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The document describes a BISA ECG device powered by Medtrics that provides 24/7 continuous monitoring. It allows for constant heart monitoring without interrupting daily activities. This device monitors the heart around the clock for any abnormalities or changes in heart activity.
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Sensors 21-02777
1. Researchers developed a portable ECG device using dry capacitive electrodes and a driven right leg circuit to reject common-mode interference.
2. Simulation and testing of the electronic interface showed high input impedance, suitable common-mode rejection, and readable P, QRS, and T waves.
3. The developed device uses an ESP32 microcontroller to digitize and wirelessly transmit ECG signals, demonstrating the potential for multi-lead wearable ECG monitoring.
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Maa s bisa (3)
Medical Analytics As a Service (MaaS) provides a RESTful API and machine learning platform for analyzing medical data like ECGs. The platform can recognize 90% of common heart conditions from ECGs, including atrial fibrillation, premature ventricular contractions, and more. It was validated against databases from MIT and the American Heart Association and achieves performance on par with top models. MaaS also offers denoising, heart rate variability metrics, and a portal for ECG processing and analytics.
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This document describes Medical Analytics As a Service (MaaS), which provides APIs, analytics, and a medical database for integrating health data. Key services include ECG analysis that can recognize 90% of common heart conditions using machine learning, and an in-development karyotyping service that aims to automate chromosome classification using deep learning. Metrics provided by ECG analysis include AFib detection accuracy of 94.7% sensitivity and 96.7% PPV compared to MIT databases. The karyotyping service aims to reduce testing time from 2 hours to 30 seconds with 99.7% classification accuracy.
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VectraplexECGFinal Brochure 3.1
The VectraplexECG System provides continuous, real-time monitoring of ECG changes suggestive of an acute myocardial infarction (AMI) using a novel Cardiac Electrical Biomarker (CEB) called VectraplexAMI. It requires only 5 electrodes versus the standard 12-lead ECG which uses 10 electrodes. The system analyzes cardiac electrical field data to generate a VectraplexAMI index number that is displayed in real-time, allowing for early detection of potential AMIs. This faster detection could allow for quicker treatment interventions compared to current protocols that rely on serial blood draws to detect cardiac marker changes.
Tractus Matrix IT Flyer
TRACTUS provides medical implant scanning solutions that can scan implants as small as 0.5mm^2 with 30 micron cell sizes. The scanner sends implant identification data to electronic health records and is integrated with major healthcare systems and databases to accurately track utilization for post-market surveillance. TRACTUS solutions simplify unique device identification tracking for hospitals, manufacturers, and regulatory bodies by automating scanning and documentation of medical implants directly into patient medical records.
IOT Based Patient Health Monitoring System Using WIFI
Health is given the extreme importance now a days by each country with the advent of the novel corona virus. So in this aspect, an IOT based patient health monitoring system is the best solution for such an epidemic. With an improvement in technology and miniaturization of sensors, there have been attempts to utilize the new technology in various areas to improve the quality of human life. As a result, this project is an attempt to solve a health care problem currently society is facing covid 19.The main objective of the project was to design for to reduce the corona virus, reduce the components and man power. The framework can be utilized to constantly screen the wellbeing parameters of a patient. The body temperature, heart rate and spo2 can be measured from anyplace on the globe utilizing IOT Internet of Things . IOT monitoring of health helps in preventing the spread of disease as well as to get a proper diagnosis of the state of health, even if the doctor is at far distance. We proposed a nonstop checking and control instrument to screen the patient condition and store the patient information’s in server utilizing Wi Fi Module based remote correspondence. Hence the proposed architecture collects the sensor data through ESP32 microcontroller and relays it to the WIFI where it is processed and analyzed for remote viewing. Feedback actions based on the analyzed data can be sent back to the doctor or guardian through Google sheet and or SMS alerts in case of any emergencies. Marupuri Usha Priya | Muthu Pandi K | Priya S | Dr. Kishore Kumar Arjunsingh "IOT Based Patient Health Monitoring System Using WIFI" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd45143.pdf Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/45143/iot-based-patient-health-monitoring-system-using-wifi/marupuri-usha-priya
Sensors 21-02777
1. Researchers developed a portable ECG device using dry capacitive electrodes and a driven right leg circuit to reject common-mode interference.
2. Simulation and testing of the electronic interface showed high input impedance, suitable common-mode rejection, and readable P, QRS, and T waves.
3. The developed device uses an ESP32 microcontroller to digitize and wirelessly transmit ECG signals, demonstrating the potential for multi-lead wearable ECG monitoring.
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This document discusses an ECG analysis partnership that can recognize 90% of common heart diseases from ECG readings. It describes several conditions the analysis can detect, including atrial fibrillation, premature arterial contractions, premature ventricular contractions, and sinus bradycardia. The analysis has advantages of being validated against several databases, performing as well or better than other models for arrhythmia detection, allowing comprehensive heart disease detection across multiple conditions, and working with all 12 leads of ECG data.
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This document describes Medical Analytics As a Service (MaaS), which provides APIs, analytics, and a medical database for integrating health data. Key services include ECG analysis that can recognize 90% of common heart conditions using machine learning, and an in-development karyotyping service that aims to automate chromosome classification using deep learning. Metrics provided by ECG analysis include AFib detection accuracy of 94.7% sensitivity and 96.7% PPV compared to MIT databases. The karyotyping service aims to reduce testing time from 2 hours to 30 seconds with 99.7% classification accuracy.
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The document summarizes Medtrics' Medical Analytics as a Service (MaaS) platform. The platform provides APIs and machine learning models to analyze ECG and karyotyping data. For ECG analysis, it can recognize 90% of common heart conditions from ECGs, including atrial fibrillation, premature ventricular contractions, and more. It has been validated against standard databases and achieves high accuracy. For karyotyping, it aims to automate the analysis through deep learning to reduce human error, cost, and processing time from hours to seconds. Roadmaps are provided for further improving the capabilities of ECG and karyotyping analysis over multiple phases.
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This document describes a Medical Analytics as a Service (MaaS) platform that provides three main services: 1) A RESTful API that allows access to historical and ongoing health data in seconds to minutes. 2) Analytics powered by machine learning that analyze comprehensive health data. 3) A medical database composed of verified medical knowledge curated by data scientists. It then provides more details on ECG analysis, karyotyping with neural networks, and an interactive API targeted towards health IT developers. The business model roadmap includes growing subscribers and integrating with medical professionals.
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The document describes a BISA ECG device powered by Medtrics that provides 24/7 continuous monitoring. It allows for around-the-clock heart monitoring without interrupting daily activities. In 3 sentences or less, the high-level summary is a device that continuously monitors heart activity remotely.
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