This document is from a 2007 MIT course on biomedical signal and image processing. It provides an introduction to clinical electrocardiography (ECG) through several sections: an overview of ECG and what it measures; a discussion of the electrophysiology of individual heart cells; how electrical currents propagate through heart tissue; specific structures of the heart that the currents pass through; and how this results in a measurable signal on the body surface. The document includes diagrams of heart anatomy and electrical activity, as well as illustrations of cellular electrophysiology.
This document is an introduction to a course on biomedical signal and image processing. It provides an overview of electrocardiography and the basics of reading electrocardiograms. It discusses how the electrical activity of the heart is generated at the cellular level and propagates through the heart tissue. It also describes the key structures of the heart that are involved in conducting electrical signals and their relationship to the patterns seen on ECG tracings. The introduction aims to provide students with background knowledge on cardiac electrophysiology needed to understand biomedical signals from the heart.
This document provides biographical information about Dalin Tang, including his education, employment history, research interests, honors and awards, publications, and patents. It details that he received his PhD in Applied Mathematics from the University of Wisconsin-Madison in 1988. He is currently a professor of Applied Mathematics and Biomedical Engineering at Worcester Polytechnic Institute, where he has worked since 1994. His research focuses on computational bioengineering, cardiovascular modeling, and biomedical applications of mathematics. He has over 50 publications in refereed journals and conferences and holds two patents related to cardiovascular modeling and analysis.
The Singularity: Toward a Post-Human RealityLarry Smarr
06.02.13
Talk to UCSD's Sixth College
Honor's Course on Kurzweil's The Singularity is Near
Title: The Singularity: Toward a Post-Human Reality
La Jolla, CA
Simulation Based Engineering Science ReportVlastimil Dejl
This document is the final report of the National Science Foundation's Blue Ribbon Panel on Simulation-Based Engineering Science (SBES). The report finds that SBES has become an indispensable tool for solving scientific and technological problems and recommends ways to advance SBES through academia, industry, national laboratories, and government agencies. Key challenges discussed include multiscale modeling, verification and validation, big data and visualization techniques, and education initiatives to prepare engineers and scientists for the knowledge explosion enabled by SBES. The report highlights promising applications of SBES in medicine, homeland security, energy/environment, materials, and industrial/defense fields.
The development of transcranial magnetic stimulationbijnnjournal
This review describes the development of transcranial magnetic stimulation in 1985 and the research related to
this technique over the following 10 years. It not only focuses on work done at the National Institutes of Health
but provides a survey of other related research as well. Key topics are the calculation of the electric field produced
during magnetic stimulation, the interaction of this electric field with a long nerve axon, coil design, the time course
of the magnetic stimulation pulse, and the safety of magnetic stimulation.
Ashley Raba has over 10 years of experience in biomedical research and project management for the DoD. She holds a Ph.D. in Biomedical Engineering and has published several papers on mathematical modeling of bioelectric phenomena. Currently she works as a research analyst evaluating new technologies and managing projects for a government customer. Her skills include mathematical modeling, high performance computing, data analysis, and technical writing.
This document is a resume for Wei Wang, who received his Ph.D. in Physics from the University of Houston in 2015. His research focused on fabricating and designing sub-wavelength periodic textures to improve light harvesting in multi-junction solar cells. He has over 8 years of experience in modeling, fabricating, and characterizing photovoltaic materials and devices. He has published 8 first-author papers and presented at over 20 international conferences. His skills include growth and processing of solar cell materials as well as optical and electrical characterization techniques.
PhD positions in Computational Cardiac Image Processing and ModellingAurelio Ruiz Garcia
Open positions in Computational Cardiac Image Processing and Modelling. Deadline Sept 1st 2014.
These positions are for doing research towards obtaining a PhD degree at the Department of Information & Communication Technologies (DTIC) of the Universitat Pompeu Fabra (UPF), Barcelona, Spain.
The projects will be conducted in the PhySense research group (http://physense.upf.edu), supervised by ICREA Research Prof. B. Bijnens and Dr. Oscar Camara. PhySense was recently established (2011) and focuses on integrating engineering/physics knowledge with physiology in order to provide an interdisciplinary research environment, working closely together with internationally known academic and clinical centres. This is approached by integrating and improving information acquisition, handling and processing techniques, combined with basic knowledge on pathophysiology, in order to advance clinical sciences.
This document is an introduction to a course on biomedical signal and image processing. It provides an overview of electrocardiography and the basics of reading electrocardiograms. It discusses how the electrical activity of the heart is generated at the cellular level and propagates through the heart tissue. It also describes the key structures of the heart that are involved in conducting electrical signals and their relationship to the patterns seen on ECG tracings. The introduction aims to provide students with background knowledge on cardiac electrophysiology needed to understand biomedical signals from the heart.
This document provides biographical information about Dalin Tang, including his education, employment history, research interests, honors and awards, publications, and patents. It details that he received his PhD in Applied Mathematics from the University of Wisconsin-Madison in 1988. He is currently a professor of Applied Mathematics and Biomedical Engineering at Worcester Polytechnic Institute, where he has worked since 1994. His research focuses on computational bioengineering, cardiovascular modeling, and biomedical applications of mathematics. He has over 50 publications in refereed journals and conferences and holds two patents related to cardiovascular modeling and analysis.
