• Login
    • Join
  • FOLLOW:
  • Subscribe Free
    • Magazine
    • eNewsletter
    Checkout
    • Magazine
    • News
    • Opinions
    • Top 30
    • Research
    • Supply Chain
    • Device Sectors
    • Directory
    • Events
    • Resources
    • Microsites
    • More
  • Magazine
  • News
  • Opinions
  • Top 30
  • Research
  • Supply Chain
  • Device Sectors
  • Directory
  • Events
  • Resources
  • Microsites
  • Current / Back Issues
    Features
    Editorial
    Digital Edition
    eNewsletter Archive
    Our Team
    Editorial Guidelines
    Reprints
    Subscribe Now
    Advertise Now
    Top Features
    Pharmaceutical Focus: A Look at Combination Products

    The Printed World: Additive Manufacturing in Medtech

    The Lost Year: 2020 Year in Review

    Extrusion Evolution

    Little Big Parts: Micromolding Under the Microscope
    OEM News
    Supplier News
    Service / Press Releases
    Online Exclusives
    Press Releases
    People in the News
    Product & Service Releases
    Supplier News
    Medtech Makers
    Technical Features
    International News
    Videos
    Product & Service Releases
    Live From Shows
    Top News
    TPI Partners with Zeiss

    PPE Moves into New Manufacturing Facility

    Neurent Medical Closes $25 Million Series B Financing

    Alcon Releases PRECISION1 for Astigmatism Contact Lenses in U.S.

    FDA OKs Canon Medical's AI-Powered, 90cm Bore CT
    From the Editor
    Blogs
    Guest Opinions
    Top Opinions
    Pharmaceutical Focus: A Look at Combination Products

    The Printed World: Additive Manufacturing in Medtech

    The Lost Year: 2020 Year in Review

    Extrusion Evolution

    Little Big Parts: Micromolding Under the Microscope
    Top 30 Medical Device Companies
    Market Data
    White Papers
    Top Research
    Fixing Face Mask Form and Function

    The Heart of the Matter: Trends in Cardiology

    Virtually the Same? The Challenges of Online Conferences

    Digital Health Delivers During a Year for the Ages

    Advanced Technology for Staking and Swaging Medical Plastics
    3D/Additive Manufacturing
    Contract Manufacturing
    Electronics
    Machining & Laser Processing
    Materials
    Molding
    Packaging & Sterilization
    R&D & Design
    Software & IT
    Testing
    Tubing & Extrusion
    Cardiovascular
    Diagnostics
    Digital Health
    Neurological
    Patient Monitoring
    Surgical
    Orthopedics
    All Companies
    Categories
    Company Capabilities
    Add New Company
    Outsourcing Directory
    maxon

    Medicoil

    Spectrum Plastics Group

    FUTEK Advanced Sensor Technology Inc.

    Element
    MPO Summit
    Industry Events
    Webinars
    Live From Show Event
    Industry Associations
    Videos
    Career Central
    eBook
    Slideshows
    Top Resources
    Meeting Critical Ventilator Product Requirements Amid Pandemic

    Impact of COVID-19 on the Medtech Supply Chain

    Finding the Upside to a Challenging Year

    Preparing Your Design Controls for FDA Approval

    A 'Trial and Error' Approach to Micromolded Parts
    Companies
    News Releases
    Product Releases
    Press Releases
    Product Spec Sheets
    Service Releases
    Case Studies
    White Papers
    Brochures
    Videos
    Outsourcing Directory
    Qosina Corp.

    Spectrum Plastics Group

    PTI Engineered Plastics Inc.

