Real-Time Ecg Signal Processing For Cardiac Monitoring

Q: What is the clinical evidence from this study?

Population: Arrhythmia / Cardiovascular disease. Intervention: Improved AlexNet model with Red Fox optimization vs. Previous literature models. Primary outcome: Classification accuracy for normal vs abnormal ECG signals.

Q: What does this research mean for the field?

The proposed ECG signal processing methodology achieves 98.8% accuracy on the PTB and MIT-BIH benchmark databases for cardiac monitoring. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

Aakansha Soy; Jharna Maiti; Karan Khati

doi:10.64252/n4dsj702

May 5, 2025International Journal of Environmental SciencesOpen Access

Real-Time Ecg Signal Processing For Cardiac Monitoring

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Why the study?

Artificial intelligence methods are needed to improve accuracy and efficiency in ECG signal processing for cardiac monitoring compared to previous approaches.

Key Points

To develop an automated real-time electrocardiogram signal processing and classification framework using deep learning to improve cardiac monitoring.
Evaluated the proposed methodology using recordings from the PTB and MIT_BIH benchmark databases.
Extracted features from pre-processed signals using three transforms, followed by Red Fox optimization for feature selection.
Implemented an Improved AlexNet deep learning model designed for deployment with Internet of Medical Things sensors.
Demonstrated an empirical classification accuracy of 98.8% on the PTB and MIT_BIH databases.
The Improved AlexNet model achieved 98.2% precision, 97.7% recall, and an F1 score of 98.4% when differentiating normal from abnormal ECG signals.

Structured PICO

P

Population

ECG signals from the PTB and MIT_BIH databases

I

Intervention

AI and IoMT technologies using Red Fox optimization and an Improved AlexNet model for real-time ECG signal processing

C

Comparator

Previous literature work

O

Outcome

Accuracy of ECG signal classification into normal and abnormal categories

An AI-based ECG signal processing model utilizing Red Fox optimization and Improved AlexNet achieved high accuracy in classifying arrhythmias, demonstrating potential for real-time remote cardiac monitoring.

Limitations

Lack of official clinical validation before implementation in clinical settings
Potential reluctance of medical practitioners to use unvalidated AI-based systems
Need for strong security mechanisms to safeguard patient data from cyberattacks targeting IoMT devices
AI models must undergo thorough validation before implementation into clinical settings
Medical practitioners might be reluctant to use AI-based ECG categorization systems in the absence of official clinical validation
Need for strong security mechanisms and privacy management to safeguard patient data from cyberattacks

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Implication

Promising benchmark AI ECG classification; leaves open prospective clinical validation before remote monitoring use.

Abstract

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Discussion

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Cite This Study

Soy et al. (2025) studied Arrhythmia / Cardiovascular disease. Improved AlexNet model with Red Fox optimization vs. Previous literature models was evaluated on Classification accuracy for normal vs abnormal ECG signals.

synapsesocial.com/papers/6ac1ab93cc6172083c92895f https://doi.org/10.64252/n4dsj702

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Real-Time Ecg Signal Processing For Cardiac Monitoring

Key Points

Structured PICO

Limitations

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

Real-Time Ecg Signal Processing For Cardiac Monitoring

Key Points

Structured PICO

Limitations

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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