Download or read book Electrocardiogram Signal Based Sudden Cardiac Arrest Prediction Using Machine Learning Approaches written by L Murukesan Loganathan and published by . This book was released on 2014 with total page 195 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis focuses on predicting occurrence of imminent sudden cardiac arrest (SCA) using heart rate variability (HVR) and electrocardioram (ECG) signals. Sudden cardiac death (SCD) is a devastating cardiovascular disease that responsible for millions of deaths per year.

Download or read book Cardiac Arrest Prediction Using Machine Learning Model written by Dibakar Sinha and published by . This book was released on 2023-05-29 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Cardiac Arrest Prediction Using Machine Learning Model is a sophisticated system that leverages the power of machine learning algorithms to identify individuals who are at high risk of experiencing a cardiac arrest. This innovative solution aims to assist healthcare professionals in proactively identifying patients who may require immediate intervention or closer monitoring, thereby improving patient outcomes and potentially saving lives. The model is designed to analyze a variety of patient data, including medical history, vital signs, laboratory results, and other relevant clinical variables. By employing advanced machine learning techniques, the model learns patterns and relationships within the data to identify potential risk factors associated with cardiac arrest. The development of this model involves a two-step process. First, a comprehensive dataset is collected, consisting of anonymized patient information, including both historical data and real-time updates. This dataset is then used to train the machine learning model, which learns to recognize patterns and associations between different variables and the occurrence of cardiac arrest. Once the model is trained, it can be applied to new patient data in real-time. The system takes input from various sources, such as electronic health records, wearable devices, and continuous monitoring systems, to continuously assess a patient's risk of cardiac arrest. The model analyzes the incoming data and generates a prediction score or risk probability indicating the likelihood of a cardiac arrest event occurring within a specific timeframe. Healthcare professionals can utilize the prediction scores provided by the model to prioritize and allocate resources more efficiently. Patients identified as having a higher risk can receive immediate attention and proactive interventions to prevent cardiac arrest, such as medication adjustments, lifestyle modifications, or close monitoring in intensive care units. This targeted approach allows healthcare providers to intervene before the condition deteriorates, potentially improving patient outcomes and reducing mortality rates. The Cardiac Arrest Prediction Using Machine Learning Model is a promising advancement in healthcare technology, providing a proactive approach to cardiac care. By leveraging the power of machine learning algorithms and real-time patient data, it offers healthcare professionals valuable insights and tools to identify high-risk individuals, ultimately leading to improved patient care and better management of cardiac arrest risks. One needs both real-world experience and in-depth knowledge to make an accurate prediction of heart illness. Heart disease is now one of the most extremely dangerous and serious illnesses since it is difficult to diagnose. Thus, the ideal moment for both physicians and patients. Only when it can be correctly anticipated before a patient experiences a heart attack can cardiovascular illness be effectively diagnosed. This goal can be accomplished by combining a suitable machine learning approach with a significant volume of cardiovascular disease health information. In the modern digital era, data is an important resource, and a lot of data was being produced across many different businesses. The main origin of information in healthcare are data about the patients and information about illnesses. Tendencies in the sickness and provide individualised therapy for each patient by using healthcare information and ML techniques.

Download or read book Assessment and Prediction of Cardiovascular Status During Cardiac Arrest Through Machine Learning and Dynamical Time series Analysis written by Sharad Shandilya and published by . This book was released on 2013 with total page 89 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this work, new methods of feature extraction, feature selection, stochastic data characterization/modeling, variance reduction and measures for parametric discrimination are proposed. These methods have implications for data mining, machine learning, and information theory. A novel decision-support system is developed in order to guide intervention during cardiac arrest. The models are built upon knowledge extracted with signal-processing, non-linear dynamic and machine-learning methods. The proposed ECG characterization, combined with information extracted from PetCO2 signals, shows viability for decision-support in clinical settings. The approach, which focuses on integration of multiple features through machine learning techniques, suits well to inclusion of multiple physiologic signals. Ventricular Fibrillation (VF) is a common presenting dysrhythmia in the setting of cardiac arrest whose main treatment is defibrillation through direct current countershock to achieve return of spontaneous circulation. However, often defibrillation is unsuccessful and may even lead to the transition of VF to more nefarious rhythms such as asystole or pulseless electrical activity. Multiple methods have been proposed for predicting defibrillation success based on examination of the VF waveform. To date, however, no analytical technique has been widely accepted. For a given desired sensitivity, the proposed model provides a significantly higher accuracy and specificity as compared to the state-of-the-art. Notably, within the range of 80-90% of sensitivity, the method provides about 40% higher specificity. This means that when trained to have the same level of sensitivity, the model will yield far fewer false positives (unnecessary shocks). Also introduced is a new model that predicts recurrence of arrest after a successful countershock is delivered. To date, no other work has sought to build such a model. I validate the method by reporting multiple performance metrics calculated on (blind) test sets.

