Over the last several years, several feature extraction techniques have been introduced. In general, all the feature extraction methods utilize one of the following three signal representation domains: temporal domain, spectral or joint time-frequency (TF) domain. In this seminar, advantages and challenges associated with feature extraction from temporal and spectral domains will be discussed. Spectral features generally assume the stationarity of the signal in the analysis frame, and do not provide any information on the temporal evolution or localization of the
extracted features. The talk will cover the recent advancements in TF feature extraction using methods based on adaptive signal representations such as pursuits-based. The application of the extraction and classification of complex instantaneous
signal parameters with respect to real world biomedical signals such as cardiac electrograms and brain electroencephalogram signals will be discussed.
This document provides background on Jacques Barth, an expert in cardiovascular imaging and risk assessment. It discusses the evolution of IMT measurement from 1986 to 2005, including the development of automated edge-contour detection software in 1992. The document also addresses issues around vulnerable plaques and reporting IMT measurements, and summarizes several studies assessing IMT as a marker of cardiovascular risk and insulin resistance.
This document provides background on Jacques Barth, an expert in cardiovascular imaging and risk assessment. It discusses the evolution of ultrasound technology for measuring intima-media thickness (IMT) from 1986 to 2005. IMT is an early marker of atherosclerosis and cardiovascular risk. The document also addresses issues around vulnerable plaques, reporting IMT measurements, and assessing cardiovascular risk in children and adolescents.
Fast Track en Cierre de Orejuela. Herramientas para facilitar el procedimient...Fundacion EPIC
This document discusses tools and techniques for facilitating left atrial appendage closure (LAAO) procedures. It provides data on success rates from clinical studies and registries of various LAAO devices. It also outlines a global strategy for LAAO, including recommendations for the pre-procedure, procedure, and post-procedure phases. Examples of specific tools and techniques discussed are imaging planning with CT and TEE, use of ICE and microTEE during the procedure, sedation versus general anesthesia, and techniques for early hospital discharge post-procedure. Two case examples of LAAO procedures are also presented.
The document discusses applications of deep learning in various medical domains including ophthalmology, dermatology, pathology, cardiology, neurosurgery and EMR. For ophthalmology, it describes using deep learning models for tasks like diabetic retinopathy detection from fundus images and age-related macular degeneration classification from OCT retina images. It also summarizes a study on diabetic retinopathy detection that achieved high performance but had limitations in reproducibility. For dermatology, it outlines the need for skin cancer classification from images and challenges in early detection.
Elsevier Medical Graph – mit Machine Learning zu Precision MedicineRising Media Ltd.
Elsevier Health Analytics entwickelt den Medical Knowledge Graph, welcher Korrelationen zwischen Krankheiten und zwischen Krankheiten und Behandlungen darstellt. Auf einem Gesamtdatensatz von sechs Millionen anonymisierten Patienten, beobachtbar über sechs Jahre, haben wir über 2000 Modelle erstellt, welche die Entwicklung von Krankheiten prognostizieren. Jedes Modell ist adjustiert für mehr als 3000 Kovariablen. Dazu kam ein Boosting Algorithmus mit Variablenselektion zum Einsatz. Die Betas der selektierten Variablen wurden extrahiert, getestet hinsichtlich Kausalität und Signifikanz, und daraus wurde die erste Version des Medical Graphen mit über 2000 Krankheitsknoten und 25.000 Effekt-Kanten gebaut. Der Graph wird aktuell in der Praxis getestet, mit dem Ziel, dem Arzt eine patienten-individuelle Entscheidungsunterstützung für die Behandlung zu geben.
Bioinformatics tools for the diagnostic laboratory - T.Seemann - Antimicrobi...Torsten Seemann
Torsten Seemann discussed bioinformatic tools for diagnostic laboratories using whole genome sequencing (WGS). He explained that WGS generates large amounts of sequencing reads that can be assembled de novo or aligned to references to identify single nucleotide polymorphisms (SNPs) and characterize genomes. Key applications of WGS include diagnostic identification, antimicrobial resistance profiling, virulence factor detection, and high-resolution epidemiological typing through SNP analysis and phylogenetic trees. Seemann emphasized that WGS analysis requires metadata, domain expertise, and open data sharing for maximum public health benefit.
This document provides background on Jacques Barth, an expert in cardiovascular imaging and risk assessment. It discusses the evolution of IMT measurement from 1986 to 2005, including the development of automated edge-contour detection software in 1992. The document also addresses issues around vulnerable plaques and reporting IMT measurements, and summarizes several studies assessing IMT as a marker of cardiovascular risk and insulin resistance.
This document provides background on Jacques Barth, an expert in cardiovascular imaging and risk assessment. It discusses the evolution of ultrasound technology for measuring intima-media thickness (IMT) from 1986 to 2005. IMT is an early marker of atherosclerosis and cardiovascular risk. The document also addresses issues around vulnerable plaques, reporting IMT measurements, and assessing cardiovascular risk in children and adolescents.
Fast Track en Cierre de Orejuela. Herramientas para facilitar el procedimient...Fundacion EPIC
This document discusses tools and techniques for facilitating left atrial appendage closure (LAAO) procedures. It provides data on success rates from clinical studies and registries of various LAAO devices. It also outlines a global strategy for LAAO, including recommendations for the pre-procedure, procedure, and post-procedure phases. Examples of specific tools and techniques discussed are imaging planning with CT and TEE, use of ICE and microTEE during the procedure, sedation versus general anesthesia, and techniques for early hospital discharge post-procedure. Two case examples of LAAO procedures are also presented.
The document discusses applications of deep learning in various medical domains including ophthalmology, dermatology, pathology, cardiology, neurosurgery and EMR. For ophthalmology, it describes using deep learning models for tasks like diabetic retinopathy detection from fundus images and age-related macular degeneration classification from OCT retina images. It also summarizes a study on diabetic retinopathy detection that achieved high performance but had limitations in reproducibility. For dermatology, it outlines the need for skin cancer classification from images and challenges in early detection.
