Key result
InfusedHeart Framework outperforms other machine learning approaches for classifying heartbeat acoustic events with ~93% accuracy.
Why the study?
A customized balanced dataset and robust classification methods are needed to improve diagnosis of cardiovascular acoustic events from heartbeat sound signals.
Does the InfusedHeart Framework improve classification accuracy of heartbeat acoustic events compared to standard machine learning models?
Does the InfusedHeart Framework improve classification accuracy of heartbeat acoustic events compared to standard machine learning models?
The novel InfusedHeart deep learning framework demonstrates high accuracy in classifying heartbeat acoustic events, potentially aiding in automated cardiovascular diagnosis.
May aid automated cardiac diagnosis; leaves open prospective clinical validation before practice adoption.
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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Pandya et al. (2022) studied Cardiovascular events (heartbeat acoustic events) (n=1,950). InfusedHeart Framework vs. RNN, LSTM, Bi-LSTM, CNN, K-means Clustering, and SVM was evaluated on Classification accuracy. The proposed InfusedHeart Framework outperformed other machine learning approaches for classifying heartbeat acoustic events, achieving an accuracy of 93.38% with data augmentation.
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