Download or read book Predicting Heart Failure written by Kishor Kumar Sadasivuni and published by John Wiley & Sons. This book was released on 2022-04-04 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: PREDICTING HEART FAILURE Predicting Heart Failure: Invasive, Non-Invasive, Machine Learning and Artificial Intelligence Based Methods focuses on the mechanics and symptoms of heart failure and various approaches, including conventional and modern techniques to diagnose it. This book also provides a comprehensive but concise guide to all modern cardiological practice, emphasizing practical clinical management in many different contexts. Predicting Heart Failure supplies readers with trustworthy insights into all aspects of heart failure, including essential background information on clinical practice guidelines, in-depth, peer-reviewed articles, and broad coverage of this fast-moving field. Readers will also find: Discussion of the main characteristics of cardiovascular biosensors, along with their open issues for development and application Summary of the difficulties of wireless sensor communication and power transfer, and the utility of artificial intelligence in cardiology Coverage of data mining classification techniques, applied machine learning and advanced methods for estimating HF severity and diagnosing and predicting heart failure Discussion of the risks and issues associated with the remote monitoring system Assessment of the potential applications and future of implantable and wearable devices in heart failure prediction and detection Artificial intelligence in mobile monitoring technologies to provide clinicians with improved treatment options, ultimately easing access to healthcare by all patient populations. Providing the latest research data for the diagnosis and treatment of heart failure, Predicting Heart Failure: Invasive, Non-Invasive, Machine Learning and Artificial Intelligence Based Methods is an excellent resource for nurses, nurse practitioners, physician assistants, medical students, and general practitioners to gain a better understanding of bedside cardiology.

Download or read book Signal Processing Driven Machine Learning Techniques for Cardiovascular Data Processing written by Rajesh Kumar Tripathy and published by Elsevier. This book was released on 2024-06-12 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: Signal Processing Driven Machine Learning Techniques for Cardiovascular Data Processing features recent advances in machine learning coupled with new signal processing-based methods for cardiovascular data analysis. Topics in this book include machine learning methods such as supervised learning, unsupervised learning, semi-supervised learning, and meta-learning combined with different signal processing techniques such as multivariate data analysis, time-frequency analysis, multiscale analysis, and feature extraction techniques for the detection of cardiovascular diseases, heart valve disorders, hypertension, and activity monitoring using ECG, PPG, and PCG signals.In addition, this book also includes the applications of digital signal processing (time-frequency analysis, multiscale decomposition, feature extraction, non-linear analysis, and transform domain methods), machine learning and deep learning (convolutional neural network (CNN), recurrent neural network (RNN), transformer and attention-based models, etc.) techniques for the analysis of cardiac signals. The interpretable machine learning and deep learning models combined with signal processing for cardiovascular data analysis are also covered. - Provides details regarding the application of various signal processing and machine learning-based methods for cardiovascular signal analysis - Covers methodologies as well as experimental results and studies - Helps readers understand the use of different cardiac signals such as ECG, PCG, and PPG for the automated detection of heart ailments and other related biomedical applications

