InfusedHeart: A Novel Knowledge-Infused Learning Framework for Diagnosis of Cardiovascular Events

In the undertaken study, we have used a customized dataset termed "Cardiac-200" and the benchmark dataset "PhysioNet." which contains 1500 heartbeat acoustic event samples (without augmentation) and 1950 samples (with augmentation) heartbeat acoustic events such as normal, murmur...

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Vydáno v:IEEE transactions on computational social systems Ročník 11; číslo 3; s. 3060 - 3069
Hlavní autoři: Pandya, Sharnil, Gadekallu, Thippa Reddy, Reddy, Praveen Kumar, Wang, Weizheng, Alazab, Mamoun
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.06.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Acoustic classification
Acoustics
Artificial neural networks
automated knowledge extraction
Cardiac arrest
cardiovascular events
Cluster analysis
Clustering
Computer and Information Sciences Computer Science
Convolutional neural networks
Customization
Data- och informationsvetenskap
Datasets
Deep learning
diagnosis
Fourier transforms
Heart beat
knowledge-infused learning framework
Machine learning
Monitoring
Neural networks
Recurrent neural networks
Signal to noise ratio
Stethoscope
Support vector machines
Texture
Vector quantization
ISSN:2329-924X, 2373-7476
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Popis
Shrnutí:In the undertaken study, we have used a customized dataset termed "Cardiac-200" and the benchmark dataset "PhysioNet." which contains 1500 heartbeat acoustic event samples (without augmentation) and 1950 samples (with augmentation) heartbeat acoustic events such as normal, murmur, extrasystole, artifact, and other unlabeled heartbeat acoustic events. The primary reason for designing a customized dataset, "cardiac-200," is to balance the total number of samples into categories such as normal and abnormal heartbeat acoustic events. The average duration of the recorded heartbeat acoustic events is 10-12 s. In the undertaken study, we have analyzed and evaluated various heartbeat acoustic events using audio processing libraries such as Chromagram, Chroma-cq, Chroma-short-time Fourier transform (STFT), Chroma-cqt, and Chroma-cens to extract more information from the recorded heartbeat sound signals. The noise removal process has been carried out using local binary pattern (LBP) methodology. The noise-robust heartbeat acoustic images are classified using long short-term memory (LSTM)-convolutional neural network (CNN), recurrent neural network (RNN), LSTM, Bi-LSTM, CNN, K-means Clustering, and support vector machine (SVM) methods. The obtained results have shown that the proposed InfusedHeart Framework had outclassed all the other customized machine learning and deep learning approaches such as RNN, LSTM, Bi-LSTM, CNN, K-means Clustering, and SVM-based classification methodologies. The proposed Knowledge-infused Learning Framework has achieved an accuracy of 89.36% (without augmentation), 93.38% (with augmentation), and a standard deviation of 10.64 (without augmentation), and 6.62 (with augmentation). Furthermore, the proposed framework has been tested for various signal-to-noise ratio conditions such as SignaltoNoiseRatio0, SignaltoNoiseRatio3, SignaltoNoiseRatio6, SignaltoNoiseRatio9, SignaltoNoiseRatio12, SignaltoNoiseRatio15, and SignaltoNoiseRatio18. In the end, we have shown a detailed comparison of texture and without texture approaches and have discussed future enhancements and prospective ways for future directions.
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ISSN:2329-924X
2373-7476
DOI:10.1109/TCSS.2022.3151643