A Generic and Patient-Specific Electrocardiogram Signal Classification System

2011 ◽  
pp. 79-98
Author(s):  
Turker Ince ◽  
Serkan Kiranyaz ◽  
Moncef Gabbouj
Keyword(s):  
Classification System ◽  
Signal Classification ◽  
Patient Specific ◽  
Electrocardiogram Signal

scholarly journals Electrocardiogram signal classification for automated delineation using bidirectional long short-term memory

2021 ◽  
Vol 22 ◽  
pp. 100507
Author(s):  
Siti Nurmaini ◽  
Alexander Edo Tondas ◽  
Annisa Darmawahyuni ◽  
Muhammad Naufal Rachmatullah ◽  
Jannes Effendi ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Signal Classification ◽  
Short Term ◽  
Term Memory ◽  
Electrocardiogram Signal ◽  
Long Short Term Memory

scholarly journals Application of Auxiliary Classifier Wasserstein Generative Adversarial Networks in Wireless Signal Classification of Illegal Unmanned Aerial Vehicles

2018 ◽  
Vol 8 (12) ◽  
pp. 2664 ◽  
Author(s):  
Caidan Zhao ◽  
Caiyun Chen ◽  
Zeping He ◽  
Zhiqiang Wu
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Real Time ◽  
Classification System ◽  
Outdoor Environment ◽  
Classification Model ◽  
Signal Classification ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Aerial Vehicles ◽  
Discriminative Model

Recently, many studies have reported on image synthesis based on Generative Adversarial Networks (GAN). However, the use of GAN does not provide much attention on the signal classification problem. In the context of using wireless signals to classify illegal Unmanned Aerial Vehicles (UAVs), this paper explores the feasibility of using GAN to improve the training datasets and obtain a better classification model, thereby improving the accuracy of classification. First, we use the generative model of GAN to generate a large datasets, which does not need manual annotation. At the same time, the discriminative model of GAN is improved to classify the types of signals based on the loss function of the discriminative model. Finally, this model can be used to the outdoor environment and obtain a real-time illegal UAVs signal classification system. Our experiments confirmed that the improvements on the Auxiliary Classifier Generative Adversarial Networks (AC-GANs) by limited datasets achieve excellent results. The recognition rate can reach more than 95% in the indoor environment, and this method is also applicable in the outdoor environment. Moreover, based on the theory of Wasserstein GANs (WGAN) and AC-GANs, a more robust Auxiliary Classifier Wasserstein GANs (AC-WGANs) model is obtained, which is suitable for multi-class UAVs. Through the combination of AC-WGANs and Universal Software Radio Peripheral (USRP) B210 software defined radio (SDR) platform, a real-time UAVs signal classification system is also implemented.

scholarly journals AOSpine—Spine Trauma Classification System: The Value of Modifiers: A Narrative Review With Commentary on Evolving Descriptive Principles

2019 ◽  
Vol 9 (1_suppl) ◽  
pp. 77S-88S ◽  
Author(s):  
Srikanth N. Divi ◽  
Gregory D. Schroeder ◽  
F. Cumhur Oner ◽  
Frank Kandziora ◽  
Klaus J. Schnake ◽  
...  
Keyword(s):  
Cervical Spine ◽  
Classification System ◽  
Injury Severity ◽  
Thoracolumbar Spine ◽  
Spinal Column ◽  
Narrative Review ◽  
Patient Specific ◽  
Upper Cervical Spine ◽  
Spine Trauma ◽  
Subaxial Cervical Spine

Study Design: Narrative review. Objectives: To describe the current AOSpine Trauma Classification system for spinal trauma and highlight the value of patient-specific modifiers for facilitating communication and nuances in treatment. Methods: The classification for spine trauma previously developed by The AOSpine Knowledge Forum is reviewed and the importance of case modifiers in this system is discussed. Results: A successful classification system facilitates communication and agreement between physicians while also determining injury severity and provides guidance on prognosis and treatment. As each injury may be unique among different patients, the importance of considering patient-specific characteristics is highlighted in this review. In the current AOSpine Trauma Classification, the spinal column is divided into 4 regions: the upper cervical spine (C0-C2), subaxial cervical spine (C3-C7), thoracolumbar spine (T1-L5), and the sacral spine (S1-S5, including coccyx). Each region is classified according to a hierarchical system with increasing levels of injury or instability and represents the morphology of the injury, neurologic status, and clinical modifiers. Specifically, these clinical modifiers are denoted starting with M followed by a number. They describe unique conditions that may change treatment approach such as the presence of significant soft tissue damage, uncertainty about posterior tension band injury, or the presence of a critical disc herniation in a cervical bilateral facet dislocation. These characteristics are described in detail for each spinal region. Conclusions: Patient-specific modifiers in the AOSpine Trauma Classification highlight unique clinical characteristics for each injury and facilitate communication and treatment between surgeons.

scholarly journals Patient Specific Machine Learning Models for ECG Signal Classification

2020 ◽  
Vol 167 ◽  
pp. 2181-2190 ◽  
Author(s):  
Saroj Kumar Pandey ◽  
Rekh Ram Janghel ◽  
Vyom Vani
Keyword(s):  
Machine Learning ◽  
Signal Classification ◽  
Patient Specific ◽  
Ecg Signal ◽  
Learning Models ◽  
Machine Learning Models
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