LDIAED: A lightweight deep learning algorithm implementable on automated external defibrillators

Nasimi, Fahimeh; Yazdchi, Mohammadreza

doi:10.1371/journal.pone.0264405

Cited by 3 publications

(2 citation statements)

References 31 publications

(27 reference statements)

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“…The system collects human manipulation information and tactile information, which are processed and transmitted to the manipulator to perform surgical action. The imaging system of the manipulator collects and amplifies the visual signal and transmits it to the VR imaging system to present the surgical scene to the operator in three dimensions [ 31 ]. Starting from different fields, the two scholars analyzed the application of virtual technology in real life.…”

Section: Discussionmentioning

confidence: 99%

Applying Lightweight Deep Learning-Based Virtual Vision Sensing Technology to Realize and Develop New Media Interactive Art Installation

Luo

2022

Computational Intelligence and Neuroscience

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The work intends to optimize the situation that interactive art devices and remote control based on traditional technology cannot meet people’s actual needs to a certain extent. With the assistance of Lightweight Deep Learning (LDL) models, Interactive Artistic Installation (IAI) shows excellent creative potential in terms of dimension, space, and sense. Virtual Vision Sensing Technology (VST) explores the emotional semantics in the human-machine environment with the help of interactive art, finds the emotional interaction elements between human and machine, and promotes Human-Computer Interaction (HCI). From the perspective of the media elements of interactive art, this paper reviews the virtual VST that subverts the expression of interactive art. Then, from the perspective of artistic creation, the impact of virtual VST on IAI thinking, methods, and artistic experience is analyzed. Thereupon, a scene construction method is designed where the physical equipment is premodeled. The model is loaded in real time with visual information. The proposed method does not require complex vision and laser scanning equipment or high-configured computer systems. The proposed new media IAI model realizes the real-time loading of the scene model. According to the physical equipment dynamic information obtained by the visual data acquisition system, the proposed method can keep the virtual scene and physical models in motion synchronization. Finally, experiment results corroborate that the environment will significantly interfere with the experimental results. The training data set with boundary occlusion will be more suitable for model training and better test results (about 97% accuracy). Hence, the research content can make the Virtual Reality works have better performance, especially the sense of experience from the perspective of aesthetics. Meanwhile, it also enriches the research theory in the field of new media art installation technology.

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Section: Discussionmentioning

confidence: 99%

Applying Lightweight Deep Learning-Based Virtual Vision Sensing Technology to Realize and Develop New Media Interactive Art Installation

Luo

2022

Computational Intelligence and Neuroscience

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“…Recent progress has been made with newly developed ML algorithms for shockable rhythm identification. 37 , 38 , 39 Although not the first CNN algorithm to be applied to a device with limited hardware resources, because of hardware constraints our CNN was limited to 5 convolutional layers and 1 connected layer. After input of the 7‐s ECG rhythm strip, running on an AED device, the algorithm averaged 383 ms to reach a shock or no‐shock decision (total time of 7.383 s).…”

Section: Discussionmentioning

confidence: 99%

Convolution Neural Network Algorithm for Shockable Arrhythmia Classification Within a Digitally Connected Automated External Defibrillator

Shen

Freed

Walter

et al. 2023

JAHA

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Background Diagnosis of shockable rhythms leading to defibrillation remains integral to improving out‐of‐hospital cardiac arrest outcomes. New machine learning techniques have emerged to diagnose arrhythmias on ECGs. In out‐of‐hospital cardiac arrest, an algorithm within an automated external defibrillator is the major determinant to deliver defibrillation. This study developed and validated the performance of a convolution neural network (CNN) to diagnose shockable arrhythmias within a novel, miniaturized automated external defibrillator. Methods and Results There were 26 464 single‐lead ECGs that comprised the study data set. ECGs of 7‐s duration were retrospectively adjudicated by 3 physician readers (N=18 total readers). After exclusions (N=1582), ECGs were divided into training (N=23 156), validation (N=721), and test data sets (N=1005). CNN performance to diagnose shockable and nonshockable rhythms was reported with area under the receiver operating characteristic curve analysis, F1, and sensitivity and specificity calculations. The duration for the CNN to output was reported with the algorithm running within the automated external defibrillator. Internal and external validation analyses included CNN performance among arrhythmias, often mistaken for shockable rhythms, and performance among ECGs modified with noise to mimic artifacts. The CNN algorithm achieved an area under the receiver operating characteristic curve of 0.995 (95% CI, 0.990–1.0), sensitivity of 98%, and specificity of 100% to diagnose shockable rhythms. The F1 scores were 0.990 and 0.995 for shockable and nonshockable rhythms, respectively. After input of a 7‐s ECG, the CNN generated an output in 383±29 ms (total time of 7.383 s). The CNN outperformed adjudicators in classifying atrial arrhythmias as nonshockable (specificity of 99.3%–98.1%) and was robust against noise artifacts (area under the receiver operating characteristic curve range, 0.871–0.999). Conclusions We demonstrate high diagnostic performance of a CNN algorithm for shockable and nonshockable rhythm arrhythmia classifications within a digitally connected automated external defibrillator. Registration URL: https://clinicaltrials.gov/ct2/show/NCT03662802 ; Unique identifier: NCT03662802

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A lightweight hybrid CNN-LSTM explainable model for ECG-based arrhythmia detection

Alamatsaz,

Tabatabaei,

Yazdchi

et al. 2024

Biomedical Signal Processing and Control

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LDIAED: A lightweight deep learning algorithm implementable on automated external defibrillators

Cited by 3 publications

References 31 publications

Applying Lightweight Deep Learning-Based Virtual Vision Sensing Technology to Realize and Develop New Media Interactive Art Installation

Applying Lightweight Deep Learning-Based Virtual Vision Sensing Technology to Realize and Develop New Media Interactive Art Installation

Convolution Neural Network Algorithm for Shockable Arrhythmia Classification Within a Digitally Connected Automated External Defibrillator

A lightweight hybrid CNN-LSTM explainable model for ECG-based arrhythmia detection

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