Elsevier Medical Graph – mit Machine Learning zu Precision MedicineRising Media Ltd.
Elsevier Health Analytics entwickelt den Medical Knowledge Graph, welcher Korrelationen zwischen Krankheiten und zwischen Krankheiten und Behandlungen darstellt. Auf einem Gesamtdatensatz von sechs Millionen anonymisierten Patienten, beobachtbar über sechs Jahre, haben wir über 2000 Modelle erstellt, welche die Entwicklung von Krankheiten prognostizieren. Jedes Modell ist adjustiert für mehr als 3000 Kovariablen. Dazu kam ein Boosting Algorithmus mit Variablenselektion zum Einsatz. Die Betas der selektierten Variablen wurden extrahiert, getestet hinsichtlich Kausalität und Signifikanz, und daraus wurde die erste Version des Medical Graphen mit über 2000 Krankheitsknoten und 25.000 Effekt-Kanten gebaut. Der Graph wird aktuell in der Praxis getestet, mit dem Ziel, dem Arzt eine patienten-individuelle Entscheidungsunterstützung für die Behandlung zu geben.
Bioinformatics tools for the diagnostic laboratory - T.Seemann - Antimicrobi...Torsten Seemann
Torsten Seemann discussed bioinformatic tools for diagnostic laboratories using whole genome sequencing (WGS). He explained that WGS generates large amounts of sequencing reads that can be assembled de novo or aligned to references to identify single nucleotide polymorphisms (SNPs) and characterize genomes. Key applications of WGS include diagnostic identification, antimicrobial resistance profiling, virulence factor detection, and high-resolution epidemiological typing through SNP analysis and phylogenetic trees. Seemann emphasized that WGS analysis requires metadata, domain expertise, and open data sharing for maximum public health benefit.
Online Detection of Shutdown Periods in Chemical Plants: A Case StudyManuel Martín
In process industry, chemical processes are controlled and monitored by using readings from multiple physical sensors across the plants. Such physical sensors are also supplemented by soft sensors, i.e. adaptive predictive models, which are often used for computing hard-to-measure variables of the process. For soft sensors to work well and adapt to changing operating conditions they need to be provided with relevant data. As production plants are regularly stopped, data instances generated during shutdown periods have to be identified to avoid updating these predictive models with wrong data. We present a case study concerned with a large chemical plant operation over a 2 years period. The task is to robustly and accurately identify the shutdown periods even in case of multiple sensor failures. State-of-the-art methods were evaluated using the first half of the dataset for calibration purposes and the other half for measuring the performance. Results show that shutdowns (i.e. sudden changes) can be quickly detected in any case but the detection delay of startups (i.e. gradual changes) is directly related with the choice of a window size.
Role of embolic protection device in coronary and carotid interventionRamachandra Barik
This document discusses various catheter-based approaches for treating thrombus-rich lesions, including embolic protection devices. It describes proximal occlusion devices that block flow into the vessel using a balloon. Filter wires and occlusion balloons are distal protection devices that trap embolic debris downstream of the lesion. Thrombectomy devices like the Angiojet and Hydrolyzer use saline jets to dissolve and aspirate thrombi. Experience shows these technologies may help reduce complications during PCI of thrombus-rich lesions.
Role of embolic protection device in coronary and carotid interventionDrRajkumar Nune
This document discusses various catheter-based approaches for treating thrombus-rich lesions, including embolic protection devices and thrombectomy catheters. It describes trials comparing different embolic protection devices for use in stent grafting, as well as thrombectomy catheters such as the Angiojet and Hydrolyser that use aspiration or rheolytic technologies to remove thrombus. Novel devices are being developed and tested in clinical trials to debulk thrombus prior to interventions like PCI in order to reduce embolization risks.
This document summarizes research on transradial intervention and guiding catheter selection from 38 randomized controlled trials including over 18,000 patients. It finds that guiding catheter selection impacts procedural outcomes for transradial PCI. Appropriate catheters for transradial PCI include standard catheters for left coronary access and Amplatz or Ikari catheters for right coronary access. Limitations of existing transradial guiding catheters include inadequate backup support, but complex PCI can be performed with 6 French guiding catheters using techniques to enhance support. Practical suggestions are provided to optimize guiding catheter selection and support for transradial PCI.
2009 ferrara, congresso regionale, i tools da raggiungere nell'ablazione dell...Centro Diagnostico Nardi
1) The document discusses tools and techniques for achieving pulmonary vein isolation (PVI) to treat atrial fibrillation, including efficacy and safety data from multiple studies and techniques.
2) Mapping and ablation technologies have advanced, including 3D mapping systems, cryoballoon ablation, and multi-electrode catheters, improving identification of arrhythmogenic substrates and tailored lesion formation.
3) Large surveys of AF ablation outcomes show success rates without antiarrhythmic drugs of 74.9-84% for paroxysmal AF, 74.8% for persistent AF, and 71% for permanent AF, with overall complication rates of 4.54%. Advancing technologies may further improve results.
Massively Parallel Sequencing - integrating the Ion PGM™ sequencer into your ...Thermo Fisher Scientific
This document summarizes the integration of massively parallel sequencing (MPS) using the Ion PGMTM sequencer into a forensic laboratory. The project aims to begin transforming STR profiling to genomic technologies, add additional SNP markers in a single workflow, and enable non-human DNA testing. Initial results show sequencing of amplified STR products is possible but alignment is challenging. A custom panel of 280 targets including STRs, SNPs, and amelogenin was also tested with most targets detected across samples. Ongoing work focuses on improving sensitivity, reproducibility, and analyzing mixed samples. Implementation of MPS as a routine forensic service is estimated within 3-5 years.
