Pulmonary Acoustic Signal Classification Using Autoregressive Coefficients and k-Nearest Neighbor

Palaniappan, Rajkumar; Sundaraj, Kenneth; Sundaraj, Sebastian; Huliraj, N; Revadi, S.S.; Archana, B.

doi:10.4028/www.scientific.net/amm.591.211

Cited by 11 publications

(3 citation statements)

References 9 publications

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“…The regressive parameters of the AR model are AR coefficients [26]. AR modelling is a prominent feature extraction technique in signal processing with various applications such as speech signal processing, heart sound processing, and lung sound processing [27]. Hammon et al [7] have used this feature along with Haar wavelet decomposition coefficients and spectral power estimates to classify the BCI3 dataset.…”

Section: Feature Extractionmentioning

confidence: 99%

Feature Engineering for an Efficient Motor Related EcoG BCI System

Jain

Jaiman

Baths

2023

Preprint

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Invasive Brain Computer Interface (BCI) systems through Electrocorticographic (ECoG) signals require efficient recognition of spatio-temporal patterns from a multi electrodes sensor array. Such signals are excellent candidates for automated pattern recognition through machine learning algorithms. However, the available data is limited due to the operative procedure required for such dataset creation. The importance of different temporal signatures and individual electrodes can be analyzed through feature extraction techniques. But, the variability of the signal due to non-stationarity is ignored while extracting features and which features to use can be challenging to figure out by visual inspection. In this study, we introduce the signal split parameter to account for the variability of the signal, and we use genetic selection, which allows the selection of the optimal combination of features from a pool of 8 different feature sets.

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Section: Feature Extractionmentioning

confidence: 99%

Feature Engineering for an Efficient Motor Related EcoG BCI System

Jain

Jaiman

Baths

2023

Preprint

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“…The auditory perception based features, which are expected to capture important distinctive characteristics of different lung sounds similar to an expert physician, show considerable lung sound recognition accuracy [6,31,5]. Besides, diseases like ILD which actually represents a group of lung diseases has also been detected by analyzing different features captured from lung sounds [11,37].…”

Section: Introductionmentioning

confidence: 99%

Lung sound classification using local binary pattern

Sengupta,

Sahidullah,

Saha

2017

Preprint

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Lung sounds contain vital information about pulmonary pathology. In this paper, we use short-term spectral characteristics of lung sounds to recognize associated diseases. Motivated by the success of auditory perception based techniques in speech signal classification, we represent time-frequency information of lung sounds using mel-scale warped spectral coefficients, called here as mel-frequency spectral coefficients (MF-SCs). Next, we employ local binary pattern analysis (LBP) to capture texture information of the MFSCs, and the feature vectors are subsequently derived using histogram representation. The proposed features are used with three well-known classifiers in this field: k-nearest neighbor (kNN), artificial neural network (ANN), and support vector machine (SVM). Also, the performance was tested with multiple SVM kernels. We conduct extensive performance evaluation experiments using two databases which include normal and adventitious sounds. Results show that the proposed features with SVM and also with kNN classifier outperform commonly used wavelet-based features as well as our previously investigated mel-frequency cepstral coefficients (MFCCs) based statistical features, specifically in abnormal sound detection. Proposed features also yield better results than morphological features and energy features computed from rational dilation wavelet coefficients. The Bhattacharyya kernel performs considerably better than other kernels. Further, we optimize the configuration of the proposed feature extraction algorithm. Finally, we have applied mRMR (minimum-redundancy maximum-relevancy) based feature selection method to remove redundancy in the feature vector which makes the proposed method computationally more efficient without any degradation in the performance. The overall performance gain is up to 24.5% as compared to the standard wavelet feature based system.

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“…(ANN) and support vector machine (SVM) classifiers have been used for classifying normal and asthmatic subjects with, spectral subband was extracted from the lung sound cycle, with a maximum classification accuracy of 89.2% and 93.3% by the ANN and SVM classifiers, respectively, Mondal, A [3] et al apply the empirical mode decomposition to lung sounds focused on pattern recognition algorithms for classifying into pulmonary dysfunction with an accuracy of 94.16 %, in [4] A. Rizal et al classify the lung sounds using Tsallis Entropy and using MLP classifier with an accuracy 95.35%, Pancaldi, F et al [5] diagnosis the lung diseases (interstitial lung diseases) by using empirical observation as proposed solution with an overall accuracy of 90.0%, A.Cheema, M.Singh [6] use an EMD method for detect Psychological stress from phonocardiography signal the average accuracy of 93.14% to classifying stressed and non-stressed, in [7] R. Palaniappan classify a pulmonary signal using Autoregressive Coefficients and k-Nearest Neighbor as a classifier with an accuracy of 95.18%. In this work we analyzed a breath sounds signals using empirical mode decomposition with Hjorth descriptors (Activity) and Permutation entropy as features, were extracted from each IMFs produced by EMD, finally,a comparative study has been assessed between an extreme learning machine (ELM) and K-nearest neighbor (K-NN) for distinguishing between normal, and adventitious respiratory sounds.…”

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confidence: 99%

ELM and K-nn machine learning in classification of breath sounds signals

Neili

Fezari

Redjati

2020

IJECE

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The acquisition of Breath sounds (BS) signals from a human respiratory system with an electronic stethoscope, provide and offer prominent information which helps the doctors to diagnosis and classification of pulmonary diseases. Unfortunately, this BS signals with other biological signals have a non-stationary nature according to the variation of the lung volume, and this nature makes it difficult to analyze and classify between several diseases. In this study, we were focused on comparing the ability of the extreme learning machine (ELM) and k-nearest neighbour (K-nn) machine learning algorithms in the classification of adventitious and normal breath sounds. To do so, the empirical mode decomposition (EMD) was used in this work to analyze BS, this method is rarely used in the breath sounds analysis. After the EMD decomposition of the signals into Intrinsic Mode Functions (IMFs), the Hjorth descriptors (Activity) and Permutation Entropy (PE) features were extracted from each IMFs and combined for classification stage. The study has found that the combination of features (activity and PE) yielded an accuracy of 90.71%, 95% using ELM and K-nn respectively in binary classification (normal and abnormal breath sounds), and 83.57%, 86.42% in multiclass classification (five classes).

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Pulmonary Acoustic Signal Classification Using Autoregressive Coefficients and k-Nearest Neighbor

Cited by 11 publications

References 9 publications

Feature Engineering for an Efficient Motor Related EcoG BCI System

Feature Engineering for an Efficient Motor Related EcoG BCI System

Lung sound classification using local binary pattern

ELM and K-nn machine learning in classification of breath sounds signals

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