The Singularity: Toward a Post-Human RealityLarry Smarr
06.02.13
Talk to UCSD's Sixth College
Honor's Course on Kurzweil's The Singularity is Near
Title: The Singularity: Toward a Post-Human Reality
La Jolla, CA
Simulation Based Engineering Science ReportVlastimil Dejl
This document is the final report of the National Science Foundation's Blue Ribbon Panel on Simulation-Based Engineering Science (SBES). The report finds that SBES has become an indispensable tool for solving scientific and technological problems and recommends ways to advance SBES through academia, industry, national laboratories, and government agencies. Key challenges discussed include multiscale modeling, verification and validation, big data and visualization techniques, and education initiatives to prepare engineers and scientists for the knowledge explosion enabled by SBES. The report highlights promising applications of SBES in medicine, homeland security, energy/environment, materials, and industrial/defense fields.
The development of transcranial magnetic stimulationbijnnjournal
This review describes the development of transcranial magnetic stimulation in 1985 and the research related to
this technique over the following 10 years. It not only focuses on work done at the National Institutes of Health
but provides a survey of other related research as well. Key topics are the calculation of the electric field produced
during magnetic stimulation, the interaction of this electric field with a long nerve axon, coil design, the time course
of the magnetic stimulation pulse, and the safety of magnetic stimulation.
Ashley Raba has over 10 years of experience in biomedical research and project management for the DoD. She holds a Ph.D. in Biomedical Engineering and has published several papers on mathematical modeling of bioelectric phenomena. Currently she works as a research analyst evaluating new technologies and managing projects for a government customer. Her skills include mathematical modeling, high performance computing, data analysis, and technical writing.
This document is a resume for Wei Wang, who received his Ph.D. in Physics from the University of Houston in 2015. His research focused on fabricating and designing sub-wavelength periodic textures to improve light harvesting in multi-junction solar cells. He has over 8 years of experience in modeling, fabricating, and characterizing photovoltaic materials and devices. He has published 8 first-author papers and presented at over 20 international conferences. His skills include growth and processing of solar cell materials as well as optical and electrical characterization techniques.
PhD positions in Computational Cardiac Image Processing and ModellingAurelio Ruiz Garcia
Open positions in Computational Cardiac Image Processing and Modelling. Deadline Sept 1st 2014.
These positions are for doing research towards obtaining a PhD degree at the Department of Information & Communication Technologies (DTIC) of the Universitat Pompeu Fabra (UPF), Barcelona, Spain.
The projects will be conducted in the PhySense research group (http://physense.upf.edu), supervised by ICREA Research Prof. B. Bijnens and Dr. Oscar Camara. PhySense was recently established (2011) and focuses on integrating engineering/physics knowledge with physiology in order to provide an interdisciplinary research environment, working closely together with internationally known academic and clinical centres. This is approached by integrating and improving information acquisition, handling and processing techniques, combined with basic knowledge on pathophysiology, in order to advance clinical sciences.
A Toolkit For Forward Inverse Problems In Electrocardiography Within The SCIR...Sandra Long
This document describes a toolkit within the SCIRun problem solving environment that provides tools for constructing and manipulating electrocardiography forward and inverse models. The toolkit contains sample networks, tutorials, and documentation to guide users through assembly and execution of forward and inverse problems. It includes tools for potential-based and activation-based forward models using finite element and boundary element methods. The toolkit is demonstrated through a case study of a potential-based finite element forward model for defibrillation and an activation-based boundary element inverse model to estimate cardiac activation times from body surface potentials. The goal of the toolkit is to make relevant modeling tools accessible to researchers while facilitating integration with other software packages.
Dmitry Georgievich Luchinsky is a senior research scientist with over 30 years of experience in engineering and research. He has led over 20 research projects in various fields including optics, rocket motors, cryogenic flows, and more. He has published over 150 research articles. Currently he works as a senior research scientist applying physics modeling to problems in industry.
This document discusses the threat of a growing U.S. innovation deficit due to declining public investment in basic research. It provides case studies of underfunded areas of science that could yield major benefits, including advances in health, energy, high-tech industries, and national security. These include research related to Alzheimer's disease, cybersecurity, space exploration, plant sciences, quantum information technologies, policy analysis, catalysis, fusion energy, infectious diseases, defense technologies, photonics, synthetic biology, materials discovery, robotics, and batteries. Increased investment in these fields could lead to new treatments, more efficient energy and manufacturing, economic growth, and strategic advantages over competitors like China.