Download or read book A Machine Learning Approach For Detecting Ventricular Fibrillation During Out Of Hospital Cardiac Arrest written by and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction: Survival from out-of-hospital cardiac arrest (OHCA) relies heavily on early identification and defibrillation of ventricular fibrillation (VF). Therefore, the aim of this study is to develop and test an automated method based on a novel machine learning technique to detect VF.Materials and methods: The dataset contained ECG segments from 169 OHCA patients treated by Tualatin Valley Fire & Rescue (Tigard, OR, USA) using the Philips HeartStart MRx monitor/defibrillator. The dataset was composed of 596 10-s ECG segments, 144 shockable and 452 non-shockable, annotated by consensus by a pool of four emergency medicine doctors. The dataset was split patient-wise into training (60%) and test (40%) sets. Each ECG segment was band-pass filtered (1-30 Hz), waveform features were calculated and fed to a state of the art machine learning algorithm, a support vector machine (SVM) classifier with radial basis function kernel. The SVM diagnosed each segment as shockable or non-shockable. The training set was used to select the most discriminative feature subset (incremental selection of maximum 5 features), and to tune the hyperparameters of the SVM through patient-wise 10-fold cross validation. The test set was used to compute the performance of the method in terms of sensitivity (SE) and specificity (SP). This procedure was repeated 500 times to estimate the distributions of SE and SP.Results: The SVM showed a mean (standard deviation) SE and SP of 96.5% (2.5) and 97.0% (1.4), respectively. The method met the minimum performance requirements of the American Heart Association (SE>90% and SP>95%). The method required on average only 279 (36) ms per segment in a standard platform.Conclusion: An automated method based on a state of the art machine learning technique accurately detects VF during OHCA. Its low computational cost makes it suitable for implementation into current defibrillators.

Download or read book Secondary Analysis of Electronic Health Records written by MIT Critical Data and published by Springer. This book was released on 2016-09-09 with total page 435 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book trains the next generation of scientists representing different disciplines to leverage the data generated during routine patient care. It formulates a more complete lexicon of evidence-based recommendations and support shared, ethical decision making by doctors with their patients. Diagnostic and therapeutic technologies continue to evolve rapidly, and both individual practitioners and clinical teams face increasingly complex ethical decisions. Unfortunately, the current state of medical knowledge does not provide the guidance to make the majority of clinical decisions on the basis of evidence. The present research infrastructure is inefficient and frequently produces unreliable results that cannot be replicated. Even randomized controlled trials (RCTs), the traditional gold standards of the research reliability hierarchy, are not without limitations. They can be costly, labor intensive, and slow, and can return results that are seldom generalizable to every patient population. Furthermore, many pertinent but unresolved clinical and medical systems issues do not seem to have attracted the interest of the research enterprise, which has come to focus instead on cellular and molecular investigations and single-agent (e.g., a drug or device) effects. For clinicians, the end result is a bit of a “data desert” when it comes to making decisions. The new research infrastructure proposed in this book will help the medical profession to make ethically sound and well informed decisions for their patients.

Download or read book Analysis of ECG Signals and Its Application in Prediction of Sudden Cardiac Death written by Varun Gopalan and published by . This book was released on 2009 with total page 58 pages. Available in PDF, EPUB and Kindle. Book excerpt: An ECG signal can provide a lot of information regarding the functioning of a heart by analyzing the different waves and the intervals present in it. The ability of a detection algorithm to retrieve these components is thus a key factor. This thesis aims at constructing a robust algorithm for accurate detection of the QRS complex. The Pan-Tompkins algorithm is used as the base and modified to suit our data. The changes include a searchback mechanism [Searchback (v1.1)] that we developed to overcome the problems due to high noise presence and a technique to make it computationally efficient. The Searchback (v1.1) mechanism's main objective is to reduce the threshold level for detection that was primarily increased by noise. The entire data is divided into large chunks of blocks which is further split into a number of smaller components called segments. We demonstrate that this method can achieve the same performance as the modified algorithm while significantly reducing the computational load. We document the strategies that work well with our data, and show orders of magnitude speedup using the modified Pan-Tompkins detector. The analyzed data is used to define biological markers that can predict sudden cardiac death. We briefly make an attempt to analyze one such marker, the QT-RR ratio. Further, we try to contrast this ratio obtained for swine that died due to cardiac arrest with the ones that survived. Going into depth of finding such markers will be our next step.