    MW Life Sciences

    Creganna Medical, part of TE Connectivity
    • Magazine
      • Current/Back Issues
      • Features
      • Editorial
      • Columns
      • Digital Editions
      • Subscribe Now
      • Advertise Now
    • News
    • Directory
      • All Companies
      • ALL CATEGORIES
      • Industry Associations
      • Company Capabilities
      • Add Your Company
    • Supply Chain
      • 3D/Additive Manufacturing
      • Contract Manufacturing
      • Electronics
      • Machining & Laser Processing
      • Materials
      • Molding
      • Packaging & Sterilization
      • R&D & Design
      • Software & IT
      • Testing
      • Tubing & Extrusion
    • Device Sectors
      • Cardiovascular
      • Diagnostics
      • Digital Health
      • Neurological
      • Patient Monitoring
      • Surgical
      • Orthopedics
    • Top 30 Company Report
    • Expert Insights
    • Slideshows
    • Videos
    • Podcasts
    • Resources
    • eBook
    • Infographics
    • Whitepapers
    • Research
      • White Papers
      • Case Studies
      • Product Spec Sheets
      • Market Data
    • MPO Summit
    • Events
      • Industry Events
      • Live From Show Events
      • Webinars
    • Microsite
      • Companies
      • Product Releases
      • Product Spec Sheets
      • Services
      • White Papers / Tech Papers
      • Press Releases
      • Videos
      • Literature / Brochures
      • Case Studies
    • About Us
      • About Us
      • Contact Us
      • Advertise with Us
      • eNewsletter Archive
      • Privacy Policy
      • Terms of Use
    Online Exclusives

    Using ECG to Diagnose Cardiovascular Disease

    How to turn raw data into results.

    Using ECG to Diagnose Cardiovascular Disease
    The ability to receive, automatically recognize, and make decisions based on remotely obtained ECG data provides doctors and patients with new ways to reduce unwelcome statistics.
    Related CONTENT
    • High-Volume COVID Antigen Test Receives FDA EUA
    • Siemens Healthineers’ Laboratory-Based IL-6 Assay Receives EUA
    • BioSerenity Achieves FDA Clearance for Wearable Device System
    • Hologic to Buy Cancer Test Maker Biotheranostics for $230M
    • Thyroid Function Tests Market in India to Reach $125 Million in 2025
    Sergey Kuznetsov, Software Engineer, Auriga10.16.19
    According to the World Health Organization, cardiovascular disease (CVD) is the leading cause of death worldwide. It’s estimated that 17.9 million people died from CVD in 2016, accounting for 31% of all deaths in the world. Of these, 85% occurred as a result of a heart attack or stroke. The main and most affordable way to diagnose CVD is ECG. The ability to receive, automatically recognize, and make decisions based on remotely obtained ECG data provides doctors and patients with new ways to reduce these unwelcome statistics.
     
    Automatic ECG rhythm recognition is already a classic task. Despite the fact that the first studies in the field of digital processing of ECG recordings appeared back in the 1970s, this area remains relevant for healthcare and continues to develop. Mainly, the changes concern improving the availability of continuous remote cardiac monitoring for ordinary patients within the framework of telemedicine systems.
     
    In recent years, research on this topic has focused on hunting for algorithms that are more accurate and less demanding of the source data. The methods of automatic recognition with increasing accuracy require an increasing amount of tagged data for training and testing models. The most accessible open data is collected on the PhysioBank project website. In addition, this resource is noteworthy in that it hosts annual competitions to define the properties of physiological data. In the 2017 competition, for example, the task was to isolate atrial fibrillation. Similar recognition quality w by two radically different approaches – feeding a large number of traditional indicators into an automatic algorithm and feeding primary raw data into a neural network.
     
    The classical approach to the training of recognition models involves preliminary filtering of input data from interference from the power supply network and broadband interference caused by the mobility of the electrodes and the natural currents of the body of muscle origin. Often, QRS complexes are detected in the signal, and the data is cut in accordance with their position.
     
    The option of direct data feed to a trained neural network is certainly easier from the point of view of data pre-processing and requires significantly less computing resources. Similar networks can be based on a DCNN structure. According to the atrial fibrillation (AFIB) recognition experience, using 10-second recordings hits the right compromise between recognition accuracy and the desire to reduce the amount of simultaneously processed data.
     
    A separate issue that the engineering community is facing is the lack of data for training. When solving recognition problems, first of all, it is necessary to determine the minimum sufficient amount of training sample. This exact problem was investigated by the Auriga team based on data from the publicly available MIT-BIH Arrythmia (mitdb) database and competition materials. We reproduced and evaluated various approaches to the recognition of cardiac arrhythmias.
     