DIAGNOSTICS-IMPACT ON THE PREMIUM CHANNEL - Heidelberg EngineeringHealthegy
Presentation from OIS@ASCRS 2016 - Kester Nahen, PhD, Managing Director
Video Presentation:
https://www.youtube.com/watch?v=2MqL1-bz4JE&list=PL1dmdBNnPTZJBhQxPOp0vdNg3s3wtN2yw&index=26
This document discusses various topics related to artificial intelligence and machine learning. It provides examples of how deep learning is being used for tasks like detecting diabetic eye disease, classifying arrhythmias from ECG signals, and localizing tumors in medical images. The document also notes limitations of current AI, such as its lack of common sense, and discusses how machine learning is being applied in other domains like predicting hospital readmissions, personalized medicine, and monitoring rainforests for illegal logging.
IRJET- A Survey on Classification and identification of Arrhythmia using Mach...IRJET Journal
This document summarizes a survey on classifying and identifying arrhythmias using machine learning techniques. The survey examines existing research that uses techniques like support vector machines, neural networks, and deep learning algorithms to classify arrhythmias based on features extracted from electrocardiogram (ECG) signals. The proposed system aims to classify four types of arrhythmias (normal rhythm, left/right bundle branch block, premature ventricular contraction) from the MIT-BIH arrhythmia database with high accuracy by optimizing the combination of preprocessing, feature extraction, and classification methods. Performance will be evaluated based on sensitivity, specificity, and accuracy metrics.
Dr. gerald pfister challenges, solutions and innovations in modern flowcyto...Hitham Esam
This document summarizes a presentation on challenges, solutions, and innovations in modern flow cytometry. It discusses instrumentation such as lasers, optics, and recent technologies like imaging flow cytometry and mass cytometry. It also covers challenges in areas like cell preparation, applications including single cell RNA sequencing, biosafety practices, and data analysis including statistics, gating strategies, and resources. The presentation aims to explore the state of the art in flow cytometry and identify opportunities for further innovation and improvement.
it's a graduation project aims to
Diagnose cardiovascular diseases in real-time using machine learning through extracting features from ECG signal with accuracy of 85% to 100%
Dr. Scott Kahn, CIO of Illumina, presents challenges and progress on big data solutions and its impact on scientific research at the 2013 Genome Informatics Alliance meeting.
Making Sense of Internet of Things: using AirBox as an ExampleLing-Jyh Chen
The document discusses the AirBox system, an IoT participatory sensing project for monitoring PM2.5 levels. AirBox uses affordable sensors in citizens' homes to create a wider monitoring network compared to traditional expensive stationary monitors. The project aims to enable grassroots environmental monitoring, empower citizens with open-source tools, and engage communities around open data and discussion of air quality issues. Over 4,000 AirBox devices have been deployed across 30 countries to provide finer-grained real-time air pollution data.
This document summarizes machine learning methods to learn improved EEG biomarkers in clinical trials from neural activity data. It discusses using machine learning techniques like convolutional neural networks to break down complex neural signals into interpretable patterns that may be related to clinical conditions, outcomes, susceptibility, or treatment responses. As a proof of concept, the document applies these methods to EEG data from a clinical trial of autism treatments, showing the machine learning approach can better classify treatment stage compared to traditional analysis methods and help identify potential neural biomarkers. It also discusses challenges in generalizing models to new patients and adapting methods to work with small clinical trial datasets.
A New Frontier of Precision Medicine: Using PET for Image-Guided Neurointerve...InsideScientific
A New Frontier of Precision Medicine: Using PET for Image-Guided Neurointerventions
Click to share on Twitter (Opens in new window)Click to share on LinkedIn (Opens in new window)Click to share on Facebook (Opens in new window)Click to share on Email (Opens in new window)
ON DEMAND
Experts discuss how PET/CT imaging can be used to enable image-guided neurointerventions and to study targeted delivery and clearance of therapeutic agents.
WATCH WEBINAR
Mice are by far the most frequently used animal for modeling disease and developing therapeutic strategies including neurointerventions. However, due to its anatomical and physiological barriers, the brain is a difficult target for delivery of therapeutic agents. Systemic administration is plagued with marginal brain accumulation and high risk of off-target side effects.
In this webinar sponsored by Scintica Instrumentation, Dr. Piotr Walczak, Dr. Mirosław Janowski and Dr. Wojciech Lesniak address this challenge and discuss why advanced imaging is essential to perform image-guided neurointerventions.
First, Dr. Janowski provides rationale as to how imaging can be used to better understand how therapeutic agents are delivered to the brain and subsequently cleared. Next, Dr. Walczak reviews methodological and technological advances for improving precision and reproducibility of brain targeting in mice based on MRI and two-photon microscopy. Finally, Dr. Lesniak presents recently-published results using ARGUS PET/CT to quantify intra-artrial delivery of antibodies, nanobodies and poly(amidoamine) dendrimers.
Key Learning Objectives Include:
- Why advanced imaging is essential to perform image-guided neurointerventions
- Why we need to visualize not only penetration of therapeutic agents to the brain, but also their clearance
- How image-guided procedures can be used to visualize and optimize delivery of therapeutic agents to the brain
Casting a Wider Net in Zebrafish Screening with Automated Microscopy and Imag...InsideScientific
Zebrafish are rapidly becoming a popular model organism for in vivo studies, particularly for drug screening and toxicology studies. Their benefits include fast development, economical husbandry, and direct amenability to microscopy since embryos are transparent. While imaging is fairly straightforward, in many cases, a substantial bottleneck to automated workflows is image analysis.
In this webinar, Dr. Jason Otterstrom and Dr. Alexandra Lubin describe an AI-powered analysis platform developed to enable true high-content screening of zebrafish, and highlight a range of applications where they have validated its performance. In brief, the easy-to-use software automatically identifies the fish outline, and internal anatomy & body regions with no required user inputs. They demonstrate the platform’s applicability in the context of counting GFP-labeled hematopoietic stem cells specifically in the tail region, along with measurement of x-ray induced apoptosis and dual-color analysis.
