Normal and Murmur Heart Sound Classification Using Linear Predictive Coding and k-Nearest Neighbor Methods

Sofwan, Aghus; Santoso, Imam; Pradipta, Himawan; Arfan, Muhammad; M, Ajub Ajulian Zahra

doi:10.1109/icicos48119.2019.8982393

Cited by 7 publications

(2 citation statements)

References 5 publications

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“…Furthermore, k-nearest neighbours (KNNs) has shown good performance on heart sound classification with the idea of classifying a data sample according to the classes of its knearest neighbours [40], [41], [43], [46], [60], [75], [85], [90], [91], [93], [103], [104]. Also, decision trees were successfully used for heart sound classification [7], [40], [41], [75], [88], [99], [103].…”

Section: Dominant Frequency Valuementioning

confidence: 99%

A Comprehensive Survey on Heart Sound Analysis in the Deep Learning Era

Zhao¹,

Chen²,

Nguyên³

et al. 2023

Preprint

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Heart sound auscultation has been demonstrated to be beneficial in clinical usage for early screening of cardiovascular diseases. Due to the high requirement of well-trained professionals for auscultation, automatic auscultation benefiting from signal processing and machine learning can help auxiliary diagnosis and reduce the burdens of training professional clinicians. Nevertheless, classic machine learning is limited to performance improvement in the era of big data. Deep learning has achieved better performance than classic machine learning in many research fields, as it employs more complex model architectures with stronger capability of extracting effective representations. Deep learning has been successfully applied to heart sound analysis in the past years. As most review works about heart sound analysis were given before 2017, the present survey is the first to work on a comprehensive overview to summarise papers on heart sound analysis with deep learning in the past six years 2017-2022. We introduce both classic machine learning and deep learning for comparison, and further offer insights about the advances and future research directions in deep learning for heart sound analysis.

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Section: Dominant Frequency Valuementioning

confidence: 99%

A Comprehensive Survey on Heart Sound Analysis in the Deep Learning Era

Zhao¹,

Chen²,

Nguyên³

et al. 2023

Preprint

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“…The classifier takes the extracted features and interprets them by extracting and recognizing the functional patterns to efficiently represent the murmurs associated with diseases of a PCG signal. In the state-of-the-art, the reported classification schemes used are Support Vector Machines [8] [16] [23][24][25] [26][27] [28] [29] , k-Nearest Neighbors [16][30] [31][32] [33] [34]…”

Section: Review Of Pgcs Classification Schemesmentioning

confidence: 99%

Comparison of Spectral and Sparse Feature Extraction Methods for Heart Sounds Classification

Ibarra Hernández,

Alonso-Arévalo,

García-Canseco

2023

Rev. Mex. Ing. Biomed.

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Cardiovascular diseases (CVDs) remain the leading cause of morbidity worldwide. The heart sound signal or phonocardiogram (PCG) is the most simple, low-cost, and effective tool to assist physicians in diagnosing CVDs. Advances in signal processing and machine learning have motivated the design of computer-aided systems for heart illness detection based only on the PCG. The objective of this work is to compare the effects of using spectral and sparse features for a classification scheme to detect the presence/absence of a pathological state in a heart sound signal, more specifically, sparse representations using Matching Pursuit with multiscale Gabor time-frequency dictionaries, linear prediction coding, and Mel-frequency cepstral coefficients. This work compares the performance of PCGs classification applying features as a result of averaging the samples or the features for each PCG sound event when feeding a random forest (RF) classifier. For data balancing, random under-sampling and synthetic minority oversampling (SMOTE) methods were applied. Furthermore, we compare the Correlation Feature Selection (CFS) and Information Gain (IG) for the dimensionality reduction. The findings show a SE=93.17 %, SP=84.32 % and ACC=85.9 % when joining MP+LPC+MFCC features set with an AUC=0.969 showing that these features are promising to be used in heart sounds anomaly detection schemes.

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