    First of all, patients 102 and 104 were excluded from ECG recordings of 48 patients because they did not have MLII lead, which was required for our analysis. Fifteen rhythms already present in the markup were used for the study. Due to different numbers of records for different classes, the data of such classes is multiplied in order to equalize their power. Data preprocessing consists only of subtracting the average. The amplitude of the signal is not normalized, since it is known that a drop in amplitude is the most important sign of a critical condition of a patient, such as asystole. There is no asystole in the current data, but it is supposed to continue work with data expansion by records from other databases.
     
    Data multiplication for “poor” classes, training is carried out by sampling from a long implementation of overlapping 10-second windows. When examining the data, one can notice that manual marking of rhythms contains a systematic error in the first segment due to the beginning of the rhythm preferred by the expert with regard to the beat phase, while the 10-second segment in real recognition can start from an arbitrary place. The continuous rhythm intervals are rounded down to the nearest second. This interval is centered on the original, which gives a random start offset from zero to half a second (an average of a quarter second).
     
    To clear data from non-systematic emissions, several types of data are excluded from the sample:
    • Recordings marked by experts as noise;
    • Areas of normal sinus rhythm where rare episodes of disturbances such as extrasystoles were detected;
    • Fragments marked Q (unclassified beat), U (ECG cannot be read), or I (isolated QRS-like artifact).
     
    Within the pacer rhythm, normal beats are also allowed due to registration peculiarities that smooth the leading edge of the beat: tape recording, amplitude-frequency response distortion, and others.
     
    Then, a set of intervals is formed containing a single rhythm, the length of which is a multiple of 1 second and is not less than 10 seconds. Data for the final validation, which should not overlap with the training pattern, is separated from the study sample. The volume of test data is equivalent to 10% of the training data. To generate the required number of samples, the data must be multiplied. Table 1 presents the distribution of prepared data by class.

    Rhythm Files Parts Seconds Pieces PieTst Test Shifts Learn
    N 33 603 36731 3427 2824 10 0 3417
    AFIB 8 77 7392 706 629 10 0 696
    P 2 68 2516 227 159 10 0 217
    SBR 1 10 1567 152 142 10 0 142
    B 6 40 1443 127 87 10 0 117
    T 7 36 819 72 36 7 1 164
    BII 1 5 698 68 63 7 3 115
    AFL 3 17 538 48 31 5 1 101
    PREX 1 19 415 35 16 4 2 161
    SVTA 3 5 141 12 7 1 5 116
    VFL 1 4 132 12 8 1 8 107
    IVR 2 2 130 12 10 1 9 101
    AB 1 2 80 7 5 1 10 106
    VT 1 2 74 6 4 1 7 103
    NOD 2 5 73 6 1 1 8 109

    Table 1. Classification of Data
    Rhythm: The label of this rhythm in standard annotations.
    Files: Number of files where this rhythm is encountered.    
    Parts: Number of source intervals (length of at least 10 seconds, a multiple of a second).
    Seconds: The total length of Parts in seconds (in descending order).
    Pieces: The number of non-overlapping 10-second intervals into which Parts can be cut (the sum of the lengths, divided evenly by 10).
    PieTst: Parts lasting 20 seconds or more can give (Len // 10 - 1) Pieces for testing. Upon that, there will be no lost remainders shorter than 10 seconds.
    Test: The number of intervals for final testing. Minimum of three numbers:
    • 10% Pieces, rounded to the nearest integer
    • PieTst (we can cut as much as possible without small remainders)
    • 10% of the ordered number of items in the class
    Shifts: The number of required steps of overlapping windows per second to get windows is slightly larger than the ordered elements of this class. If = 0, then choose from nonoverlapping pieces.
    Learn: The number of resulting intervals, which is further thinned out to achieve a given number of class elements.
     
    All the work on the preparation of the training and test samples was carried out not with the data itself, but with records containing the sample number of the beginning of the fragment and the duration in seconds. Based on the prepared indices of these fragments, the data is extracted and subjected to the simplest preprocessing: subtraction of the constant component. In addition, each element is present in an inverted form for working with inverse superposition of electrodes (entry 114). Therefore, the real amount of data is doubled.
     
    After training and testing the DCNN network, the following results were obtained.

