Key result
The proposed LSTM-based learning model achieved approximately 85% classification accuracy for synthetic pulse audio signals, outperforming baseline machine learning approaches.
Why the study?
Very few studies have explored the classification of audio signal attributes in the context of intelligent healthcare monitoring.
A novel LSTM-based learning model efficiently classifies synthetic pulse audio signals with approximately 85% accuracy, demonstrating potential for future intelligent healthcare monitoring systems.
LSTM models warrant further testing for pulse signals; leaves open clinical validation before monitoring use.
The era of machine learning has opened up groundbreaking realities and opportunities in the field of medical diagnosis. However, it is also observed that faster and proper diagnosis of any diseases/medical conditions require proper analysis and classification of digital signal data. It indicates the proper identification of tumors in the brain. Brain magnetic resonance imaging (MRI) data has to be appropriately classified, and similarly, pulse signal analysis is required to evaluate the human heart operating condition. Several studies have used machine learning (ML) modeling to classify speech signals, but very few studies have explored the classification of audio signal attributes in the context of intelligent healthcare monitoring. The study thereby aims to introduce novel mathematical modeling to analyze and classify synthetic pulse audio signal attributes with cost-effective computation. The numerical modeling is composed of several functional blocks where deep neural network-based learning (DNNL) plays a crucial role during the training phase, and also it is further combined with a recurrent structure of long-short term memory (R-LSTM) feedback connections (FCs). The design approaches further experiment in a numerical computing environment in terms of accuracy and computational aspects. The classification outcome of the proposed approach shows that it attains approximately 85% accuracy, which is comparable to the baseline approaches and execution time.
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Kantipud et al. (2022) studied Pulse audio signal classification (n=30,000). LSTM-based learning model vs. SVM, Decision Tree, and Random Forest models was evaluated on Classification accuracy. The proposed LSTM-based learning model achieved approximately 85% classification accuracy for synthetic pulse audio signals, outperforming baseline machine learning approaches.
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