Correlation analysis of vital signs to monitor disease risks in ubiquitous healthcare system

Healthcare systems for chronic diseases demand continuous monitoring of physiological parameters or vital signs of the patients’ body. Through these vital signs’ information, healthcare experts attempt to diagnose the behavior of a disease. Identifying the relationship between these vital signs is still a big question for the research community. We have proposed a sophisticated way to identify the affiliations between vital signs of three specific diseases i.e., Sepsis, Sleep Apnea, and Intradialytic Hypotension (IDH) through Pearson statistical correlation analysis. Vital signs data of about 32 patients were taken for analysis. Experimental results show significant affiliations of vital signs of Sepsis and IDH with average correlation coefficient of 0.9 and 0.58, respectively. The stability of the mentioned correlation is about 75% and 90%, respectively

Real-Time Detection of Demand Manipulation Attacks on a Power Grid

An increased usage in IoT devices across the globe has posed a threat to the power grid. When an attacker has access to multiple IoT devices within the same geographical location, they can possibly disrupt the power grid by regulating a botnet of high-wattage IoT devices. Based on the time and situation of the attack, an adversary needs access to a fixed number of IoT devices to synchronously switch on/off all of them, resulting in an imbalance between the supply and demand. When the frequency of the power generators drops below a threshold value, it can lead to the generators tripping and potentially failing. Attacks such as these can cause an imbalance in the grid frequency, line failures and cascades, can disrupt a black start or increase the operating cost. The challenge lies in early detection of abnormal demand peaks in a large section of the power grid from the power operator’s side, as it only takes seconds to cause a generator failure before any action could be taken. Anomaly detection comes handy to flag the power operator of an anomalous behavior while such an attack is taking place. However, it is difficult to detect anomalies especially when such attacks are taking place obscurely and for prolonged time periods. With this motive, we compare different anomaly detection systems in terms of detecting these anomalies collectively. We generate attack data using real-world power consumption data across multiple apartments to assess the performance of various prediction-based detection techniques as well as commercial detection applications and observe the cases when the attacks were not detected. Using static thresholds for the detection process does not reliably detect attacks when they are performed in different times of the year and also lets the attacker exploit the system to create the attack obscurely. To combat the effects of using static thresholds, we propose a novel dynamic thresholding mechanism, which improves the attack detection reaching up to 100% detection rate, when used with prediction-based anomaly score techniques

Anomaly detection in smart city wireless sensor networks

Aquesta tesi proposa una plataforma de detecció d’intrusions per a revelar atacs a les xarxes de sensors sense fils (WSN, per les sigles en anglès) de les ciutats intel·ligents (smart cities). La plataforma està dissenyada tenint en compte les necessitats dels administradors de la ciutat intel·ligent, els quals necessiten accés a una arquitectura centralitzada que pugui gestionar alarmes de seguretat en un sistema altament heterogeni i distribuït. En aquesta tesi s’identifiquen els diversos passos necessaris des de la recollida de dades fins a l’execució de les tècniques de detecció d’intrusions i s’avalua que el procés sigui escalable i capaç de gestionar dades típiques de ciutats intel·ligents. A més, es comparen diversos algorismes de detecció d’anomalies i s’observa que els mètodes de vectors de suport d’una mateixa classe (one-class support vector machines) resulten la tècnica multivariant més adequada per a descobrir atacs tenint en compte les necessitats d’aquest context. Finalment, es proposa un esquema per a ajudar els administradors a identificar els tipus d’atacs rebuts a partir de les alarmes disparades.Esta tesis propone una plataforma de detección de intrusiones para revelar ataques en las redes de sensores inalámbricas (WSN, por las siglas en inglés) de las ciudades inteligentes (smart cities). La plataforma está diseñada teniendo en cuenta la necesidad de los administradores de la ciudad inteligente, los cuales necesitan acceso a una arquitectura centralizada que pueda gestionar alarmas de seguridad en un sistema altamente heterogéneo y distribuido. En esta tesis se identifican los varios pasos necesarios desde la recolección de datos hasta la ejecución de las técnicas de detección de intrusiones y se evalúa que el proceso sea escalable y capaz de gestionar datos típicos de ciudades inteligentes. Además, se comparan varios algoritmos de detección de anomalías y se observa que las máquinas de vectores de soporte de una misma clase (one-class support vector machines) resultan la técnica multivariante más adecuada para descubrir ataques teniendo en cuenta las necesidades de este contexto. Finalmente, se propone un esquema para ayudar a los administradores a identificar los tipos de ataques recibidos a partir de las alarmas disparadas.This thesis proposes an intrusion detection platform which reveals attacks in smart city wireless sensor networks (WSN). The platform is designed taking into account the needs of smart city administrators, who need access to a centralized architecture that can manage security alarms in a highly heterogeneous and distributed system. In this thesis, we identify the various necessary steps from gathering WSN data to running the detection techniques and we evaluate whether the procedure is scalable and capable of handling typical smart city data. Moreover, we compare several anomaly detection algorithms and we observe that one-class support vector machines constitute the most suitable multivariate technique to reveal attacks, taking into account the requirements in this context. Finally, we propose a classification schema to assist administrators in identifying the types of attacks compromising their networks