    Classification Report Confusion Matrix
    precision recall f1-score support rhythm N AFIB P SBR B T BII AFL PREX SVTA VFL IVR AB VT NOD
    0.91 1.00 0.95 20 N 20 0 0 0 0 0 0 0 0 0 0 0 0 0 0
    0.87 1.00 0.93 20 AFIB 0 20 0 0 0 0 0 0 0 0 0 0 0 0 0
    1.00 1.00 1.00 20 P 0 0 20 0 0 0 0 0 0 0 0 0 0 0 0
    1.00 1.00 1.00 20 SBR 0 0 0 20 0 0 0 0 0 0 0 0 0 0 0
    0.95 1.00 0.98 20 B 0 0 0 0 20 0 0 0 0 0 0 0 0 0 0
    1.00 0.86 0.92 14 T 0 2 0 0 0 12 0 0 0 0 0 0 0 0 0
    1.00 1.00 1.00 14 BII 0 0 0 0 0 0 14 0 0 0 0 0 0 0 0
    1.00 0.90 0.95 10 AFL 0 1 0 0 0 0 0 9 0 0 0 0 0 0 0
    1.00 1.00 1.00 8 PREX 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0
    1.00 1.00 1.00 2 SVTA 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0
    1.00 1.00 1.00 2 VFL 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0
    0.00 0.00 0.00 2 IVR 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1
    1.00 1.00 1.00 2 AB 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0
    1.00 1.00 1.00 2 VT 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0
    0.00 0.00 0.00 2 NOD 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
     
    0.96 158 Accuracy
    0.85 0.85 0.85 158 Macro average
    0.94 0.96 0.95 158 Weighted average
    0.968       Ranking-based average precision

    Table 2. DCNN Network Training and Testing Results
    Based on the results obtained, we can draw the first conclusions on the results of training. Some classes, with fairly insignificant numbers of records, cannot significantly affect network training – for example, IVR and NOD. For the remaining small classes, the network is most likely retrained. This is easily verified by validating ECG records of those people whose data were not used in the training of the neural network.
     
    It should be noted that if training is carried out based on one group of people and validation is done based on another group, then the rhythms present in only one record, as well as records containing one rhythm (6 rhythms, 4 records), will drop out of the classification.
     
    Out of the remaining 9 classes, validations showed good results only for 4.

    Classification Report   Confusion Matrix
    precision Recall f1-score support rhythm N AFIB B P T AFL SVTA NOD IVR
    0.50 1.00 0.66 154 N 154 0 0 0 0 0 0 0 0
    0.77 0.76 0.77 126 AFIB 26 96 0 0 0 2 2 0 0
    1.00 0.85 0.92 26 B 0 3 22 0 1 0 0 0 0
    1.00 0.99 1.00 290 P 0 2 0 288 0 0 0 0 0
    0 0 0 70 T 59 11 0 0 0 0 0 0 0
    0 0 0 50 AFL 48 1 0 0 0 0 1 0 0
    0 0 0 10 SVTA 0 6 0 0 0 4 0 0 0
    0 0 0 8 NOD 8 0 0 0 0 0 0 0 0
    0 0 0 20 IVR 15 5 0 0 0 0 0 0 0
     
    0.74 754 Accuracy
    0.36 0.40 0.37 754 Macro average
    0.65 0.74 0.68 754 Weighted average
    0.804       Ranking-based average precision

    Table 3. Four Classes with Good Results
    It is easy to see that the neural network is subject to strongly marked retraining for classes with a small training sample: T, AFL, SVTA. Good accuracy and specificity indicators for cross-validation on the training sample are not confirmed by testing on patients selected for validation. Moreover, in the error matrix, there is a tendency to mix small classes with a normal sinus rhythm (i.e., to some average ECG picture).
     
    For the remaining 4 classes, it makes sense to re-conduct the training and validation process. Validation results are slightly better for 3 classes. Presumably, as a result of filtering out noise of small classes from the training sample we achieve the following results.