Download or read book Artificial Intelligence in Healthcare written by Adam Bohr and published by Academic Press. This book was released on 2020-06-21 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data

Download or read book Deep Learning Approach For A Shock Advise Algorithm Using Short Electrocardiogram Analysis Intervals written by Elisabete Aramendi and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Purpose: Deep learning is a subfield of machine learning techniques with the ability to automatically learn features from data. Typical deep architectures use convolutional neural networks (CNN). The purpose of this study was to design defibrillator shock advice algorithms based on CNNs using short analysis segments.Materials and methods: Electronic files were gathered between 2013 and 2015 from the defibrillators (Lifepak 1000/500, Stryker) used to treat out-of-hospital cardiac arrest patients by the basic life support ambulances of the Basque Country. Six clinicians reviewed the electrocardiogram (ECG) of the rhythm analysis intervals, their consensus shock/no-shock decisions were used as ground truth. The analysis intervals were divided into nonoverlapping ECG segments of 2, 3 and 4-seconds. Data were partitioned patient-wise into training (70%) to develop the algorithm, and test (30%) to report the results. A deep learning shock/no-shock algorithm was designed using 2/3 CNN layers and two dense layers. The process was repeated 100 times to statistically characterize the sensitivity (Se), specificity (Sp), and accuracy (Acc) of the shock advice algorithm.Results: 4216 analyses (852 patients) were reviewed. The consensus was shock for 498 (181) analyses and no-shock for 3718 (790) analyses, with inter-rater Fleissu02bc kappa score u03ba=0.93 (excluding systematic errors). The mean (90% confidence interval) Se, Sp and Acc of the deep learning algorithms were: 96.1 (93.3u201398.4), 99.1 (98.4u201399.5) and 98.5 (97.9u201399.0) for 4-s segments; 95.7 (92.7u2013 98.1), 99.0 (98.3u201399.5) and 98.4 (97.6u201399.0) for 3-s segments; and 93.9 (89.3u2013 97.3), 99.0 (98.4u201399.4) and 98.0 (97.3u201398.6) for 2-s segments. The minimum 90% Se and 95% Sp recommended by the American Heart Association (AHA) were met for all segment lengths.Conclusions: Deep learning shock advice algorithms were demonstrated to be compliant with AHA performance using ECG segment lengths as short as 2- seconds.

Download or read book The Minnesota Code Manual of Electrocardiographic Findings written by Ronald J. Prineas and published by . This book was released on 1982 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Machine Learning and Adaptive Signal Processing Methods for Electrocardiography Applications written by Calvin Apollos Perumalla and published by . This book was released on 2017 with total page 91 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation is directed towards improving the state of art cardiac monitoring methods and automatic diagnosis of cardiac anomalies through modern engineering approaches such as adaptive signal processing, and machine learning methods. The dissertation will describe the invention and associated methods of a cardiac rhythm monitor dubbed the Integrated Vectorcardiogram (iVCG). In addition, novel machine learning approaches are discussed to improve diagnoses and prediction accuracy of cardiac diseases. It is estimated that around 17 million people in the world die from cardiac related events each year. It has also been shown that many of such deaths can be averted with long-term continuous monitoring and actuation. Hence, there is a growing need for better cardiac monitoring solutions. Leveraging the improvements in computational power, communication bandwidth, energy efficiency and electronic chip size in recent years, the Integrated Vectorcardiogram (iVCG) was invented as an answer to this problem. The iVCG is a miniaturized, integrated version of the Vectorcardiogram that was invented in the 1930s. The Vectorcardiogram provides full diagnostic quality cardiac information equivalent to that of the gold standard, 12-lead ECG, which is restricted to in-office use due to its bulky, obtrusive form. With the iVCG, it is possible to provide continuous, long-term, full diagnostic quality information, while being portable and unobtrusive to the patient. Moreover, it is possible to leverage this Big Data and create machine learning algorithms to deliver better patient outcomes in the form of patient specific machine diagnosis and timely alerts. First, we present a proof-of-concept investigation for a miniaturized vectorcardiogram, the iVCG system for ambulatory on-body applications that continuously monitors the electrical activity of the heart in three dimensions. We investigate the minimum distance between a pair of leads in the X, Y and Z axes vii such that the signals are distinguishable from the noise. The target dimensions for our prototype iVCG are 3x3x2 cm and based on our experimental results we show that it is possible to achieve these dimensions. Following this, we present a solution to the problem of transforming the three VCG component signals to the familiar 12-lead ECG for the convenience of cardiologists. The least squares (LS) method is employed on the VCG signals and the reference (training) 12-lead ECG to obtain a 12x3 transformation matrix to generate the real-time ECG signals from the VCG signals. The iVCG is portable and worn on the chest of the patient and although a physician or trained technician will initially install it in the appropriate position, it is prone to subsequent rotation and displacement errors introduced by the patient placement of the device. We characterize these errors and present a software solution to correct the effect of the errors on the iVCG signals. We also describe the design of machine learning methods to improve automatic diagnosis and prediction of various heart conditions. Methods very similar to the ones described in this dissertation can be used on the long term, full diagnostic quality Big Data such that the iVCG will be able to provide further insights into the health of patients. The iVCG system is potentially breakthrough and disruptive technology allowing long term and continuous remote monitoring of patients electrical heart activity. The implications are profound and include 1) providing a less expensive device compared to the 12-lead ECG system (the "gold standard"); 2) providing continuous, remote tele-monitoring of patients; 3) the replacement of current Holter shortterm monitoring system; 4) Improved and economic ICU cardiac monitoring; 5) The ability for patients to be sent home earlier from a hospital since physicians will have continuous remote monitoring of the patients.