Key Topics Include:
- What is high-content imaging and how does it apply to Zebrafish
- How Deep-Learning can make analysis of zebrafish images truly high-content by extracting the fish’s anatomy
- Learn example assays where automated microscopy can facilitate use of zebrafish for screening studies
- One solution to orient zebrafish embryos without manual manipulation through specialized plates and software
How to measure and improve brain-based outcomes that matter in health careSharpBrains
Pioneers advancing health research, prevention and treatment will help us understand emerging best practices where targeted assessments, monitoring and interventions can transfer into significant healthcare and quality of life outcomes.
-- Chair: Alvaro Fernandez, CEO & Co-Founder of SharpBrains
-- Dr. Madeleine S Goodkind, staff psychologist at New Mexico VA Health Care System
-- Dr. Randy McIntosh, Vice-president of Research and Director of Baycrest’s Rotman Research Institute
-- Chris Berka, CEO and Co-Founder of Advanced Brain Monitoring (ABM)
Presentation @ The 2015 SharpBrains Virtual Summit http://sharpbrains.com/summit-2015/agenda
Abstract: Ontologies are used in numerous research disciplines and commercial applications to uniformly and semantically annotate real-world objects. Due to a rapid development of application domains the corresponding ontologies are changed frequently to include up-to-date knowledge. These changes dramatically influence dependent data as well as applications/systems, for instance, ontology mappings, that semantically interrelate ontologies. The talk will give an overview on evolution of ontologies and ontology-based mappings.
Intelligent Enterprise: How to Create the Customer Experience of the Future? ...Catalina Arango
This document discusses the importance of customer experience and how machine learning can help create superior customer experiences. It notes that 80% of customers have switched brands due to poor customer experience. The document outlines SAP's strategy to deliver an intelligent enterprise using machine learning as a key enabler. It provides examples of how machine learning can optimize customer churn, product recommendations, call centers, production planning, and more. Finally, it shows how multiple machine learning use cases can be applied across a business process like search, selection, ordering, payment, and reconciliation.
Online Detection of Shutdown Periods in Chemical Plants: A Case StudyManuel Martín
In process industry, chemical processes are controlled and monitored by using readings from multiple physical sensors across the plants. Such physical sensors are also supplemented by soft sensors, i.e. adaptive predictive models, which are often used for computing hard-to-measure variables of the process. For soft sensors to work well and adapt to changing operating conditions they need to be provided with relevant data. As production plants are regularly stopped, data instances generated during shutdown periods have to be identified to avoid updating these predictive models with wrong data. We present a case study concerned with a large chemical plant operation over a 2 years period. The task is to robustly and accurately identify the shutdown periods even in case of multiple sensor failures. State-of-the-art methods were evaluated using the first half of the dataset for calibration purposes and the other half for measuring the performance. Results show that shutdowns (i.e. sudden changes) can be quickly detected in any case but the detection delay of startups (i.e. gradual changes) is directly related with the choice of a window size.
Role of embolic protection device in coronary and carotid interventionRamachandra Barik
This document discusses various catheter-based approaches for treating thrombus-rich lesions, including embolic protection devices. It describes proximal occlusion devices that block flow into the vessel using a balloon. Filter wires and occlusion balloons are distal protection devices that trap embolic debris downstream of the lesion. Thrombectomy devices like the Angiojet and Hydrolyzer use saline jets to dissolve and aspirate thrombi. Experience shows these technologies may help reduce complications during PCI of thrombus-rich lesions.
Role of embolic protection device in coronary and carotid interventionDrRajkumar Nune
This document discusses various catheter-based approaches for treating thrombus-rich lesions, including embolic protection devices and thrombectomy catheters. It describes trials comparing different embolic protection devices for use in stent grafting, as well as thrombectomy catheters such as the Angiojet and Hydrolyser that use aspiration or rheolytic technologies to remove thrombus. Novel devices are being developed and tested in clinical trials to debulk thrombus prior to interventions like PCI in order to reduce embolization risks.
This document summarizes research on transradial intervention and guiding catheter selection from 38 randomized controlled trials including over 18,000 patients. It finds that guiding catheter selection impacts procedural outcomes for transradial PCI. Appropriate catheters for transradial PCI include standard catheters for left coronary access and Amplatz or Ikari catheters for right coronary access. Limitations of existing transradial guiding catheters include inadequate backup support, but complex PCI can be performed with 6 French guiding catheters using techniques to enhance support. Practical suggestions are provided to optimize guiding catheter selection and support for transradial PCI.
2009 ferrara, congresso regionale, i tools da raggiungere nell'ablazione dell...Centro Diagnostico Nardi
1) The document discusses tools and techniques for achieving pulmonary vein isolation (PVI) to treat atrial fibrillation, including efficacy and safety data from multiple studies and techniques.
2) Mapping and ablation technologies have advanced, including 3D mapping systems, cryoballoon ablation, and multi-electrode catheters, improving identification of arrhythmogenic substrates and tailored lesion formation.
3) Large surveys of AF ablation outcomes show success rates without antiarrhythmic drugs of 74.9-84% for paroxysmal AF, 74.8% for persistent AF, and 71% for permanent AF, with overall complication rates of 4.54%. Advancing technologies may further improve results.
Massively Parallel Sequencing - integrating the Ion PGM™ sequencer into your ...Thermo Fisher Scientific
This document summarizes the integration of massively parallel sequencing (MPS) using the Ion PGMTM sequencer into a forensic laboratory. The project aims to begin transforming STR profiling to genomic technologies, add additional SNP markers in a single workflow, and enable non-human DNA testing. Initial results show sequencing of amplified STR products is possible but alignment is challenging. A custom panel of 280 targets including STRs, SNPs, and amelogenin was also tested with most targets detected across samples. Ongoing work focuses on improving sensitivity, reproducibility, and analyzing mixed samples. Implementation of MPS as a routine forensic service is estimated within 3-5 years.