Cardiac Pacemakers: Function,
Troubleshooting, and Management
Part 1 of a 2-Part Series
Siva K. Mulpuru, MD, Malini Madhavan, MBBS, Christopher J. McLeod, MBCHB, PHD, Yong-Mei Cha, MD,
Paul A. Friedman, MD
Pacific Research Platform Application DriversLarry Smarr
The document summarizes several science driver teams that use the Pacific Research Platform (PRP) for high-speed data transfers between California universities. It discusses projects in biomedical research, earth sciences, particle physics, astronomy, and other fields. Specific examples highlighted include using the PRP to share cancer genomics data between multiple institutions, connect a supercomputer to telescope data, enable virtual reality transfers between universities, and link laboratories studying earthquakes. The PRP is also being expanded to support additional uses like cryo-electron microscopy, cultural heritage databases, and networking in southern California.
Dr. A. T. Patrascu has extensive experience in theoretical physics, applied physics, and mathematics gained from education and research positions at several international institutions. He has published work in highly regarded journals on topics including quantum field theory, string theory, molecular spectroscopy, and astrophysics. He is the sole author of several published papers and author of a book awarded a Springer prize for excellent doctoral theses.
Dr. A. T. Patrascu has extensive experience in theoretical physics, applied physics, and mathematics gained from education and research positions at several international institutions. He has published work in highly regarded journals on topics including quantum field theory, string theory, molecular spectroscopy, and astrophysics. He is the sole author of several published papers and author of a book awarded a Springer-Nature prize for excellent doctoral theses.
Interactive Visualization Systems and Data Integration Methods for Supporting...Don Pellegrino
This thesis explored developing new interactive visualization systems and data integration methods to support discovery in collections of scientific information. It addressed challenges of existing methods to support overviews and exploration as the volume of data increases. The work involved instantiating graph structures from real-world datasets, developing interactive visualizations, and using quantitative and semantic guidance to explore connections. It evaluated the methods on datasets from VAST challenges, open notebook science, and Pfizer drug discovery to demonstrate feasibility and identify future work opportunities at larger scales with these approaches.
Chenglin Zhang is a research scientist at Rice University with over 12 years of experience in synthesizing magnetic and superconducting materials. He has grown many new single crystal materials and published over 70 peer-reviewed papers. His expertise includes material fabrication, neutron and X-ray scattering, transport characterization, and data analysis. He received his Ph.D from Rutgers University and has held positions at the University of Tennessee and Rice University, where he managed research groups.
Jennifer K. W. Chesnutt has extensive experience in academia including as an adjunct professor and postdoctoral researcher. She holds a Ph.D. in Mechanical Engineering and has designed computational models of blood flow and clotting. She has also taught courses in mathematics and supervised undergraduate research. Her work has resulted in numerous publications in peer-reviewed journals and presentations at conferences.
High throughput mining of the scholarly literature; talk at NIHpetermurrayrust
Elsevier stopped Chris Hartgerink, a statistician, from downloading research papers in bulk from Sciencedirect for the purpose of content mining to detect potentially problematic research findings, despite having legal access through his university's subscription and only intending to extract facts without redistributing full papers; he had downloaded around 30GB of data over 10 days to mine psychology literature for test results, figures, tables and other information reported in papers. Hartgerink's research aims to investigate unreliable findings that can harm policy and research progress through an innovative content mining method.
Sang Hoon Shin is a Ph.D. student at Purdue University studying reliability physics of transistors at the scaling limit under Professor Muhammad A. Alam. He received his B.S. from Hanyang University in South Korea and M.S. from the University of Tokyo. His research focuses on self-heating effects, high power device packaging, and two dimensional material transistor reliability. He has published over 20 papers in journals and conferences and received several awards for his work.
This thesis examines the use of electrical bioimpedance for cerebral monitoring by investigating the biophysical basis and effects of hypoxic/ischemic brain damage on tissue impedance, developing instrumentation for impedance measurements, and analyzing sensitivity maps to determine the clinical feasibility of the method.
This document discusses the development of a stretchable micro-electrode array (SMEA) platform for electrophysiological measurements from cardiomyocytes under mechanical loading. The SMEA platform aims to mimic the cyclic stretching and contraction of heart muscle tissue in vivo while performing electrophysiology tests, in order to develop a more accurate in vitro model for cardiotoxicity screening. The document outlines the motivation for such a model, including limitations of current in vitro models. It then provides an overview of the SMEA platform, which uses pneumatically actuated PDMS membranes patterned with electrodes to apply mechanical strain to cultured cardiomyocytes. The focus of the thesis is to develop a manufacturable process for realizing this novel heart-on-
Modeling of generation and propagation of cardiac action potential using frac...IOSR Journals
This document presents a model for simulating cardiac action potentials using fractional differential equations. The model builds off the existing Hund-Rudy dynamic model by replacing ordinary differential equations with fractional differential equations to more accurately model ion exchange mechanisms. The fractional model is shown to capture cardiac action potential generation and propagation with higher accuracy than ordinary differential equation models. An electrical circuit representation of the fractional model is also proposed to abstract the transmembrane ion exchange process.
Dr. Babak Amir Parviz is an expert in nanotechnology and self-assembly manufacturing. He has received several awards for his research developing self-assembly techniques to build nano-scale devices. Self-assembly is an alternative to conventional manufacturing where parts assemble themselves, similar to how nature builds things. Dr. Amir Parviz believes self-assembly could revolutionize manufacturing by allowing the construction of much smaller parts and complex systems. He is currently researching applications of self-assembly at the intersection of biology and engineering like tissue regeneration and disease diagnostics.
