety and performance of operators in the nuclear energy industry. In this critical review, we examine how artificial intelligence (AI) technologies can be leveraged to mitigate human errors, thereby improving the safety and performance of operators in nuclear power plants (NPPs). First, we discuss the various causes of human errors in NPPs. Next, we examine the ways in which AI has been introduced to and incorporated into different types of operator support systems to mitigate these human errors. We specifically examine (1) operator support systems, including decision support systems, (2) sensor fault detection systems, (3) operation validation systems, (4) operator monitoring systems, (5) autonomous control systems, (6) predictive maintenance systems, (7) automated text analysis systems, and (8) safety assessment systems. Finally, we provide some of the shortcomings of the existing AI technologies and discuss the challenges still ahead for their further adoption and implementation to provide future research directions

Insight into blood pressure targets for universal coverage of hypertension services in Iran: the 2017 ACC/AHA versus JNC 8 hypertension guidelines

BACKGROUND: We compared the prevalence, awareness, treatment, and control of hypertension in Iran based on two hypertension guidelines; the 2017 ACC/AHA -with an aggressive blood pressure target of 130/80 mmHg- and the commonly used JNC8 guideline cut-off of 140/90 mmHg. We shed light on the implications of the 2017 ACC/AHA for population subgroups and high-risk individuals who were eligible for non-pharmacologic and pharmacologic therapies. METHODS: Data was obtained from the Iran national STEPS 2016 study. Participants included 27,738 adults aged ≥25 years as a representative sample of Iranians. Regression models of survey design were used to examine the determinants of prevalence, awareness, treatment, and control of hypertension. RESULTS: The prevalence of hypertension based on JNC8 was 29.9% (95% CI: 29.2-30.6), which soared to 53.7% (52.9-54.4) based on the 2017 ACC/AHA. The percentage of awareness, treatment, and control were 59.2% (58.0-60.3), 80.2% (78.9-81.4), and 39.1% (37.4-40.7) based on JNC8, which dropped to 37.1% (36.2-38.0), 71.3% (69.9-72.7), and 19.6% (18.3-21.0), respectively, by applying the 2017 ACC/AHA. Based on the new guideline, adults aged 25-34 years had the largest increase in prevalence (from 7.3 to 30.7%). They also had the lowest awareness and treatment rate, contrary to the highest control rate (36.5%) between age groups. Compared with JNC8, based on the 2017 ACC/AHA, 24, 15, 17, and 11% more individuals with dyslipidaemia, high triglycerides, diabetes, and cardiovascular disease events, respectively, fell into the hypertensive category. Yet, based on the 2017 ACC/AHA, 68.2% of individuals falling into the hypertensive category were eligible for receiving pharmacologic therapy (versus 95.7% in JNC8). LDL cholesterol< 130 mg/dL, sufficient physical activity (Metabolic Equivalents≥600/week), and Body Mass Index were found to change blood pressure by - 3.56(- 4.38, - 2.74), - 2.04(- 2.58, - 1.50), and 0.48(0.42, 0.53) mmHg, respectively. CONCLUSIONS: Switching from JNC8 to 2017 ACC/AHA sharply increased the prevalence and drastically decreased the awareness, treatment, and control in Iran. Based on the 2017 ACC/AHA, more young adults and those with chronic comorbidities fell into the hypertensive category; these individuals might benefit from earlier interventions such as lifestyle modifications. The low control rate among individuals receiving treatment warrants a critical review of hypertension services

Hepatocellular carcinoma incidence at national and provincial levels in Iran from 2000 to 2016: A meta-regression analysis.

BackgroundThe incidence of Hepatocellular carcinoma (HCC), the most common primary liver cancer with high mortality, is undergoing global change due to evolving risk factor profiles. We aimed to describe the epidemiologic incidence of HCC in Iran by sex, age, and geographical distribution from 2000 to 2016.MethodsWe used the Iran Cancer Registry to extract cancer incidence data and applied several statistical procedures to overcome the dataset's incompleteness and misclassifications. Using Spatio-temporal and random intercept mixed effect models, we imputed missing values for cancer incidence by sex, age, province, and year. Besides, we addressed case duplicates and geographical misalignments in the data.ResultsAge-standardized incidence rate (ASIR) increased 1.17 times from 0.57 (95% UI: 0.37-0.78) per 100,000 population in 2000 to 0.67 (0.50-0.85) in 2016. It had a 21.8% total percentage change increase during this time, with a 1.28 annual percentage change in both sexes. Male to female ASIR ratio was 1.51 in 2000 and 1.57 in 2016. Overall, after the age of 50 years, HCC incidence increased dramatically with age and increased from 1.19 (0.98-1.40) in the 50-55 age group to 6.65 (5.45-7.78) in the >85 age group. The geographical distribution of this cancer was higher in the central, southern, and southwestern regions of Iran.ConclusionThe HCC incidence rate increased from 2000 to 2016, with a more significant increase in subgroups such as men, individuals over 50 years of age, and the central, southern, and southwestern regions of the country. We recommend health planners and policymakers to adopt more preventive and screening strategies for high-risk populations and provinces in Iran