DIAGNOSTICS-IMPACT ON THE PREMIUM CHANNEL - Heidelberg EngineeringHealthegy
Presentation from OIS@ASCRS 2016 - Kester Nahen, PhD, Managing Director
Video Presentation:
https://www.youtube.com/watch?v=2MqL1-bz4JE&list=PL1dmdBNnPTZJBhQxPOp0vdNg3s3wtN2yw&index=26
This document discusses various topics related to artificial intelligence and machine learning. It provides examples of how deep learning is being used for tasks like detecting diabetic eye disease, classifying arrhythmias from ECG signals, and localizing tumors in medical images. The document also notes limitations of current AI, such as its lack of common sense, and discusses how machine learning is being applied in other domains like predicting hospital readmissions, personalized medicine, and monitoring rainforests for illegal logging.
IRJET- A Survey on Classification and identification of Arrhythmia using Mach...IRJET Journal
This document summarizes a survey on classifying and identifying arrhythmias using machine learning techniques. The survey examines existing research that uses techniques like support vector machines, neural networks, and deep learning algorithms to classify arrhythmias based on features extracted from electrocardiogram (ECG) signals. The proposed system aims to classify four types of arrhythmias (normal rhythm, left/right bundle branch block, premature ventricular contraction) from the MIT-BIH arrhythmia database with high accuracy by optimizing the combination of preprocessing, feature extraction, and classification methods. Performance will be evaluated based on sensitivity, specificity, and accuracy metrics.
Dr. gerald pfister challenges, solutions and innovations in modern flowcyto...Hitham Esam
This document summarizes a presentation on challenges, solutions, and innovations in modern flow cytometry. It discusses instrumentation such as lasers, optics, and recent technologies like imaging flow cytometry and mass cytometry. It also covers challenges in areas like cell preparation, applications including single cell RNA sequencing, biosafety practices, and data analysis including statistics, gating strategies, and resources. The presentation aims to explore the state of the art in flow cytometry and identify opportunities for further innovation and improvement.
it's a graduation project aims to
Diagnose cardiovascular diseases in real-time using machine learning through extracting features from ECG signal with accuracy of 85% to 100%
Dr. Scott Kahn, CIO of Illumina, presents challenges and progress on big data solutions and its impact on scientific research at the 2013 Genome Informatics Alliance meeting.
Making Sense of Internet of Things: using AirBox as an ExampleLing-Jyh Chen
The document discusses the AirBox system, an IoT participatory sensing project for monitoring PM2.5 levels. AirBox uses affordable sensors in citizens' homes to create a wider monitoring network compared to traditional expensive stationary monitors. The project aims to enable grassroots environmental monitoring, empower citizens with open-source tools, and engage communities around open data and discussion of air quality issues. Over 4,000 AirBox devices have been deployed across 30 countries to provide finer-grained real-time air pollution data.
This document summarizes machine learning methods to learn improved EEG biomarkers in clinical trials from neural activity data. It discusses using machine learning techniques like convolutional neural networks to break down complex neural signals into interpretable patterns that may be related to clinical conditions, outcomes, susceptibility, or treatment responses. As a proof of concept, the document applies these methods to EEG data from a clinical trial of autism treatments, showing the machine learning approach can better classify treatment stage compared to traditional analysis methods and help identify potential neural biomarkers. It also discusses challenges in generalizing models to new patients and adapting methods to work with small clinical trial datasets.
A New Frontier of Precision Medicine: Using PET for Image-Guided Neurointerve...InsideScientific
A New Frontier of Precision Medicine: Using PET for Image-Guided Neurointerventions
Click to share on Twitter (Opens in new window)Click to share on LinkedIn (Opens in new window)Click to share on Facebook (Opens in new window)Click to share on Email (Opens in new window)
ON DEMAND
Experts discuss how PET/CT imaging can be used to enable image-guided neurointerventions and to study targeted delivery and clearance of therapeutic agents.
WATCH WEBINAR
Mice are by far the most frequently used animal for modeling disease and developing therapeutic strategies including neurointerventions. However, due to its anatomical and physiological barriers, the brain is a difficult target for delivery of therapeutic agents. Systemic administration is plagued with marginal brain accumulation and high risk of off-target side effects.
In this webinar sponsored by Scintica Instrumentation, Dr. Piotr Walczak, Dr. Mirosław Janowski and Dr. Wojciech Lesniak address this challenge and discuss why advanced imaging is essential to perform image-guided neurointerventions.
First, Dr. Janowski provides rationale as to how imaging can be used to better understand how therapeutic agents are delivered to the brain and subsequently cleared. Next, Dr. Walczak reviews methodological and technological advances for improving precision and reproducibility of brain targeting in mice based on MRI and two-photon microscopy. Finally, Dr. Lesniak presents recently-published results using ARGUS PET/CT to quantify intra-artrial delivery of antibodies, nanobodies and poly(amidoamine) dendrimers.
Key Learning Objectives Include:
- Why advanced imaging is essential to perform image-guided neurointerventions
- Why we need to visualize not only penetration of therapeutic agents to the brain, but also their clearance
- How image-guided procedures can be used to visualize and optimize delivery of therapeutic agents to the brain
Casting a Wider Net in Zebrafish Screening with Automated Microscopy and Imag...InsideScientific
Zebrafish are rapidly becoming a popular model organism for in vivo studies, particularly for drug screening and toxicology studies. Their benefits include fast development, economical husbandry, and direct amenability to microscopy since embryos are transparent. While imaging is fairly straightforward, in many cases, a substantial bottleneck to automated workflows is image analysis.
In this webinar, Dr. Jason Otterstrom and Dr. Alexandra Lubin describe an AI-powered analysis platform developed to enable true high-content screening of zebrafish, and highlight a range of applications where they have validated its performance. In brief, the easy-to-use software automatically identifies the fish outline, and internal anatomy & body regions with no required user inputs. They demonstrate the platform’s applicability in the context of counting GFP-labeled hematopoietic stem cells specifically in the tail region, along with measurement of x-ray induced apoptosis and dual-color analysis.