    Classification Report   Confusion Matrix
    Precision Recall f1-score support rhythm N AFIB B P
    0.93 1.00 0.97 154 N 154 0 0 0
    0.93 0.92 0.92 126 AFIB 10 116 0 0
    1.00 0.62 0.76 26 B 1 9 16 0
    1.00 1.00 1.00 290 P 0 0 0 290
     
    0.97 596 Accuracy
    0.97 0.88 0.91 596 Macro average
    0.97 0.97 0.96 596 Weighted average
    0.983       Ranking-based average precision
     
    Table 4. Validation Results for the Remaining Four Classes
    From the studies conducted, we can conclude that records of even 2 or 3 patients may be sufficient for reliable recognition of heart rhythm pathologies. The quality of such models should be checked in practice with a mandatory allocation of a group of patients for validation. It appears that the amount of data necessary for each case depends on the rhythm disturbances traits peculiar to the specific form of pathology.
     
    Sergey Kuznetsov is a software engineer at Auriga.
    Related Searches
    • Software
    • engineering
    • research
    • health
    Related Knowledge Center
    • Cardiovascular
    Suggested For You
    High-Volume COVID Antigen Test Receives FDA EUA High-Volume COVID Antigen Test Receives FDA EUA
    Siemens Healthineers’ Laboratory-Based IL-6 Assay Receives EUA Siemens Healthineers’ Laboratory-Based IL-6 Assay Receives EUA
    BioSerenity Achieves FDA Clearance for Wearable Device System BioSerenity Achieves FDA Clearance for Wearable Device System
    Hologic to Buy Cancer Test Maker Biotheranostics for $230M Hologic to Buy Cancer Test Maker Biotheranostics for $230M
    Thyroid Function Tests Market in India to Reach $125 Million in 2025 Thyroid Function Tests Market in India to Reach $125 Million in 2025
    EKF Introduces Accurate Quantitative COVID-19 Antibody Test Kit EKF Introduces Accurate Quantitative COVID-19 Antibody Test Kit
    Mologic COVID-19 Rapid Antigen Test Receives CE Mark Approval Mologic COVID-19 Rapid Antigen Test Receives CE Mark Approval
    Philips Buys BioTelemetry for $2.8 Billion Philips Buys BioTelemetry for $2.8 Billion
    FDA Authorizes Abbott FDA Authorizes Abbott's Rapid, $25 COVID-19 Test for At-Home Use
    FDA, EU Nods for POC Testing on Siemens Healthineers FDA, EU Nods for POC Testing on Siemens Healthineers' epoc NXS Host Mobile Computer
    BellaSeno Establishes High-Throughput Additive Manufacturing Facility for Resorbable Implants BellaSeno Establishes High-Throughput Additive Manufacturing Facility for Resorbable Implants
    Combo COVID-19, Flu Home Test Gains EUA Combo COVID-19, Flu Home Test Gains EUA
    CRISPR-Based COVID-19 Test Uses Smartphone Camera CRISPR-Based COVID-19 Test Uses Smartphone Camera
    Cytek Biosciences Completes Series D Funding Round Cytek Biosciences Completes Series D Funding Round
    CE Mark Granted for Automated Glycemic Control and Continuous Diagnostics System CE Mark Granted for Automated Glycemic Control and Continuous Diagnostics System

    Related Online Exclusives

    • Cardiovascular | Surgical
      Prospects of AIMDs in the Implantable Medical Device Industry

      Prospects of AIMDs in the Implantable Medical Device Industry

      Active implantable medical devices (AIMDs) are a significant player in the medical implant market.
      Sunil Jha, Research Content Developer, Global Market Insights 11.18.20

    • Cardiovascular
      How to Improve ECG Rhythm Recognition Using a DCNN

      How to Improve ECG Rhythm Recognition Using a DCNN

      Comparing and contrasting methods of quality enhancement.
      Sergey Kuznetsov, Software Engineer, Auriga 07.06.20

    • Cardiovascular
      Examining the State of the Pacemaker Industry

      Examining the State of the Pacemaker Industry

      The advent of FDA-cleared MRI pacemakers is a significant trend.
      Sunil Jha, Research Content Developer, Global Market Insights 02.21.20


    • Cardiovascular | Diagnostics | Patient Monitoring
      Ladies First: An Examination of the Women

      Ladies First: An Examination of the Women's Health Device Industry

      Economic burden for women's diseases tops $500 billion, but women’s health makes up about 4 percent of total R&D funding for healthcare products and services.
      Hrishikesh Kadam, Research Content Developer, Global Market Insights (GMI) 12.11.19

