Automatic Multimodal Heart Disease Classification using Phonocardiogram Signal
10 Dec 2020-pp 3514-3521
TL;DR: In this paper, the authors proposed an integrated automatic multimodal heart disease classification (AMHDC) system using the Phonocardiogram (PCG) signal using pre-processing techniques such as Data Normalization and Data Augmentation.
Abstract: Heart diseases are considered the leading cause of death globally. Early diagnosis of disease may help give appropriate prescribing medicines, which may help control and reduce conditions. The current clinical diagnosis methods such as Electrocardiograms, computed tomography, echocardiogram, Magnetic Resonance Imaging, etc. provide valuable information for diagnosis and treatment. However, these techniques are time-intensive, operator-dependent, and expensive. In this paper, we propose a low-cost solution real-time solution to diagnose heart diseases. We suggest an integrated automatic multimodal heart disease classification (AMHDC) system using the Phonocardiogram (PCG) signal. For this purpose, first, we have developed an advanced fusion method using pre-processing techniques such as Data Normalization and Data Augmentation. Secondly, we have extracted the spectrograms from heart sound and used them as features and images for signal and image processing. Finally, we created a real-time integrated Convolutional Neural Network (CNN) model for high-performance heart disease classification. The results show our model outperformed the state-of-art research, whose accuracy is 89.7%., while our model reported accuracy of 93% for audio and 96% for image-based approach.
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3 citations
17 Dec 2022
TL;DR: In this paper , an integrated solution combines signal filtering, variable construction (feature engineering) and multidimensional data summarization, for a tighter and more effective integration of PCA and K-means clustering.
Abstract: We discuss our progress towards solving a challenging biomedical problem: identifying similar patterns among multiple physiological nerve signals hidden in high throughput data, collected from micro electrical sensors implanted in several animal organs. The problem is difficult because patterns come as spikes within millisecond time-windows, data sets have high dimensionality and there is background electrical noise. A previous analytic system discovers patterns combining PCA dimensionality reduction and K-means clustering, which is slow and misses important patterns hidden by noise. Moreover, it requires reading the data set several times and it requires multiple languages and tools. With such limitations in mind, we present an improved, integrated system that effectively allows the discovery of more accurate patterns, with automated algorithm parameter tuning, by learning model parameters incrementally exploiting summarization. Our integrated solution combines signal filtering, variable construction (feature engineering) and multidimensional data summarization, for a tighter and more effective integration of PCA and K-means clustering. We present preliminary experiments on signals collected from key nerves in a rat. We show our method discovers more patterns in larger time-windows, with better noise filtering, taking less time. In the future, we plan to link signal patterns to specific physiological functions, paving the way for innovative medical treatment via nerve stimulation.
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