Key Topics Include:
- What is high-content imaging and how does it apply to Zebrafish
- How Deep-Learning can make analysis of zebrafish images truly high-content by extracting the fish’s anatomy
- Learn example assays where automated microscopy can facilitate use of zebrafish for screening studies
- One solution to orient zebrafish embryos without manual manipulation through specialized plates and software
How to measure and improve brain-based outcomes that matter in health careSharpBrains
Pioneers advancing health research, prevention and treatment will help us understand emerging best practices where targeted assessments, monitoring and interventions can transfer into significant healthcare and quality of life outcomes.
-- Chair: Alvaro Fernandez, CEO & Co-Founder of SharpBrains
-- Dr. Madeleine S Goodkind, staff psychologist at New Mexico VA Health Care System
-- Dr. Randy McIntosh, Vice-president of Research and Director of Baycrest’s Rotman Research Institute
-- Chris Berka, CEO and Co-Founder of Advanced Brain Monitoring (ABM)
Presentation @ The 2015 SharpBrains Virtual Summit http://sharpbrains.com/summit-2015/agenda
Abstract: Ontologies are used in numerous research disciplines and commercial applications to uniformly and semantically annotate real-world objects. Due to a rapid development of application domains the corresponding ontologies are changed frequently to include up-to-date knowledge. These changes dramatically influence dependent data as well as applications/systems, for instance, ontology mappings, that semantically interrelate ontologies. The talk will give an overview on evolution of ontologies and ontology-based mappings.
Similar to Biomedical Signal Extraction for Computer-assisted Clinical Decision Making - Dr. Behnaz Ghoraani - WiDS Miami 2018 (20)
Intelligent Enterprise: How to Create the Customer Experience of the Future? ...Catalina Arango
This document discusses the importance of customer experience and how machine learning can help create superior customer experiences. It notes that 80% of customers have switched brands due to poor customer experience. The document outlines SAP's strategy to deliver an intelligent enterprise using machine learning as a key enabler. It provides examples of how machine learning can optimize customer churn, product recommendations, call centers, production planning, and more. Finally, it shows how multiple machine learning use cases can be applied across a business process like search, selection, ordering, payment, and reconciliation.
Transforming an Organization using Data Science - Successes & Challenges - Da...Catalina Arango
Many companies are going through a data science transformation. Armed with executive support, the teams in our company quickly learned what works and what doesn’t work in transforming how the company becomes a leader in data-driven insights. I will talk through some of the successes and challenges our company faced, provide lessons learned that have improved the way we are operating and adopting the culture shift as a result of embedding data science into the organization.
Natural Language Processing - Principles and Practice - Gracie DiazCatalina Arango
Work with unstructured data such as natural language processing (NLP) is a fascinating and nuanced field within machine learning and data science. While NLP benefits from an abundance of tools and cutting-edge research, it can be a bit overwhelming when getting started with applying these methods. This presentation will discuss concepts useful to approaching an NLP project and will walk through an example application. It will also note a few best-practices that the presenter has found along the way.
Insight into Today's Revenue Opportunities in Data Transformation and Digitalization Based on 20+ Years of Experience - Christine Trainor, former Director of Data Intelligence at Tyco International provides insights to applied data science from her years leading in this space.
Let's paint a Picasso - A Look at Generative Adversarial Networks (GAN) and i...Catalina Arango
This document provides an overview of Generative Adversarial Networks (GANs) and their applications. It explains the basic concepts of GANs including how they use generative and discriminative neural networks in an adversarial game-theory framework to generate new realistic data. Several types and applications of GANs are described, such as using GANs to generate images conditioned on text, edit images while preserving realism, and generate images of human poses. Challenges with GANs and potential future applications are also discussed.
WiDS Miami 2019 - In this talk, Tania Oliveira Sr. Director of Visa Consulting & Analytics will discuss the power of VisaNet´s data, and how Visa drives insights to help its clients. She will showcase specific examples of how VCA has combined Visa data with payments expertise in order to optimize Merchant’s performance and profitability.
The Power of Topology - Colleen Farrelly - WiDS Miami 2018Catalina Arango
A lot of data science coverage in the media focuses on big data—storage systems, deep learning, and analyzing data with billions or trillions of observations. However, there’s an equally pressing problem in many industries and smaller companies today: small sample sizes or small subgroups within larger datasets. Machine learning algorithms fail to converge. Statistical methods break down completely. And valuable insight is lost.
However, recent advances in a branch of machine learning called topological data analysis (TDA), along with novel applications of topology to existing statistical methods, have provided a toolset suited to the challenges of small data. These methods have great potential as the field of data science moves from quantity to quality of data. This talk overviews several of TDA’s major tools, as well as their applications to three projects in which traditional methods fail.
How AI is Transforming Industry - Dr. Monica DeZulueta - WiDS Miami 2018Catalina Arango
A talk given by Dr. Monica DeZulueta on how Artificial Intelligence (AI) can impact the industries in South Florida. What the Analysts are saying about the impact of AI on the economy and industry.
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
Natural Language Processing (NLP), RAG and its applications .pptxfkyes25
1. In the realm of Natural Language Processing (NLP), knowledge-intensive tasks such as question answering, fact verification, and open-domain dialogue generation require the integration of vast and up-to-date information. Traditional neural models, though powerful, struggle with encoding all necessary knowledge within their parameters, leading to limitations in generalization and scalability. The paper "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" introduces RAG (Retrieval-Augmented Generation), a novel framework that synergizes retrieval mechanisms with generative models, enhancing performance by dynamically incorporating external knowledge during inference.