Temporal Characteristics of High-Frequency Oscillations as a Biomarker of Human Epilepsy

Epilepsy is a debilitating neurological disorder characterized by recurrent spontaneous seizures. While seizures themselves adversely affect physiological function for short time periods relative to normal brain states, their cumulative impact can significantly decrease patient quality of life in myriad ways. For many, anti-epileptic drugs are effective first-line therapies. One third of all patients do not respond to chemical intervention, however, and require invasive resective surgery to remove epileptic tissue. While this is still the most effective last-line treatment, many patients with ‘refractory’ epilepsy still experience seizures afterward, while some are not even surgical candidates. Thus, a significant portion of patients lack further recourse to manage their seizures – which additionally impacts their quality of life. High-frequency oscillations (HFOs) are a recently discovered electrical biomarker with significant clinical potential in refractory human epilepsy. As a spatial biomarker, HFOs occur more frequently in epileptic tissue, and surgical removal of areas with high HFO rates can result in improved outcomes. There is also limited preliminary evidence that HFOs change prior to seizures, though it is currently unknown if HFOs function as temporal biomarkers of epilepsy and imminent seizure onset. No such temporal biomarker has ever been identified, though if it were to exist, it could be exploited in online seizure prediction algorithms. If these algorithms were clinically implemented in implantable neuromodulatory devices, improvements to quality of life for refractory epilepsy patients might be possible. Thus, the overall aim of this work is to investigate HFOs as potential temporal biomarkers of seizures and epilepsy, and further to determine whether their time-varying properties can be exploited in seizure prediction. In the first study we explore population-level evidence for the existence of this temporal effect in a large clinical cohort with refractory epilepsy. Using sophisticated automated HFO detection and big-data processing techniques, a continuous measure of HFO rates was developed to explore gradual changes in HFO rates prior to seizures, which were analyzed in aggregate to assess their stereotypical response. These methods resulted in the identification of a subset of patients in whom HFOs from epileptic tissue gradually increased before seizures. In the second study, we use machine learning techniques to investigate temporal changes in HFO rates within individuals, and to assess their potential usefulness in patient-specific seizure prediction. Here, we identified a subset of patients whose predictive models sufficiently differentiated the preictal (before seizure) state better than random chance. In the third study, we extend our prediction framework to include the signal properties of HFOs. We explore their ability to improve the identification of preictal periods, and additionally translate their predictive models into a proof-of-concept seizure warning system. For some patients, positive results from this demonstration show that seizure prediction using HFOs could be possible. These studies overall provide convincing evidence that HFOs can change in measurable ways prior to seizure start. While this effect was not significant in some individuals, for many it enabled seizures to be predicted above random chance. Due to data limitations in overall recording duration and number of seizures captured, these findings require further validation with much larger high-density intracranial EEG datasets. Still, they provide a preliminary framework for the eventual use of HFOs in patient-specific seizure prediction with the potential to improve the lives of those with refractory epilepsy.PHDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/168079/1/jaredmsc_1.pd

An intelligent healthcare system with peer-to-peer learning and data assessment

Modern e-healthcare systems are prevalent in many medical institutions to reduce physicians' workload and enhance diagnostic accuracy, which leverages affordable wearable devices and Machine-Learning (ML) techniques. The healthcare systems collect various vital biosignals (e.g., heart rate and blood pressure) from wearable devices of users (e.g., chronic patients living alone at home) and analyze these patients' data in real-time by different ML classifiers (e.g. Support Vector Machine (SVM) or Hidden Markov Model (HMM)). The automatic diagnosis effectively improves the physicians' performance in terms of diagnostic efficiency and accuracy. There are three challenges impacting these healthcare systems -- the increasing number of patients, new diseases and the changes of existing disease patterns, which are caused by population aging as well as the alteration of environment and lifestyle. This research is intended to explore a novel healthcare system with advanced ML solutions that can solve the challenges and exhibit high accuracy and efficiency

Application of Hierarchical Temporal Memory to Anomaly Detection of Vital Signs for Ambient Assisted Living