This document provides a summary of Amanda J. Neukirch's education and professional experience. She received a Ph.D. in Physics from the University of Rochester, where she studied excited state dynamics in nanoscale systems. She is currently a postdoctoral associate at Los Alamos National Laboratory, where she uses computational methods to analyze electronic and optical properties of perovskite materials. Her research experience also includes positions at Lawrence Livermore National Laboratory and the University of Rochester, where she conducted computational and experimental work on topics related to electron and molecular dynamics.
Brian grew up loving astronomy and earned his PhD in particle physics. He is now a researcher with the ATLAS experiment at CERN, a physics professor at the University of Manchester, and a science communicator on BBC programs. He believes exploration of the universe through experiments like those at CERN is vital. Kathy McCormick also earned her PhD in physics and now works as a subject matter expert for the US Customs and Border Protection, where she defines regulations and tests new security equipment, drawing on her experience with radiation detection equipment during her thesis research. Sam Wurzel earned his master's in physics and co-founded Octopart, an online parts search engine, after becoming frustrated searching paper catalogs as a graduate student and being
Transcranial Photobiomodulation in the Treatment of Psychiatric Disorders: Ca...Paolo Cassano, MD, PhD
From Machine to Mind: Can We Close the Loop? At the 2019 Mind-Body Interface International Symposium (Taiwan): Dr Paolo Cassano suggests that machines shedding near-infrared light onto the head could communicate with the mind.
A Toolkit For Forward Inverse Problems In Electrocardiography Within The SCIR...Sandra Long
This document describes a toolkit within the SCIRun problem solving environment that provides tools for constructing and manipulating electrocardiography forward and inverse models. The toolkit contains sample networks, tutorials, and documentation to guide users through assembly and execution of forward and inverse problems. It includes tools for potential-based and activation-based forward models using finite element and boundary element methods. The toolkit is demonstrated through a case study of a potential-based finite element forward model for defibrillation and an activation-based boundary element inverse model to estimate cardiac activation times from body surface potentials. The goal of the toolkit is to make relevant modeling tools accessible to researchers while facilitating integration with other software packages.
Dmitry Georgievich Luchinsky is a senior research scientist with over 30 years of experience in engineering and research. He has led over 20 research projects in various fields including optics, rocket motors, cryogenic flows, and more. He has published over 150 research articles. Currently he works as a senior research scientist applying physics modeling to problems in industry.
This document discusses the threat of a growing U.S. innovation deficit due to declining public investment in basic research. It provides case studies of underfunded areas of science that could yield major benefits, including advances in health, energy, high-tech industries, and national security. These include research related to Alzheimer's disease, cybersecurity, space exploration, plant sciences, quantum information technologies, policy analysis, catalysis, fusion energy, infectious diseases, defense technologies, photonics, synthetic biology, materials discovery, robotics, and batteries. Increased investment in these fields could lead to new treatments, more efficient energy and manufacturing, economic growth, and strategic advantages over competitors like China.
Cardiac Pacemakers: Function,
Troubleshooting, and Management
Part 1 of a 2-Part Series
Siva K. Mulpuru, MD, Malini Madhavan, MBBS, Christopher J. McLeod, MBCHB, PHD, Yong-Mei Cha, MD,
Paul A. Friedman, MD
Pacific Research Platform Application DriversLarry Smarr
The document summarizes several science driver teams that use the Pacific Research Platform (PRP) for high-speed data transfers between California universities. It discusses projects in biomedical research, earth sciences, particle physics, astronomy, and other fields. Specific examples highlighted include using the PRP to share cancer genomics data between multiple institutions, connect a supercomputer to telescope data, enable virtual reality transfers between universities, and link laboratories studying earthquakes. The PRP is also being expanded to support additional uses like cryo-electron microscopy, cultural heritage databases, and networking in southern California.
Dr. A. T. Patrascu has extensive experience in theoretical physics, applied physics, and mathematics gained from education and research positions at several international institutions. He has published work in highly regarded journals on topics including quantum field theory, string theory, molecular spectroscopy, and astrophysics. He is the sole author of several published papers and author of a book awarded a Springer prize for excellent doctoral theses.
Dr. A. T. Patrascu has extensive experience in theoretical physics, applied physics, and mathematics gained from education and research positions at several international institutions. He has published work in highly regarded journals on topics including quantum field theory, string theory, molecular spectroscopy, and astrophysics. He is the sole author of several published papers and author of a book awarded a Springer-Nature prize for excellent doctoral theses.
Interactive Visualization Systems and Data Integration Methods for Supporting...Don Pellegrino
This thesis explored developing new interactive visualization systems and data integration methods to support discovery in collections of scientific information. It addressed challenges of existing methods to support overviews and exploration as the volume of data increases. The work involved instantiating graph structures from real-world datasets, developing interactive visualizations, and using quantitative and semantic guidance to explore connections. It evaluated the methods on datasets from VAST challenges, open notebook science, and Pfizer drug discovery to demonstrate feasibility and identify future work opportunities at larger scales with these approaches.