    • Cardiovascular | Surgical
      CRM Device Demand Soars Amid Surging Cardiovascular Disease Prevalence

      CRM Device Demand Soars Amid Surging Cardiovascular Disease Prevalence

      Estimates claim the cardiac rhythm management industry's size to reach $13.7 billion by 2025.
      Paroma Bhattacharya, Research Content Developer, Global Market Insights 10.15.19

    • Cardiovascular | Digital Health | Software & IT | Surgical
      Gaming Through Surgery: Apps Prepare Clinicians for Challenging Cases

      Gaming Through Surgery: Apps Prepare Clinicians for Challenging Cases

      A video game app can be leveraged to introduce healthcare professionals to challenging procedures for a variety of clinical application areas.
      Sean Fenske, Editor-in-Chief 08.08.19


    • 3D/Additive Manufacturing | Cardiovascular
      3D Printing in the Medical Technology Industry: An Economic Case

      3D Printing in the Medical Technology Industry: An Economic Case

      A look at final part cost variables reveals additive manufacturing can be an efficient solution for many medtech applications, but only if approached correctly.
      Matt Sand, President, 3DEO 05.30.19

    • Cardiovascular | Contract Manufacturing | Electronics | Molding | Patient Monitoring
      Staying Dry: Waterproofing a Heart Monitor

      Staying Dry: Waterproofing a Heart Monitor

      Comar resolves market growth needs for the myPatch sl Holter recorder while enhancing design and addressing specific demands.
      Adrian Possumato, Principal Consultant, Medica Consulting 05.21.19

    • Cardiovascular
      Meeting Cardiac Stent Market Needs

      Meeting Cardiac Stent Market Needs

      Biotronik's PK Papyrus device was developed to provide physicians with a reliable covered stent that can be quickly deployed.
      Michael Barbella, Managing Editor 04.04.19


    • Cardiovascular
      Room for Improvement in Cardiac Stent Development

      Room for Improvement in Cardiac Stent Development

      Less-than-optimal outcomes will drive future growth in the global stent market.
      Michael Barbella, Managing Editor 03.29.19

    • Cardiovascular | Packaging & Sterilization | Surgical
      Why Does Medtech Rely on Metal Finishing?

      Why Does Medtech Rely on Metal Finishing?

      What is so important about using a metal finish, at least when it comes to cleanliness and sterilization?
      Megan Ray Nichols, Science Writer; Editor, Schooled By Science 03.20.19

    • Cardiovascular | Software & IT
      Special Considerations for Automatic Defibrillator Development and Algorithm Optimization

      Special Considerations for Automatic Defibrillator Development and Algorithm Optimization

      ...
      Alexander Naumov and Anastasia Lebedeva, Software Engineers, Auriga Inc. 02.28.19


    • Cardiovascular | Electronics | Surgical
      Reducing the Noise Impact of Medical Devices

      Reducing the Noise Impact of Medical Devices

      Diaphragm pumps can be particularly troublesome when it comes to unwanted noise in a point-of-care device.
      Jamie Campbell, Product Manager, Parker Precision Fluidics 01.07.19

    • Cardiovascular | Surgical | Tubing & Extrusion
      Film-Cast Tubing Specifications Used in Catheter-Based Medical Devices

      Film-Cast Tubing Specifications Used in Catheter-Based Medical Devices

      Film-cast tubing is not extruded tubing, so knowing the differences is critical for ensuring successful definition of its specifications.
      Brett Steen, Engineering Consultant 01.07.19

    • Cardiovascular | Neurological | Surgical
      The YouTube for Healthcare

      The YouTube for Healthcare

      VuMedi is a video platform that exclusively caters to medical professionals, physicians, and the healthcare industry.
      Sean Fenske, Editor-in-Chief 12.05.18


    Trending
    • Telemedicine, Regulatory Changes To Characterize Medtech Industry In 2021
    • Philips Buys Capsule Technologies In $635M Deal
    • Masimo Earns CE Mark For New Fingertip Pulse Oximeter
    • Understanding Food-Grade Vs. Biocompatibility For Medical Device Materials
    • Top 10 Trends In The Medical Device And Equipment Industry
    Breaking News
    • TPI Partners with Zeiss
    • PPE Moves into New Manufacturing Facility
    • Neurent Medical Closes $25 Million Series B Financing
    • Alcon Releases PRECISION1 for Astigmatism Contact Lenses in U.S.
    • FDA OKs Canon Medical's AI-Powered, 90cm Bore CT
    View Breaking News >
    CURRENT ISSUE