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Biomedical Signal Extraction for Computer-assisted Clinical Decision Making - Dr. Behnaz Ghoraani - WiDS Miami 2018
1. Behnaz Ghoraani, Ph.D.
Assistant Professor
Florida Atlantic University
Computer and Electrical Engineering
Biomedical Signal Feature Extraction for Computer-assisted
Clinical Decision Making
Biomedical Signal and Image Analysis Lab
2. Agenda
— Signal Processing andAnalysisTools
— Research Projects at BSIA Lab
— Atrial Fibrillation
— Artifact Reduction inAuditory Evoked Potentials
— AutomaticAssessment of Medication States of Patients with
Parkinson’s Disease usingWearable Sensors
— Automatic Localization of Epileptic Spikes in EEGs of Children
with Infantile Spasms
Talk @WDSC 20182
3. Signal Analysis/Information Extraction/Decision Making
Feature
Extraction
Classifier
Trained
Classifier
Training Phase
Classification Phase
Classification Result
Sensor
Classification Scheme
Train
Features
Test
Features
Signal
Processing
Talk @WDSC 20183
4. -2 -1.5 -1 -0.5 0 0.5 1 1.5 2
0.5
1
1.5
2
2.5
3
Decision Making (Cont.)
— What is Signal classification?
Talk @WDSC 20184
13. Time – frequency map (TF tiling)
time
frequency
time
frequency
fr
tr
fr
tr
Heinsenberg’s boxes
Talk @WDSC 2018
FT
TF
13
14. Wavelets – Using a known small wave estimating an unknown signal
CWTx
ϕ
(τ,s) =
1
s
. x(t).w
t −τ
s
"
#
$
%
&
'dt∫
s
s
S=1/f
t1 t2 t3 t3
…. s1,s2,s3,s4………………
Mother wavelet
Talk @WDSC 201814
15. F>> F<<
S<< S>>
In Wavelets ‘scale’ is inversely tied to the ‘frequency ‘
time
Scaling
Wavelets
• Good time resolution and poor freq resolution at
higher frequencies
• Poor time resolution and good freq resolution at
lower frequencies.
freq
Time – frequency map (TF tiling) - Wavelets
s= window size
Variable, but restricted resolution constraints !!!Talk @WDSC 201815
17. Wigner-Ville distribution
Wx (t,ω)= x(t).x(t +τ).e− jωt
dτ∫
It is nothing but the Fourier transform of the auto correlation
of the signal. In other words it is same as STFT where the
window function is nothing but the signal itself !!
So far the best TF resolution is achieved only by Wigner-Ville.
Talk @WDSC 201817
18. Signal
What did we achieve ?
timetime
Wigner-ville
Cross terms
Talk @WDSC 201818
19. Why do we have cross terms ?
Wx (t,ω)= x(t).x(t +τ).e− jωt
dτ∫
(a+b) = multi component signal
x(t) . X(t+l) (a+b) (a+b) ( a2+b2+2ab)
( a2+b2+2ab)
“ CROSS TERMS ”Talk @WDSC 201819
21. How to solve this restricted resolution problem ?
An adaptive technique is needed, which should alter
its TF tiling to any resolution !
“ Adaptive Signal Decompistion”
“A Pursuit in search for the best
TF localized match”
Talk @WDSC 201821
22. Redundant dictionary
of TF functions for all
values of translation,
scaling and modulation
Signal projection
over the TF dictionary
After ‘m‘ iterations
Original signal
‘m’ TF functions + Signal residue
Matching Pursuit
Talk @WDSC 201822
23. In MP the scaling parameter is independent of frequency
time
Scaling
freq
This approach is much more adaptive with
no restrictions on windowing patterns .
s
s
Still the dictionary selection limits complete modeling of the signal
Matching Pursuit
Talk @WDSC 201823
31. TF Features
— Joint TF Moments
— Sparsity
— Discontinuity
— Coherency
Classifier
W
H
)(tx
V Matrix
Decomposition
TFD
Feature
Extraction
Talk @WDSC 201831
32. TF Feature Example
— 3-second audio signal
— TFD with 512 and 2048 frequency and time bins.
— TFM:
— TFM decomposition:
— TFM Features:
2048512´V
15512´W 204815´H
{ }MPDDSSMOMO whwhwh ,,,,,, )1()1(
Talk @WDSC 201832
33. Extension to Multi-channel Signals
Ankle
Ankle
Trunk
Time (s)
Frequency (Hz)
Sensors
Wrist
TrunkWrist
Talk @WDSC 201833
34. Tensor Decomposition
— Canonical Polyadic Decomposition (CPD)
— Decomposition to rank-1 components
— Rank-1 decompositions are the 𝒂𝒊, 𝒃𝒊 and 𝒄𝒊 where 𝑖 ∈
1,2,3, … 𝑹;
𝑨, 𝑩 and 𝑪 are Factor Matrices
Talk @WDSC 2018
T +⋯+ =
𝒂 𝟏
𝒃 𝟏 𝒃 𝑹
𝒂 𝑹
𝒄 𝟏 𝒄 𝑹
𝑨
𝑩
𝑪
≈
34
35. Some of the Research Projects @ BSIA Lab
35 Talk @WDSC 2018
36. Current BSIA Lab Projects
— Atrial Fibrillation (AF)
• Catheter guidance in AF ablation
• AF detection from surface ECG
• Risk detection in external DC cardioversion
• Scar quantification in endocardium duringAF
• Scar quantification in LA from delayed-enhance
MRI
— Sudden Cardiac Death (SCD)
• Risk stratification of SCD
• 3D distribution of stress inducedTWA
— Artifact Rejection of EEGAuditory Evoked
Potential
— Quantification of Hypsarrhythmia in
Infantile Spasms
• Temporal and Spectral quantification of
EEG
• Epileptic discharge detection from EEG
— Parkinson’s Disease
— Automated detection of medication off
vs. dyskinesia from ambulatory gate data
in PD patients
— Structure-constrained Basis Pursuit For
Compressed Sensing
— Real-time Speech Recognition Using
Compressively Sensed Samples
— TF feature extraction using Dictionary
learning
Talk @WDSC 201836
37. Atrial Fibrillation
— Among these heart problems, atrial fibrillation (AF) is the
most common heart rhythm disorder that affects millions of
people, accounting for frequent hospitalizations, increased
risks of stroke, heart failure and mortality.
