A Real-Time Cardiac Arrhythmia Classification System with Wearable Electrocardiogram

2011 ◽  
Author(s):  
Sheng Hu ◽  
Zhenzhou Shao ◽  
Jindong Tan
Keyword(s):  
Real Time ◽  
Cardiac Arrhythmia ◽  
Classification System

scholarly journals A Real-Time Cardiac Arrhythmia Classification System with Wearable Sensor Networks

Sensors ◽  
2012 ◽  
Vol 12 (9) ◽  
pp. 12844-12869 ◽  
Author(s):  
Sheng Hu ◽  
Hongxing Wei ◽  
Youdong Chen ◽  
Jindong Tan
Keyword(s):  
Sensor Networks ◽  
Real Time ◽  
Cardiac Arrhythmia ◽  
Classification System ◽  
Wearable Sensor

DESIGN OF A VLSI HAMMING NEURAL NETWORK FOR ARRHYTHMIA CLASSIFICATION

2009 ◽  
Vol 18 (04) ◽  
pp. 825-839 ◽  
Author(s):  
BEHZAD GHANAVTI ◽  
GHOLAMREZA SHOMALNASAB
Keyword(s):  
Neural Network ◽  
Cardiac Arrhythmia ◽  
Classification System ◽  
Cmos Technology ◽  
Input Pattern ◽  
Implantable Cardioverter Defibrillators ◽  
Binary Input ◽  
Cardioverter Defibrillators

The implantable cardioverter defibrillators (ICDs) detect and treat dangerous cardiac arrhythmia. This paper describes a VLSI neural network chip to be implemented using 0.35 μ CMOS technology which acts as an intercardia tachycardia classification system. The Hamming network used to classify non binary input pattern and also reduce impact of noise, drift and offset inherent in analog application. Simulation result using HSPICE and level 49 parameters (BSIM3V3) that verify the functionality of circuit are presented.

scholarly journals Real-Time Wheat Classification System for Selective Herbicides Using Broad Wheat Estimation in Deep Neural Network

2019 ◽  
Vol 9 (1) ◽  
pp. 153
Author(s):  
Arif Ullah ◽  
Nazri Mohd Nawi ◽  
Anditya Arifianto ◽  
Imran Ahmed ◽  
Muhammad Aamir ◽  
...  
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Classification System ◽  
Deep Neural Network

A Sensing Chair

1999 ◽  
Author(s):  
Hong Z. Tan
Keyword(s):  
Automatic Control ◽  
Real Time ◽  
Classification System ◽  
Tracking System ◽  
Pressure Sensors ◽  
Sensory Systems ◽  
Human Computer Interactions ◽  
Sitting Posture ◽  
Haptic Sensing ◽  
Posture Tracking

Abstract This paper is concerned with how objects in an environment can be made aware of people via haptic sensing. It was motivated by the desire to make our environment “smarter” by providing it with sensory systems similar to our own. The work reported here focuses on an object that is involved in virtually all human-computer interactions, yet has remained sensory-deprived — the chair. A real-time sitting posture classification system has been developed using surface-mounted pressure sensors placed on the seatpan and backrest of a chair. The ultimate goal of this work is to build a robust multi-user sitting-posture tracking system that will have many applications including ergonomics and automatic control of airbag deployment in a car. Challenges for reaching the goal and plans of nature work are discussed.

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