    November/December 2020

    • Pharmaceutical Focus: A Look at Combination Products
    • The Printed World: Additive Manufacturing in Medtech
    • The Lost Year: 2020 Year in Review
    • View More >

    Cookies help us to provide you with an excellent service. By using our website, you declare yourself in agreement with our use of cookies.
    You can obtain detailed information about the use of cookies on our website by clicking on "More information”.

    • About Us
    • Privacy Policy
    • Terms And Conditions
    • Contact Us

    follow us

    Subscribe
    Nutraceuticals World

    Latest Breaking News From Nutraceuticals World

    NIH Study Compares Low-Fat, Plant-based to Low-Carb, Animal-Based Diet
    Gadot Positions Mineral Line for Vegan Market
    Nutritfy India to Launch Global Broadcast Channel Covering Nutrition
    Coatings World

    Latest Breaking News From Coatings World

    IGL Coatings Launches Graphene Reinforced Dual System Ceramic Coating
    Miller Paint Declares Simple Serenity its 2021 Color of the Year
    TAUBMANS Paint by PPG Releases ‘Chromatic Joy’ Palettes
    Medical Product Outsourcing

    Latest Breaking News From Medical Product Outsourcing

    TPI Partners with Zeiss
    PPE Moves into New Manufacturing Facility
    Neurent Medical Closes $25 Million Series B Financing
    Contract Pharma

    Latest Breaking News From Contract Pharma

    WDSrx Expands Warehouse Network
    Piramal Inks Clinical Supply Deal with Theratechnologies
    Recipharm Signs Manufacturing Deal with Enzymatica
    Beauty Packaging

    Latest Breaking News From Beauty Packaging

    Lageen Tubes Launches Mono-Material PE Tube Solutions
    Jason Jones Loses Battle with Covid-19
    Weleda Celebrates 100 Years By 'Opening' Its Gardens
    Happi

    Latest Breaking News From Happi

    NY's 1,4-Dioxane Law May Impact Concentrated Cleaners
    Sabinsa's Top Scientist Addresses US FDA
    Ipsy Adds New Personal Care Brand
    Ink World

    Latest Breaking News From Ink World

    Morancé Soudure France Adds Comexi F2 MC 10-color Flexo Press
    THIMM Group Installs 1st Koenig & Bauer CorruFLEX
    Cowan Graphics Adds Fujifilm Inca OnsetX3 HS
    Label & Narrow Web

    Latest Breaking News From Label & Narrow Web

    Lemu Group engineers mask-making machine
    Niagara Label upgrades with Nilpeter flexo press
    Soma partners with Moore & Moore in US
    Nonwovens Industry

    Latest Breaking News From Nonwovens Industry

    Lemu Group Engineers Mask-Making Machine
    Armbrust American Adds Meltblown Manufacturing
    P&G Reports 8% Sales Increase
    Orthopedic Design & Technology

    Latest Breaking News From Orthopedic Design & Technology

    Siemens Healthineers’ DR Systems Cleared by FDA
    Former Medtronic Exec Appointed Zimmer Biomet's Chief Transformation Officer
    SeaSpine Launches Regatta Lateral Plates
    Printed Electronics Now

    Latest Breaking News From Printed Electronics Now

    UDC Subsidiary Adesis' Website Wins 2020 MarCom Platinum Award
    Roadsimple Modernizes Warehouse Ops with Zebra Technologies
    Graphene Flagship Launches Redesigned Website

    Copyright © 2021 Rodman Media. All rights reserved. Use of this constitutes acceptance of our privacy policy The material on this site may not be reproduced, distributed, transmitted, or otherwise used, except with the prior written permission of Rodman Media.

    AD BLOCKER DETECTED

    Our website is made possible by displaying online advertisements to our visitors.
    Please consider supporting us by disabling your ad blocker.


    FREE SUBSCRIPTION Already a subscriber? Login