Cloud-Based Artificial Intelligence System for Large-Scale Arrhythmia Screening

Tseng, Chi-Ho; Lin, Chen; Chang, Hong; Liu, Cyuan-Cin; Serafico, Bess Ma. Fabinal; Wu, Li-Ching; Lin, Chih‐Ting; Hsu, Tien; Huang, Chun‐Yao; Lo, Men-Tzung

doi:10.1109/mc.2019.2933195

Cited by 9 publications

(3 citation statements)

References 17 publications

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“…governance and effectively avoid accidents but also improve the utilization and processing efficiency of information. At the same time, the combination of machine vision and smart city technologies will become one of the important components of the digital earth, so as to realize the integration of information collection and processing [25,26]. At present, with the performance improvement and cost reduction of equipment such as digital processors and cameras, machine vision-based video surveillance strategies will become the mainstream.…”

Section: R E T R a C T E D R E T R A C T E Dmentioning

confidence: 99%

Technical Research on Moving Target Monitoring and Intelligent Tracking Algorithm Based on Machine Vision

Zhou

Geng

2022

Wireless Communications and Mobile Computing

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Machine vision is an important branch of the rapid development of modern artificial intelligence, and it is a key technology to convert the image information of monitoring targets into digital signals. However, due to the wide range of machine vision applications, this research focuses on its application in video surveillance. In the era of artificial intelligence, the detection and tracking of moving objects have always been a key issue in video surveillance. The simulation of human vision is realized by combining the relevant functions of the computer and the image acquisition device, which enables the computer to have the ability to recognize the surrounding environment through images. The intelligent video analysis technology can automatically analyze and extract the key useful information from the video source with the powerful data processing ability of the computer, so as to realize the computer’s “understanding” of the video. It allows the computer to “understand” what is shown in the video or what kind of “event” happened and provides a new method and reliable basis for accident detection and accident analysis. Therefore, after a brief introduction to machine vision, moving target monitoring methods, and intelligent tracking algorithms, this paper will focus on moving target monitoring and intelligent tracking strategies for video surveillance. In addition, this paper will focus on introducing the principle of intelligent tracking algorithm through formulas and compare the accuracy and success rate of target monitoring and intelligent tracking between the machine vision-based algorithm and other algorithms during the experiment. Finally, experiments show that the monitoring and tracking effect of machine vision combined with “cloud” is the best, and the overall average can reach 85.7%. Based on this, this paper fully confirms the feasibility of the moving target monitoring and intelligent tracking algorithm based on machine vision.

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Section: R E T R a C T E D R E T R A C T E Dmentioning

confidence: 99%

Technical Research on Moving Target Monitoring and Intelligent Tracking Algorithm Based on Machine Vision

Zhou

Geng

2022

Wireless Communications and Mobile Computing

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“…The emerging artificial intelligence (AI) technology (e.g., machine learning) is a promising way to cope with the data explosion of IoT applications. The knowledge that AI learns from IoT data could bring many benefits to the QoS of IoT users [1]. However, the traditional cloud-centric learning paradigm faces many challenges, such as high maintenance costs, data privacy risk, and high service delay.…”

Section: Introductionmentioning

confidence: 99%

Blockchain-Based Incentive Energy-Knowledge Trading in IoT: Joint Power Transfer and AI Design

Lin

Bashir

et al. 2022

IEEE Internet Things J.

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Recently, edge artificial intelligence techniques (e.g., federated edge learning) are emerged to unleash the potential of big data from Internet of Things (IoT). By learning knowledge on local devices, data privacy-preserving and quality of service (QoS) are guaranteed. Nevertheless, the dilemma between the limited on-device battery capacities and the high energy demands in learning is not resolved. When the on-device battery is exhausted, the edge learning process will have to be interrupted. In this paper, we propose a novel Wirelessly Powered Edge intelliGence (WPEG) framework, which aims to achieve a stable, robust, and sustainable edge intelligence by energy harvesting (EH) methods. Firstly, we build a permissioned edge blockchain to secure the peer-to-peer (P2P) energy and knowledge sharing in our framework. To maximize edge intelligence efficiency, we then investigate the wirelessly-powered multi-agent edge learning model and design the optimal edge learning strategy. Moreover, by constructing a two-stage Stackelberg game, the underlying energy-knowledge trading incentive mechanisms are also proposed with the optimal economic incentives and power transmission strategies. Finally, simulation results show that our incentive strategies could optimize the utilities of both parties compared with classic schemes, and our optimal learning design could realize the optimal learning efficiency.

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“…For potential patients with AF symptoms, general procedures in hospitals usually involve an ECG Holter device that would be brought back home and used to record for >24 h [ 6 , 7 ]. The recent development of wearable devices with dry electrodes that enable patients to start recording only when symptoms occur [ 8 , 9 ] is another solution. However, some patients with AF go undiagnosed owing to the asymptomatic (‘silent AF’) and paroxysmal occurrence of AF [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Identification of Patients with Potential Atrial Fibrillation during Sinus Rhythm Using Isolated P Wave Characteristics from 12-Lead ECGs

Yang

Hsiao

Peng

et al. 2022

JPM

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Atrial fibrillation (AF) is largely underdiagnosed. Previous studies using deep neural networks with large datasets have shown that screening AF with a 12-lead electrocardiogram (ECG) during sinus rhythm (SR) is possible. However, the poor availability of these trained models and the small size of the retrievable datasets limit its reproducibility. This study proposes an approach to generate explainable features for detecting AF during SR with limited data. We collected 94,224 12-lead ECGs from 64,196 patients from Taipei Medical University Hospital. We selected ECGs during SR from 213 patients before AF diagnosis and randomly selected 247 age-matched participants without AF records as the controls. We developed a signal-processing technique, MA-UPEMD, to isolate P waves, and quantified the spatial and temporal features using principal component analysis and inter-lead relationships. By combining these features, the machine learning models yielded AUC of 0.64. We showed that, even with this limited dataset, the P wave, representing atrial electrical activity, is depicted by our proposed approach. The extracted features performed better than the bandpass filter-extracted P waves and deep neural network model. We provided a physiologically explainable and reproducible approach for classifying patients with AF during SR.

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Cloud-Based Artificial Intelligence System for Large-Scale Arrhythmia Screening

Cited by 9 publications

References 17 publications

Technical Research on Moving Target Monitoring and Intelligent Tracking Algorithm Based on Machine Vision

Technical Research on Moving Target Monitoring and Intelligent Tracking Algorithm Based on Machine Vision

Blockchain-Based Incentive Energy-Knowledge Trading in IoT: Joint Power Transfer and AI Design

Identification of Patients with Potential Atrial Fibrillation during Sinus Rhythm Using Isolated P Wave Characteristics from 12-Lead ECGs

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