ECG Decision Support System based on feedforward Neural Networks

Lassoued, Hela; Ketata, Raouf; Yacoub, Slim

doi:10.21307/ijssis-2018-029

Cited by 7 publications

(4 citation statements)

References 36 publications

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“…Complementing these approaches, Ref. [33] assesses the efficacy of neural networks in an Electrocardiogram (ECG) DSS for categorizing heartbeats as normal or abnormal, thus supporting medical professionals to make an assessment of the patient's state and helping them proceed with the appropriate therapy. In a similar vein, the authors of [34] present a web-based DSS to efficiently predict, plan, and respond to fire and flood events.…”

Section: Related Work/existing Ds System Methodologiesmentioning

confidence: 99%

Methodologies and Handling Techniques of Large-Scale Information in Decision Support Systems for Complex Missions

Tsavdaridis,

Papaodysseus,

Karadimas

et al. 2024

Applied Sciences

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Designing integrating systems for support, real-time monitoring, and executing of complex missions is challenging, since they often fail due to high levels of complexity and overwhelming volume of input data. Past attempts have resorted to “ad hoc” solutions, which face issues of being non-updatable, non-upgradable, and not applicable to similar missions, necessitating a complete redesign and reconstruction of the system. In the national defense and security sector, the impact of this reconstruction requirement leads to significant costs and delays. This study presents advanced methodologies for organizing large-scale datasets and handling complex operational procedures systematically, enhancing the capabilities of Decision Support Systems (DSSs). By introducing Complex Mission Support Systems (CMSSs), a novel SS sub-component, improved accuracy and effectiveness are achieved. The CMSS includes mission conceptualization, analysis, real-time monitoring, control dynamics, execution strategies, and simulations. These methods significantly aid engineers in developing DSSs that are highly user-friendly and operational, thanks to human-reasoning-centered design, increasing performance and efficiency. In summary, the systematic development of data cores that support complex processes creates an adaptable and adjustable framework in a wide range of diverse missions. This approach significantly enhances the overall sustainability and robustness of an integrated system.

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Section: Related Work/existing Ds System Methodologiesmentioning

confidence: 99%

Methodologies and Handling Techniques of Large-Scale Information in Decision Support Systems for Complex Missions

Tsavdaridis,

Papaodysseus,

Karadimas

et al. 2024

Applied Sciences

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“…The PNN also has an advantage over other RBFNN in terms of training speed [58]. Table 1 provided a detailed comparison of the three benchmark methods used in this study [59]. The final results are elaborated on in the upcoming sections.…”

Section: Radial Basis Function Neural Network (Rbfnn)mentioning

confidence: 99%

An NLP-Deep Learning Approach for Product Rating Prediction Based on Online Reviews and Product Features

Amirifar,

Lahmiri,

Zanjani

2024

IEEE Trans. Comput. Soc. Syst.

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An NLP-Deep Learning approach for Product Rating Prediction Based on Online Reviews and Product FeaturesTolou AmirifarThis study focuses on predicting the popularity of a product based on its overall rating score.Unlike previous studies that focus on predicting the review rating based on sentiment analysis and polarity of the reviews, in this thesis, the effect of product features in determining its popularity is directly measured and analyzed in order to predict its overall rating score. To this end, a methodology consisting of three phases is considered. Phase 1 predicts the overall rating by feeding the general product features, extracted from the online product information available on Amazon webpages to a Deep Learning (DL) model. Phase 2 identifies other features that customers care about the most by applying the Named Entity Recognition (NER) algorithm to the customer online reviews; and lastly, Phase 3 feeds the combination of the general and custom features to the DL model to predict the overall rating score of the product.The experimental results on a dataset of laptop products, collected from Amazon, indicate an impressive performance of the proposed approach, which is mainly attributed to including custom product features to the inputs of the DL algorithm when compared with the existing method. More precisely, the proposed model could achieve the highest accuracy score of 84.01%, 84.68% for recall, 87.63% for precision, and 84.06% for F1 score. Applying this procedure could help businesses identify the specific areas of strengths and weaknesses of their products or services from the perspective of their customers, allowing them to thrive in today's competitive markets. from the Department of Supply Chain and Business Technology Management at the John Molson School of Business, for their noble supervision, support with full encouragement and enthusiasm, valuable suggestions, and motivating advice. This thesis would not have been possible without your trust in me and your guidance from the initial step in research that allowed me to develop an understanding of the subject. I am grateful for my parents whose constant love and support keep me motivated and confident. My accomplishments and success are because they believed in me. Finally, I owe my deepest gratitude to my husband, Ali. I am forever thankful for the unconditional love and support throughout the entire thesis process and every day. v Contents List of Figures .

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“…The methods for classifying cardiac arrhythmias use the same MIT-BIH database composed of 17 arrhythmia categories. In the second approach, the classification of the heart rhythm (QRS complex) is built on the classification of 2 to 7 classes [15]. In other approach the use of discrete wavelet transformation (DWT) preprocessing with MLP, RBFNN, PNN to detect normal and abnormal beats is explained in [16]- [18].…”

Section: Introductionmentioning

confidence: 99%

A deep learning-based cardio-vascular disease diagnosis system

Hamdan

Aksa

Omari

et al. 2022

IJEECS

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Recently ehealth technologies are becoming an overwhelming aspect of public health services that provides seamless access to healthcare information. Machine learning tools associated with IoT technology play an important role in developing such health technologies. This paper proposes a decision support system-based system (DSS) to make diagnosis of cardiovascular diseases. It uses deep learning approaches that classify electrocardiogram (ECG) signals. Thus, a two-stage long-short term memory (LSTM) based neural network architecture, along with an adequate preprocessing of the ECG signals is designed as a diagnosis-aided system for cardiac arrhythmia detection based on an ECG signal analysis. This deep learning based cardio-vascular disease diagnosis system (namely ‘DLCVD’) is built to meet higher performance requirements in terms of accuracy, specificity, and sensitivity. This must also be capable of an online real-time classification. Experimental results using the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database show that DLCVD led to outstanding performance

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ECG Decision Support System based on feedforward Neural Networks

Cited by 7 publications

References 36 publications

Methodologies and Handling Techniques of Large-Scale Information in Decision Support Systems for Complex Missions

Methodologies and Handling Techniques of Large-Scale Information in Decision Support Systems for Complex Missions

An NLP-Deep Learning Approach for Product Rating Prediction Based on Online Reviews and Product Features

A deep learning-based cardio-vascular disease diagnosis system

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