TY - JOUR
T1 - InfusedHeart
T2 - A Novel Knowledge-Infused Learning Framework for Diagnosis of Cardiovascular Events
AU - Pandya, Sharnil
AU - Gadekallu, Thippa Reddy
AU - Reddy, Praveen Kumar
AU - Wang, Weizheng
AU - Alazab, Mamoun
PY - 2022/3/2
Y1 - 2022/3/2
N2 - 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.
AB - 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.
KW - Acoustic classification
KW - Acoustics
KW - automated knowledge extraction
KW - Cardiac arrest
KW - cardiovascular events
KW - Convolutional neural networks
KW - deep learning
KW - diagnosis
KW - Heart beat
KW - knowledge-infused learning framework.
KW - Monitoring
KW - Stethoscope
KW - Support vector machines
UR - http://www.scopus.com/inward/record.url?scp=85125735311&partnerID=8YFLogxK
U2 - 10.1109/TCSS.2022.3151643
DO - 10.1109/TCSS.2022.3151643
M3 - Article
AN - SCOPUS:85125735311
SP - 1
EP - 10
JO - IEEE Transactions on Computational Social Systems
JF - IEEE Transactions on Computational Social Systems
SN - 2329-924X
ER -