A Novel FrWT Based Arrhythmia Detection in ECG Signal Using YWARA and PCA

Gupta, Varun; Mittal, M.; Mittal, Vikas

doi:10.1007/s11277-021-09403-1

Cited by 49 publications

(8 citation statements)

References 80 publications

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“…In medicine, all kinds of heart diseases need to process and analyze ECGs [ 34 – 37 ]. Therefore, this method is not limited to the field of heart failure classification, but can also be extended to other fields such as arrhythmia [ 38 – 40 ] and coronary artery disease [ 41 – 44 ].…”

Section: Discussionmentioning

confidence: 99%

Heart failure classification using deep learning to extract spatiotemporal features from ECG

Zhang,

Yuan-Lu,

Tang

et al. 2024

BMC Med Inform Decis Mak

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Background Heart failure is a syndrome with complex clinical manifestations. Due to increasing population aging, heart failure has become a major medical problem worldwide. In this study, we used the MIMIC-III public database to extract the temporal and spatial characteristics of electrocardiogram (ECG) signals from patients with heart failure. Methods We developed a NYHA functional classification model for heart failure based on a deep learning method. We introduced an integrating attention mechanism based on the CNN-LSTM-SE model, segmenting the ECG signal into 2 to 20 s long segments. Ablation experiments showed that the 12 s ECG signal segments could be used with the proposed deep learning model for superior classification of heart failure. Results The accuracy, positive predictive value, sensitivity, and specificity of the NYHA functional classification method were 99.09, 98.9855, 99.033, and 99.649%, respectively. Conclusions The comprehensive performance of this model exceeds similar methods and can be used to assist in clinical medical diagnoses.

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Section: Discussionmentioning

confidence: 99%

Heart failure classification using deep learning to extract spatiotemporal features from ECG

Zhang,

Yuan-Lu,

Tang

et al. 2024

BMC Med Inform Decis Mak

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“…Nevertheless, the role of fuzzy logic enables the execution of intelligent decision making in communication networks. Furthermore, intelligent techniques have also been demonstrated to play an important role in critical systems such as those used in the healthcare domain as seen in [34][35][36][37][38][39][40]. This arises due to the increasing role of computing systems and technologies in healthcare applications as seen in [41][42][43][44][45][46][47].…”

Section: Existing Work-network Systems and Support For Multimedia Con...mentioning

confidence: 99%

Stratospheric platform applications: reduced cognitive load for multimedia and education

Periola

2024

Wireless Netw

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The proliferation of computing solutions in future communication networks leads to the occurrence of cognitive visual overstimulation (CVO). CVO occurrence arises in multimedia content editing and provisioning of Science, Technology, Engineering, and Mathematics (STEM) education. The presented research proposes a high-altitude platform-based architecture that enable multimedia content editing related to space themes, and insertion in the edge of space i.e., the stratosphere (space horizon) environment. The use of the proposed architecture in content editing and STEM education contexts reduces the CVO by an average of (22–84.1) % and (13.6–31.7) %, respectively. Investigations show that the proposed architecture reduces the latency by an average of 30.3%, and 49.4% considering individual, and multiple content access, respectively. The use of the proposed architecture also reduces the power consumption associated with multimedia content editing by (8.05–42.9) % on average.

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“…Varun Gupta introduced a model that utilized fractional wavelet transform for pre-processing, Yule-Walker Autoregressive Analysis for feature extraction, and Principal Component Analysis for detection. This model achieved remarkable results, including a mean square error of 0.1656%, detection accuracy of 99.89%, and an output signal-to-noise ratio of 25.25 dB [4]. Tsai-Min Chen developed a model consisting of 5 CNN blocks, a bidirectional RNN layer, attention layer, and dense layer.…”

Section: Introductionmentioning

confidence: 99%

Efficient Edge-AI Models for Robust ECG Abnormality Detection on Resource-Constrained Hardware

Huang,

Herbozo Contreras,

Leung

et al. 2023

Preprint

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This study introduces two models, CLTC and CCfC, designed for abnormality identification using ECG data. Trained on the TNMG subset dataset, both models were evaluated for their performance, generative capacity, and resilience. They demonstrated comparable results in terms of F1 scores and AUROC values. The CCfC model achieved slightly higher accuracy, while the CLTC model showed better handling of empty channels. Remarkably, the models were successfully deployed on a resource-constrained microcontroller, proving their suitability for edge device applications. Generalization capabilities were confirmed through the evaluation of the CPSC dataset. The models' efficient resource utilization, occupying 70.6% of total storage and 9.4% of flash memory, makes them promising candidates for real-world healthcare applications. Overall, this research advances abnormality identification in ECG data, contributing to the progress of AI in healthcare.

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A Novel FrWT Based Arrhythmia Detection in ECG Signal Using YWARA and PCA

Cited by 49 publications

References 80 publications

Heart failure classification using deep learning to extract spatiotemporal features from ECG

Heart failure classification using deep learning to extract spatiotemporal features from ECG

Stratospheric platform applications: reduced cognitive load for multimedia and education

Efficient Edge-AI Models for Robust ECG Abnormality Detection on Resource-Constrained Hardware

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