Talk @WDSC 201837
38. • QRS Complex
• Represents contraction of
the ventricles
• T Wave
• Represents relaxation of
the ventricles
Electrocardiogram
• PWave
• Represents contraction of the atria
Talk @WDSC 201838
43. AF Projects
— Identifying Atrial Fibrillation Episodes from ECG recordings.
— Developing a New Computer-aided Clinical Decision
Support System For Prediction of Successful Post-
cardioversion PatientsWith Persistent Atrial Fibrillation.
Talk @WDSC 201843
44. Computer-aided Clinical Decision Support System For
Prediction of Successful Post-cardioversion Patients
With Persistent Atrial Fibrillation
Talk @WDSC 201844
Overall outline of the study. Standard supervised learning approach is applied
consisting of a feature extraction step followed by a classification step. Leave-
one-out cross validation is used to evaluate the predictive power of our
technique.
48. Results
Talk @WDSC 201848
Dataset: 40 persistent AF patients who had a successful external DCE
cardioversion therapy. Prior to cardioversion, a10-minute 12-lead ECG was
recorded. Twenty patients had maintained SR (AF-Free) after 2-week follow-up and
20 had a relapse of AF (AF-Relapse).
Receiver operating characteristic
analysis using leave-one-out cross
validation. The AUC is 0.97 and the
best sensitivity and specificity are
100% and 95%, respectively.
49. EEG Auditory Evoked Potentials
Talk @WDSC 201849
Latency – neural conduction/processing time
Amplitude – strength/magnitude of response
52. Talk @WDSC 2018
D. Sinkiewicz, L. Friesen and B.
Ghoraani,“Analysis of Cochlear
Implant Artifact RemovalTechniques
Using the ContinuousWavelet
Transform”, in the proceedings of the
36th Annual International IEEE EMBS
Conference, Pages: 5482-5485,
September, 2014.
D. Sinkiewicz, L. Friesen, and
B. Ghoraani,“A Novel Method for
Extraction of Neural Response from
Cochlear Implant Auditory Evoked
Potentials”, MEP Journal, December
2016.
52
53. Parkinson’s Disease
— Parkinson disease (PD) is a chronic progressive neurological
disorder that will affect over half a million Americans by
2030.
— A chronic progressive neurological disorder that leads to
different motor and non-motor disabilities.
— Bradykinesia: impairment of the power of voluntary movements
that leads to slow movements.
— Tremor: consists of hand and leg tremor while resting.
— Dyskinesias: Uncontrolled muscles movements that is induced
by levodopa with long-term medication.
Talk @WDSC 201853
54. 54
OFF and ON States for One ofThe Patients
Dataset
Talk @WDSC 2018
55. Translating Multidimensional Sensor Data to Guide
Self-management for Patients with Parkinson
Disease
Talk @WDSC 201855
1
2
Deep Brain Stimulator/
Drug Infusion Pump
Body-worn
Inertial Sensors
Developed Algorithm
Before medication
(OFF)
UPDRS-49; mAIMS=5
1 hour after medication
(partial ON)
UPDRS=30; mAIMS=7
2 hours after medication
(ON with dyskinesia)
UPDRS=17; mAIMS=16
3 hours after medication
(OFF again)
UPDRS=42; mAIMS=4
ON
OFF
40 sec
Classification
Certainty
RTI Status Report
Existing
Devices/Algorithms
Motor Impairment Report
Clinical Visits For
Physician Interpretation
3
a
b
c
d
Clinically
Actionable
Information
Front Back
58. Classification Report
Talk @WDSC 201858
M. Hssayeni, M. Burack , J.Adams, B. Ghoraani “Personalized Assessment of Response to
Therapeutic Intervention in Individuals with Parkinson's Disease”, under preparation to the
IEEETransactions on Biomedical and Health Informatics.
60. Infantile Spasms
— Quantification of Hypsarrhythmia in Infantile Spasms
Talk @WDSC 2018
!"#$!(%$&)!
$$%!
!
&'!
!
(!
)!
*+,-.,/01!(23)!
!
*+,-.,/01!(23)!
!
456,!(7,0)!
!"#$!(%$&)!
a)! b)! c)!
d)! e)!
Traitruengsakuly S, Seltzerz LE, Paciorkowskiz AR, Ghoraani B.
Automatic Localization of Epileptic Spikes in EEGs of Children with
Infantile Spasms, 37th IEEE Engineering In Medicine and Biology Proceedings:
6194- 6197.August 2015, Milan, Italy.60
61. TF Feature Extraction
Talk @WDSC 2018
0 0.2 0.4 0.6 0.8 1
0
0.2
0.4
0.6
0.8
1
False Positive Rate (1−specificity)
TruePositiveRate(sensitivity)
(b) ROC based on F2
AUC = 98.56% ( = 10)
AUC = 93.14% ( = 0.5)
Classification Performance
Using SupportVector Machine
S.Traitruengsakul, L. E. Seltzer,A. R. Paciorkowski, and B. Ghoraani,
“Developing A Novel Epileptic Discharge LocalizationAlgorithm for
Electroencephalogram Infantile Spasms During Hypsarrhythmia”, Medical
& Biology Engineering and Computing Journal, January 2017.
61
62. Some of the members of BSIA Lab
Talk @WDSC 201862
63. q Dr. Moussa Mansour,
Massachusetts General
Hospital
q Dr. Jamie Adams, University
of Rochester Medical Center
q Dr. Michelle Burack,
University of Rochester
Medical Center
q Dr. David Huang, University of
Rochester Medical Center
q Dr. Lendra Friesen, University
of Connecticut
q Dr. Laurie Seltzer, Neurology,
University of Rochester
Medical Center
q Dr. Elizabeth M. Cherry,
School of Mathematical
Sciences, RIT
q Dr. Arkady M. Pertsov, SUNY
Upstate Medical University
School of
Mathematical
Sciences
RIT
q National Institutes of Health
(NIH) - 1R15HL127663-01
q NSF Advance - HRD-1209115.
q I-SENSE Seed Funding
63 Talk @WDSC 2018