Download or read book Electrocardiogram Signal Classification and Machine Learning written by Sara Moein and published by Medical Information Science Reference. This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Technological tools and computational techniques have enhanced the healthcare industry. These advancements have led to significant progress in the diagnosis of heart disorders. Electrocardiogram Signal Classification and Machine Learning: Emerging Research and Opportunities is a critical scholarly resource that examines the importance of automatic normalization and classification of electrocardiogram (ECG) signals of heart disorders. Featuring a wide range of topics such as common heart disorders, particle swarm optimization, and benchmarks functions, this publication is geared toward medical professionals, researchers, professionals, and students seeking current and relevant research on the categorization of ECG signals.

Download or read book Proceedings of 2nd International Conference on Communication Computing and Networking written by C. Rama Krishna and published by Springer. This book was released on 2018-09-07 with total page 1039 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book provides insights from the 2nd International Conference on Communication, Computing and Networking organized by the Department of Computer Science and Engineering, National Institute of Technical Teachers Training and Research, Chandigarh, India on March 29–30, 2018. The book includes contributions in which researchers, engineers, and academicians as well as industrial professionals from around the globe presented their research findings and development activities in the field of Computing Technologies, Wireless Networks, Information Security, Image Processing and Data Science. The book provides opportunities for the readers to explore the literature, identify gaps in the existing works and propose new ideas for research.

Download or read book Biomedical Signals Based Computer Aided Diagnosis for Neurological Disorders written by M. Murugappan and published by Springer Nature. This book was released on 2022-06-17 with total page 295 pages. Available in PDF, EPUB and Kindle. Book excerpt: Biomedical signals provide unprecedented insight into abnormal or anomalous neurological conditions. The computer-aided diagnosis (CAD) system plays a key role in detecting neurological abnormalities and improving diagnosis and treatment consistency in medicine. This book covers different aspects of biomedical signals-based systems used in the automatic detection/identification of neurological disorders. Several biomedical signals are introduced and analyzed, including electroencephalogram (EEG), electrocardiogram (ECG), heart rate (HR), magnetoencephalogram (MEG), and electromyogram (EMG). It explains the role of the CAD system in processing biomedical signals and the application to neurological disorder diagnosis. The book provides the basics of biomedical signal processing, optimization methods, and machine learning/deep learning techniques used in designing CAD systems for neurological disorders.

Download or read book Soft Computing and Industry written by Rajkumar Roy and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 862 pages. Available in PDF, EPUB and Kindle. Book excerpt: Soft computing embraces various methodologies for the development of intelligent systems that have been successfully applied to a large number of real-world problems. Soft Computing in Industry contains a collection of papers that were presented at the 6th On-line World Conference on Soft Computing in Industrial Applications that was held in September 2001. It provides a comprehensive overview of recent theoretical developments in soft computing as well as of successful industrial applications. It is divided into seven parts covering material on: keynote papers on various subjects ranging from computing with autopoietic systems to the effects of the Internet on education; intelligent control; classification, clustering and optimization; image and signal processing; agents, multimedia and Internet; theoretical advances; prediction, design and diagnosis. The book is aimed at researchers and professional engineers who develop and apply intelligent systems in computer engineering.

Download or read book Feature Selection and Ensemble Methods for Bioinformatics written by Oleg Okun and published by IGI Global. This book was released on 2011 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book offers a unique perspective on machine learning aspects of microarray gene expression based cancer classification, combining computer science, and biology"--Provided by publisher.