Heart sound feature extraction and classification using autoregressive power spectral density (AR-PSD) and statistics features

Kristomo, Domy; Hidayat, Risanuri; Soesanti, Indah; Kusjani, Adi

doi:10.1063/1.4958525

Cited by 13 publications

(6 citation statements)

References 7 publications

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“…The parameter of the learning rate and the momentum was 0.3 and 0.2, respectively. In the previous study [14], the experiment for the number of hidden neurons 0 to 20 has been done. In this study we continued the experiment for the number of hidden neurons 20-60 as shown in Fig.…”

Section: Resultsmentioning

confidence: 99%

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Syllables sound signal classification using multi-layer perceptron in varying number of hidden-layer and hidden-neuron

2018

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Abstract. The research on signal processing of syllables sound signal is still the challenging tasks, due to non-stationary, speaker-dependent, variable context, and dynamic nature factor of the signal. In the process of classification using multi-layer perceptron (MLP), the process of selecting a suitable parameter of hidden neuron and hidden layer is crucial for the optimal result of classification. This paper presents a speech signal classification method by using MLP with various numbers of hidden-layer and hidden-neuron for classifying the Indonesian Consonant-Vowel (CV) syllables signal. Five feature sets were generated by using Discrete Wavelet Transform (DWT), Renyi Entropy, Autoregressive Power Spectral Density (AR-PSD) and Statistical methods. Each syllable was segmented at a certain length to form a CV unit. The results show that the average recognition of WRPSDS with 1, 2, and 3 hidden layers were 74.17%, 69.17%, and 63.03%, respectively.

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Section: Resultsmentioning

confidence: 99%

“…The next phase was the peak normalization. By applying peak normalization, the signal magnitude variation which is caused by the differences in the recording condition (such as speaker distance and loudness factor) can be avoided [14]. …”

Section: Methodsmentioning

confidence: 99%

Syllables sound signal classification using multi-layer perceptron in varying number of hidden-layer and hidden-neuron

2018

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“…In addition, there are various steps for the PCG signals in time-domain features, including all statistical-based extraction [1]. PCG signals also need feature selection to avoid the outliers data to have more significant results [8].…”

Section: Related Workmentioning

confidence: 99%

The Classification of Heart Murmurs: The Identification of Significant Time Domain Features

et al. 2020

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Phonocardiogram (PCG) is a type of acoustic signal collected from the heartbeat sound. PCG signals collected in the form of wave files and collected type of heart sound with a specific period. The difficulty of the binomial class in supervised machine learning is the steps-by-steps workflow. The consideration and decision make for every part are importantly stated so that misclassification will not occur. For the heart murmurs classification, data extraction has highly cared for it as we might have fault data consisting of outside signals. Before classifying murmurs in binomial, it will involve multiple features for selection that can have a better classification of the heart murmurs. Nevertheless, since classification performance is vital to conclude the results, models are needed to compare the research's output. The main objective of this study is to classify the signal of the murmur via time-domain based EEG signals. In this study, significant time-domain features were identified to determine the best features by using different feature selection methods. It continues with the classification with different models to compete for the highest accuracy as the performance for murmur classification. A set of Michigan Heart Sound and Murmur database (MHSDB) was provided by the University of Michigan Health System with chosen signals listening with the bell of the stethoscope at the apex area by left decubitus posture of the subjects. The PCG signals are pre-processed by segmentation of three seconds, downsampling eight thousand Hz and normalized to -1, +1. Features are extracted into ten features: Root Mean Square, Variance, Standard Deviation, Maximum, Minimum, Median, Skewness, Shape Factor, Kurtosis, and Mean. Two cross-validation methods applied, such as data splitting and k-fold cross-validation, were used to measure this study's data. Chi-Square and ANOVA technique practice to identify the significant features to improve the classification performance. The classification learners are enrolled and compared by Ada Boost, Random Forest (RF) and Support Vector Machine (SVM). The datasets will separate into a ratio of 70:30 and 80:20 for training and testing data, respectively. The chi-Square selection method was the best features selection method and 80:20 data splitting with better performance than the 70:30 ratio. The best classification accuracy for the models significantly come by SVM with all the categories with 100% except 70:30 test on test data with 97.2% only.

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“…Furthermore, they aimed to improve the accuracy of the classification process in support vector machine (SVM) [13], [14]. The implementations of this classification process include feature selection by using Gaussian mixture model [13], automatic speech assessment [15], heart sound features in the frequency and time domain [16], music genre classification [17], [18], and audio-visual recognition [19], [20].…”

Section: Introductionmentioning

confidence: 99%

Forward feature selection for toxic speech classification using support vector machine and random forest

Gumelar

Yogatama

Adi

et al. 2022

IJ-AI

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<span lang="EN-US">This study describes the methods for eliminating irrelevant features in speech data to enhance toxic speech classification accuracy and reduce the complexity of the learning process. Therefore, the wrapper method is introduced to estimate the forward selection technique based on support vector machine (SVM) and random forest (RF) classifier algorithms. Eight main speech features were then extracted with derivatives consisting of 9 statistical sub-features from 72 features in the extraction process. Furthermore, Python is used to implement the classifier algorithm of 2,000 toxic data collected through the world's largest video sharing media, known as YouTube. Conclusively, this experiment shows that after the feature selection process, the classification performance using SVM and RF algorithms increases to an excellent extent. We were able to select 10 speech features out of 72 original feature sets using the forward feature selection method, with 99.5% classification accuracy using RF and 99.2% using SVM.</span>

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Heart sound feature extraction and classification using autoregressive power spectral density (AR-PSD) and statistics features

Cited by 13 publications

References 7 publications

Syllables sound signal classification using multi-layer perceptron in varying number of hidden-layer and hidden-neuron

Syllables sound signal classification using multi-layer perceptron in varying number of hidden-layer and hidden-neuron

The Classification of Heart Murmurs: The Identification of Significant Time Domain Features

Forward feature selection for toxic speech classification using support vector machine and random forest

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