This thesis presents the development of a framework for anomaly detection of vital signs for an Ambient Assisted Living (AAL) health monitoring scenario. It is driven by spatiotemporal reasoning of vital signs that Cortical Learning Algorithms (CLA) based on Hierarchal Temporal Memory (HTM) theory undertakes in an AAL health monitoring scenario to detect anomalous data points preceding cardiac arrest. This thesis begins with a literature review on the existing Ambient intelligence (AmI) paradigm, AAL technologies and anomaly detection algorithms used in a health monitoring scenario. The research revealed the significance of the temporal and spatial reasoning in the vital signs monitoring as the spatiotemporal patterns of vital signs provide a basis to detect irregularities in the health status of elderly people. The HTM theory is yet to be adequately deployed in an AAL health monitoring scenario. Hence HTM theory, network and core operations of the CLA are explored. Despite the fact that standard implementation of the HTM theory comprises of a single-level hierarchy, multiple vital signs, specifically the correlation between them is not sufficiently considered. This insufficiency is of particular significance considering that vital signs are correlated in time and space, which are used in the health monitoring applications for diagnosis and prognosis tasks. This research proposes a novel framework consisting of multi-level HTM networks. The lower level consists of four models allocated to the four vital signs, Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), Heart Rate (HR) and peripheral capillary oxygen saturation (SpO2) in order to learn the spatiotemporal patterns of each vital sign. Additionally, a higher level is introduced to learn spatiotemporal patterns of the anomalous data point detected from the four vital signs. The proposed hierarchical organisation improves the model’s performance by using the semantically improved representation of the sensed data because patterns learned at each level of the hierarchy are reused when combined in novel ways at higher levels. To investigate and evaluate the performance of the proposed framework, several data selection techniques are studied, and accordingly, a total record of 247 elderly patients is extracted from the MIMIC-III clinical database. The performance of the proposed framework is evaluated and compared against several state-of-the-art anomaly detection algorithms using both online and traditional metrics. The proposed framework achieved 83% NAB score which outperforms the HTM and k-NN algorithms by 15%, the HBOS and INFLO SVD by 16% and the k-NN PCA by 21% while the SVM scored 34%. The results prove that multiple HTM networks can achieve better performance when dealing with multi-dimensional data, i.e. data collected from more than one source/sensor

Energy-efficient early emergency detection for healthcare monitoring on WBAN platform

This dissertation introduces an innovative structure aimed at improving anomaly detection and predictive analyses in Wireless Body Area Networks (WBANs), a crucial technology within the realm of digital healthcare. Motivated by the need to improve diagnostic precision and clinical decision-making, especially in environments constrained by the computational limitations of edge devices, this research aims to revolutionise patient monitoring systems. The research begins with a comprehensive review of current WBAN technologies and their applications in healthcare. It identifies a distinct gap in the ability of these systems to adapt to the dynamic and complex nature of patient health monitoring. Traditional WBAN methodologies, heavily reliant on static thresholds and centralised cloud-based processing, often fall short of effectively managing the nuanced and varied data derived from patient monitoring, leading to real-time responsiveness and energy efficiency challenges. The research progresses from static to dynamic threshold to address these challenges, enhancing the system's adaptability to fluctuating health indicators. The Multi-Level Classification Threshold Algorithm (MLCTA) was formulated to refine the classification of health-related data. The study subsequently presents a compound method that combines threshold-based techniques with linear regression analysis. This integration significantly bolsters the model's predictive capacity for health incidents by providing a more profound comprehension of vital sign patterns. When used in conjunction with actual patient data, this approach notably heightens the precision of health event forecasts. The framework includes a series of progressively advanced algorithms: The Modified Adaptive Local Emergency Detection (MALED) lays the groundwork with its adaptive response to health data changes. This is enhanced by the Differential Change Analysis (DCA), which introduces sensitivity to the rate of change in vital signs for early anomaly detection. The Local Emergency Detection Algorithm Using Adaptive Sampling (LEDAS) further optimises this framework by implementing adaptive sampling based on the patient's health status, ensuring efficient data collection. The pinnacle of this progression is the Sequential Multi-Dimensional Trend Analysis (SMDTA), which offers a comprehensive multi-dimensional analysis of health data, identifying intricate patterns and relationships among various vital signs for precise health predictions. Additionally, incorporating dynamic thresholds across these algorithms refines anomaly detection, making the system more flexible and responsive to changing patient health dynamics. Together, these algorithms represent a significant leap from basic monitoring systems to advanced networks capable of sophisticated multi-dimensional health analysis. Empirical evaluation using actual patient data from clinical databases demonstrated the superior efficacy of the proposed framework. Notably, the hybrid approach combining linear regression with threshold-based methods achieved near 96% accuracy in anomaly detection, significantly reducing the false-positive rate to 2%. Furthermore, the optimised local emergency detection strategies led to an average 85% reduction in data transmissions, contributing to a 19% decrease in energy consumption compared to existing methods, thereby underscoring the system's suitability for energy-constrained environments. The results of this research highlight not only the potential of advanced WBAN systems in enhancing healthcare delivery but also pave the way for future developments in medical technology. The proposed framework and its algorithms open new avenues in clinical decision-making, offering robust, efficient, and user-friendly solutions for healthcare professionals and patients