Why the study?
Diagnosing fetal ECG signals on resource-limited wearable devices presents substantial challenges due to signal complexity and high computational demands.
Does a T5-based deep learning model accurately classify fetal heart rate from FECG signals while maintaining resource efficiency for wearable devices?
Does a T5-based deep learning model accurately classify fetal heart rate from FECG signals while maintaining resource efficiency for wearable devices?
A novel T5-based deep learning framework effectively classifies fetal heart rate from non-invasive abdominal ECG signals with high accuracy and low RMSE, demonstrating potential for resource-efficient wearable deployment.
Fetal Electrocardiogram (FECG) signals represent vital instruments for examining any irregularities in the heart’s functioning. Contemporary wearable technologies, including smartwatches and smartphones, now come with sophisticated sensors and computational systems designed to gather and process FECG signals from users. Lately, large language models such as T5 have attracted interest due to their capabilities in handling intricate patterns within data, positioning them as promising options for classifying morphological FECG signals and detecting arrhythmias. Nevertheless, diagnosing FECG signals on devices with limited resources presents considerable challenges owing to the complicated nature of the signals and the computational demands of implementing such algorithms on wearable tools. To tackle these difficulties, this paper suggests a strategy that merges T5-based learning methodologies to attain two main goals: (i) reducing the complexity of learning models without sacrificing diagnostic precision and (ii) ensuring performance in resource-limited wearable devices for ongoing monitoring of FECG signals. The research further investigates the implementation of the suggested T5-based algorithm through Software Codesign techniques to improve resource efficiency, concentrating on factors like reduced latency, decreased hardware usage, and enhanced energy efficiency. Comprehensive experiments were conducted using diverse FECG datasets and validated. The proposed T5-based methodology demonstrated significant improvements in diagnosing FECG signals compared to other learning frameworks, showcasing its effectiveness in managing complex data patterns and achieving high diagnostic performance. While the experimental findings highlight the T5-based model's potential for use in wearable devices. the study focused on the algorithm's adaptability and performance in software environments, paving the way for future exploration into resource-efficient implementations suitable for wearable applications.
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Patil et al. (2024) studied Fetal Heart Rate (FHR) abnormalities (n=5). T5-based Large Language Model architecture vs. Other learning frameworks was evaluated on Fetal Heart Rate (FHR) classification performance (RMSE).
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