Chenglin Zhang is a research scientist at Rice University with over 12 years of experience in synthesizing magnetic and superconducting materials. He has grown many new single crystal materials and published over 70 peer-reviewed papers. His expertise includes material fabrication, neutron and X-ray scattering, transport characterization, and data analysis. He received his Ph.D from Rutgers University and has held positions at the University of Tennessee and Rice University, where he managed research groups.
Jennifer K. W. Chesnutt has extensive experience in academia including as an adjunct professor and postdoctoral researcher. She holds a Ph.D. in Mechanical Engineering and has designed computational models of blood flow and clotting. She has also taught courses in mathematics and supervised undergraduate research. Her work has resulted in numerous publications in peer-reviewed journals and presentations at conferences.
High throughput mining of the scholarly literature; talk at NIHpetermurrayrust
Elsevier stopped Chris Hartgerink, a statistician, from downloading research papers in bulk from Sciencedirect for the purpose of content mining to detect potentially problematic research findings, despite having legal access through his university's subscription and only intending to extract facts without redistributing full papers; he had downloaded around 30GB of data over 10 days to mine psychology literature for test results, figures, tables and other information reported in papers. Hartgerink's research aims to investigate unreliable findings that can harm policy and research progress through an innovative content mining method.
Sang Hoon Shin is a Ph.D. student at Purdue University studying reliability physics of transistors at the scaling limit under Professor Muhammad A. Alam. He received his B.S. from Hanyang University in South Korea and M.S. from the University of Tokyo. His research focuses on self-heating effects, high power device packaging, and two dimensional material transistor reliability. He has published over 20 papers in journals and conferences and received several awards for his work.
This thesis examines the use of electrical bioimpedance for cerebral monitoring by investigating the biophysical basis and effects of hypoxic/ischemic brain damage on tissue impedance, developing instrumentation for impedance measurements, and analyzing sensitivity maps to determine the clinical feasibility of the method.
This document discusses the development of a stretchable micro-electrode array (SMEA) platform for electrophysiological measurements from cardiomyocytes under mechanical loading. The SMEA platform aims to mimic the cyclic stretching and contraction of heart muscle tissue in vivo while performing electrophysiology tests, in order to develop a more accurate in vitro model for cardiotoxicity screening. The document outlines the motivation for such a model, including limitations of current in vitro models. It then provides an overview of the SMEA platform, which uses pneumatically actuated PDMS membranes patterned with electrodes to apply mechanical strain to cultured cardiomyocytes. The focus of the thesis is to develop a manufacturable process for realizing this novel heart-on-
Modeling of generation and propagation of cardiac action potential using frac...IOSR Journals
This document presents a model for simulating cardiac action potentials using fractional differential equations. The model builds off the existing Hund-Rudy dynamic model by replacing ordinary differential equations with fractional differential equations to more accurately model ion exchange mechanisms. The fractional model is shown to capture cardiac action potential generation and propagation with higher accuracy than ordinary differential equation models. An electrical circuit representation of the fractional model is also proposed to abstract the transmembrane ion exchange process.
Dr. Babak Amir Parviz is an expert in nanotechnology and self-assembly manufacturing. He has received several awards for his research developing self-assembly techniques to build nano-scale devices. Self-assembly is an alternative to conventional manufacturing where parts assemble themselves, similar to how nature builds things. Dr. Amir Parviz believes self-assembly could revolutionize manufacturing by allowing the construction of much smaller parts and complex systems. He is currently researching applications of self-assembly at the intersection of biology and engineering like tissue regeneration and disease diagnostics.
This document provides a summary of Amanda J. Neukirch's education and professional experience. She received a Ph.D. in Physics from the University of Rochester, where she studied excited state dynamics in nanoscale systems. She is currently a postdoctoral associate at Los Alamos National Laboratory, where she uses computational methods to analyze electronic and optical properties of perovskite materials. Her research experience also includes positions at Lawrence Livermore National Laboratory and the University of Rochester, where she conducted computational and experimental work on topics related to electron and molecular dynamics.
Brian grew up loving astronomy and earned his PhD in particle physics. He is now a researcher with the ATLAS experiment at CERN, a physics professor at the University of Manchester, and a science communicator on BBC programs. He believes exploration of the universe through experiments like those at CERN is vital. Kathy McCormick also earned her PhD in physics and now works as a subject matter expert for the US Customs and Border Protection, where she defines regulations and tests new security equipment, drawing on her experience with radiation detection equipment during her thesis research. Sam Wurzel earned his master's in physics and co-founded Octopart, an online parts search engine, after becoming frustrated searching paper catalogs as a graduate student and being
Transcranial Photobiomodulation in the Treatment of Psychiatric Disorders: Ca...Paolo Cassano, MD, PhD
From Machine to Mind: Can We Close the Loop? At the 2019 Mind-Body Interface International Symposium (Taiwan): Dr Paolo Cassano suggests that machines shedding near-infrared light onto the head could communicate with the mind.
artificial intelligence and data science contents.pptxGauravCar
What is artificial intelligence? Artificial intelligence is the ability of a computer or computer-controlled robot to perform tasks that are commonly associated with the intellectual processes characteristic of humans, such as the ability to reason.
