A classification method using deep belief network for phonocardiogram signal classification

Faturrahman, Moh.; Wasito, Ito; Ghaisani, Fakhirah D.; Mufidah, Ratna

doi:10.1109/icacsis.2017.8355047

Cited by 3 publications

(2 citation statements)

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“…Finally, SVM was applied for the classification process on public datasets provided by PASCAL Classifying Heart Sounds Challenge 2011. The result metrics used to represent the classification model's result were precision for normal heart sounds (P n ), precision for murmurs (P m ), precision for artefacts (P artifact ), precision for extra heart sounds (P extra ), specificity, and sensitivity as shown in Table 1 (Faturrahman et al, 2017). Since the spectrograms contain redundant data, the researchers applied PCA for dimension reduction and then used deep belief network (DBN) for feature extraction, followed by the application of SVM algorithm for classification.…”

Section: Spectrogrammentioning

confidence: 99%

“…The result metrics used to represent the classification model's result were precision for normal heart sounds ( P n ), precision for murmurs ( P m ), precision for artefacts ( P artifact ), precision for extra heart sounds ( P extra ), specificity, and sensitivity as shown in Table 1. In their research work Faturrahman et al applied the Shannon energy envelope (SEE) technique for segmentation of PCG signals, and further extracted spectrogram by applying the STFT algorithm (Faturrahman et al, 2017). Since the spectrograms contain redundant data, the researchers applied PCA for dimension reduction and then used deep belief network (DBN) for feature extraction, followed by the application of SVM algorithm for classification.…”

Section: Literature Reviewmentioning

confidence: 99%

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Classifying the heart sound signals using textural‐based features for an efficient decision support system

2023

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Cardiovascular diseases have surpassed cancer as the leading cause of death on the planet today. Numerous decision‐making systems with computer‐assisted support have been developed to assist cardiologists to detect heart disease, and thus, lowering the mortality rate. The purpose of this research is to classify audio signals received from the heart as normal or abnormal. The PhysioNet Computing in Cardiology (CinC) 2016 benchmark dataset, popularly known as PhysioNet 2016, has been used to validate the proposed methodology presented here. PhysioNet 2016 contains a total of 3200 phonocardiogram (PCG) recordings divided into sub‐datasets A‐F. The state‐of‐the‐art studies conducted till date have not considered the harmonic details of the beat that can be extracted from its equivalent chromagram image. In this work, textural features such as linear binary pattern (LBP), adaptive‐LBP, and ring‐LBP have been extracted from the existing spectrogram and combined with the features extracted from the chromagram. It has been observed that the combination of features extracted from both the image variants has resulted in a greater accuracy as compared to the scenario where researchers were using only the spectrogram. The experiment yielded the mean accuracy, precision, and F1‐score as 94.87, 93.11, and 95.273, respectively. The heart sound classification models employ spectrogram, scalogram, and mel‐spectrogram images to view and analyse the acoustic properties of a PCG signal. Although these visual tools provide useful information about the signal, yet they are unable to distinguish between pitch and resonance in heart sound generation. However, this paper proposes an alternative approach of heart sound signal representation that allows for a more precise measure of pitch‐related changes in the heart sound. Its results highlight the significance of extracting textural features from time‐chroma representation (i.e., chromagram) of PCG signals that have not been explored yet in the domain related to classification of heart sound signals as normal and abnormal.

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Section: Spectrogrammentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%