› ...
Artificial intelligence (AI) | Definitio
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Applications of artificial Intelligence in Mechanical Engineering.pdfAtif Razi
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
Design and optimization of ion propulsion dronebjmsejournal
Electric propulsion technology is widely used in many kinds of vehicles in recent years, and aircrafts are no exception. Technically, UAVs are electrically propelled but tend to produce a significant amount of noise and vibrations. Ion propulsion technology for drones is a potential solution to this problem. Ion propulsion technology is proven to be feasible in the earth’s atmosphere. The study presented in this article shows the design of EHD thrusters and power supply for ion propulsion drones along with performance optimization of high-voltage power supply for endurance in earth’s atmosphere.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Data Control Language.pptx Data Control Language.pptx
BSP_ECG_slides.pdf
1. MIT OpenCourseWare
http://ocw.mit.edu
HST.582J / 6.555J / 16.456J Biomedical Signal and Image Processing
Spring 2007
For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms.
2. Harvard-MIT Division of Health Sciences and Technology
HST.582J: Biomedical Signal and Image Processing, Spring 2007
Course Director: Dr. Julie Greenberg
Introduction to Clinical
Electrocardiography
Andrew Reisner, MD
MGH Dept. of Emergency Medicine
Visiting Scientist, HST
Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
3. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
Electrocardiography
{ The heart is an electrical organ, and
its activity can be measured non-
invasively
{ Wealth of information related to:
z The electrical patterns proper
z The geometry of the heart tissue
z The metabolic state of the heart
{ Standard tool used in a wide-range
of medical evaluations
4. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology . Downloaded on [DD Month YYYY].
A heart
• Blood circulates, passing near
every cell in the body, driven by this
pump
• …actually, two pumps…
• Atria = turbochargers
• Myocardium = muscle
• Mechanical systole
• Electrical systole
Courtesy of Dr. Roger Mark. HST.542J Quantitative Physiology: Organ
Transport Systems, Spring 2004. (Massachusetts Institute of Technology:
MIT OpenCourseWare). http://ocw.mit.edu (accessed June 17, 2008).
Figure adapted from Phillips RE, Feeney MK, 1980 The Cardiac Rhythms.
Saunders, Philadelphia and from Hoffman BF, Cranefield PF 1960 Electrophysiology
of the Heart. McGraw Hill, New York.
5. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
To understand the ECG:
{ Electrophysiology of a single cell
{ How a wave of electrical current
propagates through myocardium
{ Specific structures of the heart
through which the electrical wave
travels
{ How that leads to a measurable
signal on the surface of the body
6. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
Part I: A little electrophysiology
7. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
Once upon a time, there was a cell:
ATPase
ATPase
8. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
time
time
Intracellu
lar
Intracellu
lar
millivoltag
e
millivoltag
e
-
-90
90
Resting comfortably
a myocyte
9. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
time
time
Intracellu
lar
Intracellu
lar
millivoltag
e
millivoltag
e
Depolarizing trigger
10. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
time
time
Intracellu
lar
Intracellu
lar
millivoltag
e
millivoltag
e
Na
channels
open,
briefly
11. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
time
time
Intracellu
lar
Intracellu
lar
millivoltag
e
millivoltag
e
In: Na+
Mystery
current
12. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
time
time
Intracellu
lar
Intracellu
lar
millivoltag
e
millivoltag
e
In: Na+
Ca++ is in balance
with K+ out
13. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
time
time
Intracellu
lar
Intracellu
lar
millivoltag
e
millivoltag
e
In: Na+
Excitation/Contraction Coupling:
Ca++ causes the Troponin Complex
(C, I & T) to release inhibition
of Actin & Myosin
14. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
time
time
Intracellu
lar
Intracellu
lar
millivoltag
e
millivoltag
e
In: Na+
Ca++ in; K+ out
More K+ out;
Ca++ flow halts
15. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
time
time
Intracellu
lar
Intracellu
lar
millivoltag
e
millivoltag
e
In: Na+
In: Ca++; Out: K+
Out: K+
Sodium channels reset
16. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
time
time
Intracellu
lar
Intracellu
lar
millivoltag
e
millivoltag
e
In: Na+
Higher resting potential
Few sodium channels reset
Slower upstroke
17. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
time
time
Intracellu
lar
Intracellu
lar
millivoltag
e
millivoltag
e
a pacemaker cell
Slow current of Na+ in;
note the resting potential
is less negative in a
pacemaker cell
-
-55
55
18. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
time
time
Intracellu
lar
Intracellu
lar
millivoltag
e
millivoltag
e
a pacemaker cell
Threshold voltage
-
-40
40
19. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
time
time
Intracellu
lar
Intracellu
lar
millivoltag
e
millivoltag
e
Ca++ flows in
20. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
time
time
Intracellu
lar
Intracellu
lar
millivoltag
e
millivoltag
e
. . . and K+ flows out
21. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
time
time
Intracellu
lar
Intracellu
lar
millivoltag
e
millivoltag
e
. . . and when it is negative
again, a few Na+
channels open
22. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
How a wave of electrical current
propagates through myocardium
{ Typically, an impulse originating
anywhere in the myocardium will
propagate throughout the heart
{ Cells communicate electrically via
“gap junctions”
{ Behaves as a “syncytium”
{ Think of the “wave” at a football
game!
23. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
The dipole field due to current flow in a myocardial cell at the
advancing front of depolarization.
Vm is the transmembrane potential.
Courtesy of Dr. Roger Mark.HST.542J Quantitative Physiology: Organ Transport Systems, Spring 2004. (Massachusetts
Institute of Technology: MIT OpenCourseWare). http://ocw.mit.edu (accessed June 17, 2008).
24. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
Cardiac Electrical Activity
Figure by MIT OpenCourseWare.
Q S
T
R
P
SA node
(Pacemaker)
AV node
(delay)
AV bundle
& branches
(Insulated)
Purkinje fibers (Activation)
Fibro-fatty atrioventricular
groove (Separates atrial and
ventricular tissue)
Contractile
Conductive
Nonconductive
25. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
Important specific structures
{ Sino-atrial node = pacemaker
(usually)
{ Atria
{ After electrical excitation:
contraction
{ Atrioventricular node (a tactical
pause)
{ Ventricular conducting fibers
(freeways)
{ Ventricular myocardium (surface
roads)
After electrical excitation: contraction
{
26. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
The Idealized Spherical Torso with the
Centrally Located Cardiac Source (Simple
dipole model)
Courtesy of Dr. Roger Mark. HST.542J Quantitative Physiology: Organ Transport Systems, Spring 2004. (Massachusetts
Institute of Technology: MIT OpenCourseWare). http://ocw.mit.edu (accessed June 17, 2008).
27. Figure by MIT OpenCourseWare. After F. Netter.
Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
Excitation of the Heart
28. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
Excitation of the Heart
Figure by MIT OpenCourseWare. After F. Netter.
29. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
Figure by MIT OpenCourseWare.
30. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
-1200
-1500
aVR
aVF
aVL
I
II
III
-900
-800
-300
+300
+600
+900
+1200
+1500
1800
00
Figure by MIT OpenCourseWare.
31. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
The temporal pattern of the heart vector
combined with the geometry of the standard
frontal plane limb leads.
Figure by MIT OpenCourseWare.
I
II
III
32. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
Cardiac Electrical Activity
Figure by MIT OpenCourseWare.
Courtesy of Dr. Roger Mark. HST.542J Quantitative
Physiology: Organ Transport Systems, Spring 2004.
(Massachusetts Institute of Technology: MIT OpenCourseWare).
http://ocw.mit.edu (accessed June 17, 2008). Figure adapted
from Phillips RE, Feeney MK, 1980 The Cardiac Rhythms.
Saunders, Philadelphia and from Hoffman BF, Cranefiel
PF 1960 Electrophysiology of the Heart. McGraw Hill, New York.
Q S
T
R
P
SA node
(Pacemaker)
AV node
(delay)
AV bundle
& branches
(Insulated)
Purkinje fibers (Activation)
Fibro-fatty atrioventricular
groove (Separates atrial and
ventricular tissue)
Contractile
Conductive
Nonconductive
33. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
Normal features of the electrocardiogram.
Figure by MIT OpenCourseWare. After p. 50 in Netter, Frank H. A Compilation of Paintings on the Normal and Pathologic
Anatomy and Physiology, Embryology, and Diseases of the Heart, edited by Fredrick F. Yonkman. Vol. 5 of The Ciba
Collection of Medical Illustrations. Summit, N.J.: Ciba Pharmaceutical Company, 1969.
34. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
Normal sinus rhythm
Figure 15 - Normal Sinus Rhythm—Rate 85
Figure by MIT OpenCourseWare.
35. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
What has changed?
Figure 16 - Sinus Tachycardia—Rate 122
Figure by MIT OpenCourseWare.
36. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
Sinus bradycardia
Figure 17 - Sinus Bradycardia—Rate 48
V1
Figure by MIT OpenCourseWare.
37. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
time
time
Intracellu
lar
Intracellu
lar
millivoltag
e
millivoltag
e
Neurohumeral factors
Vagal stimulation makes
the resting potential
MORE NEGATIVE. . .
38. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
time
time
Intracellu
lar
Intracellu
lar
millivoltag
e
millivoltag
e
Neurohumeral factors
. . . and the pacemaker
current SLOWER. . .
39. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
time
time
Intracellu
lar
Intracellu
lar
millivoltag
e
millivoltag
e
. . . and raise the
THRESHOLD
40. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
time
time
Intracellu
lar
Intracellu
lar
millivoltag
e
millivoltag
e
Catecholamines make
the resting potential
MORE EXCITED. . .
41. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
time
time
Intracellu
lar
Intracellu
lar
millivoltag
e
millivoltag
e
. . . and speed the
PACEMAKER
CURRENT. . .
42. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
time
time
Intracellu
lar
Intracellu
lar
millivoltag
e
millivoltag
e
. . . and lower the
THRESHOLD FOR
DISCHARGE. . .
43. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
time
time
Intracellu
lar
Intracellu
lar
millivoltag
e
millivoltag
e
Vagal Stimulation:
44. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
time
time
Intracellu
lar
Intracellu
lar
millivoltag
e
millivoltag
e
Ricardo Montelban Effect
Vagal Stimulation:
Image removed due to
copyright restrictions.
Photo of actor Ricardo
Montelban.
45. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
time
time
Intracellu
lar
Intracellu
lar
millivoltag
e
millivoltag
e
Adrenergic Stim. =
46. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
time
time
Intracellu
lar
Intracellu
lar
millivoltag
e
millivoltag
e
Adrenergic Stim. =
Potsy Effect
Image removed due to
copyright restrictions.
Photo of characters from TV
show “Happy Days,” including
Potsy.
47. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
Sinus arrhythmia
Figure 18 - Sinus Arrhythmia
Figure by MIT OpenCourseWare.
48. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
(
Atrial premature contractions
(see arrowheads)
Figure by MIT OpenCourseWare.
Figure 25 - Atrial Premature Contractions
49. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
50. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
{ Usually just a spark; rarely sufficient
for an explosion
{ “Leakiness” leads to pacemaker-like
current
{ Early after-depolarization
{ Late after-depolarization
51. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
What’s going on here?
Figure by MIT OpenCourseWare.
Figure 36 - Ventricular Premature Contractions
52. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
Wave-front Trajectory in a Ventricular
Premature Contraction.
53. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
Is this the same thing?
Figure by MIT OpenCourseWare.
Figure 24 - Ventricular Escape Beat
54. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
What’s going on here?
Figure 50 - Complete A-V Block with Junctional Escape Rhythm
Figure by MIT OpenCourseWare.
55. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
What’s going on here?
Figure 35 - Atrial Fibrillation (2 examples)
Figure by MIT OpenCourseWare.
56. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
Non-sustained ventricular tachycardia
(3 episodes)
Figure by MIT OpenCourseWare.
Figure 43 - Short Bursts of Ventricular Tachycardia
57. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
Slow Refractory
Quick Refractory
KeyWords:
Heterogeneous, Circus, Self-Perpetuating
Side “A” Side “B”
58. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
No Longer
Refractory
KeyWords:
Heterogeneous, Circus, Self-Perpetuating
Side “A” Side “B”
59. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
KeyWords:
Heterogeneous, Circus, Self-Perpetuating
Side “A” Side “B”
60. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
KeyWords:
Heterogeneous, Circus, Self-Perpetuating
Side “A” Side “B”
61. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
KeyWords:
Heterogeneous, Circus, Self-Perpetuating
Side “A” Side “B”
62. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
KeyWords:
Heterogeneous, Circus, Self-Perpetuating
Side “A” Side “B”
63. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
INCREASED
Refractory
Side “A” Side “B”
64. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
INCREASED
Refractory
Side “A” Side “B”
65. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
INCREASED
Refractory
Side “A” Side “B”
66. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
INCREASED
Refractory
Side “A” Side “B”
67. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
INCREASED
Refractory
Side “A” Side “B”
68. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
INCREASED
Refractory
Side “A” Side “B”
69. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
Ventricular Fibrillation
Figure 45 - Three Examples of Ventricular Fibrillation
Figure by MIT OpenCourseWare.
70. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
71. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
72. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
73. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
74. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
Heart attack
75. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
Figure by MIT OpenCourseWare.
-1200
-1500
aVR
aVF
aVL
I
II
III
-900
-800
-300
+300
+600
+900
+1200
+1500
1800
00
76. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
Figure by MIT OpenCourseWare.
77. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
Heart attack
78. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
Hyperkalemia
See ECG Wave-Maven (http://ecg.bidmc.harvard.edu/maven/mavenmain.asp) for
many other examples of how metabolic conditions can affect the ECG.
Courtesy of Ary Goldberger, M.D. Used with permission.
Source: Nathanson L A, McClennen S, Safran C, Goldberger AL. ECG Wave-Maven: Self-Assessment Program for Students and
Clinicians. http://ecg.bidmc.harvard.edu. Case #164.
79. Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].
Understanding the ECG:
A Cautionary Note
{ Basic cell electrophysiology, wavefront
propagation model, dipole model:
Powerful, but incomplete
{ There will always be electrophysiologic
phenomena which will not conform with
these explanatory models
{ Examples:
z metabolic disturbances
z anti-arrhythmic medications
z need for 12-lead ECG to record a 3-D
phenomenon
80. Questions?
Cite as: Andrew Reisner. Course materials for HST.582J / 6.555J / 16.456J, Biomedical Signal and Image Processing, Spring 2007. MIT OpenCourseWare
(http://ocw.mit.edu), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY].