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Respiratory sounds of pathological and healthy subjects were analyzed via autoregressive (AR) models with a view to construct a diagnostic aid based on auscultation. Using the AR vectors, two reference libraries, pathological and healthy, were built. Two classifiers, k-nearest neighbour (k-NN) classifier and a quadratic classifier, were designed and(More)
The aim of this study is the classification of wheeze and non-wheeze epochs within respiratory sound signals acquired from patients with asthma and COPD. Since a wheeze signal, having a sinusoidal waveform, has a different behavior in time and frequency domains from that of a non-wheeze signal, the features selected for classification are kurtosis, Renyi(More)
Functional near-infrared spectroscopy (fNIRS) is an emerging technique for monitoring the concentration changes of oxy- and deoxy-hemoglobin (oxy-Hb and deoxy-Hb) in the brain. An important consideration in fNIRS-based neuroimaging modality is to conduct group-level analysis from a set of time series measured from a group of subjects. We investigate the(More)
The objective of this study is to probe the existence of a third crackle type, medium, besides the traditionally accepted types, namely, fine and coarse crackles and, furthermore, to explore the representative parameter values for each crackle type. A set of clustering experiments have been conducted on pulmonary crackles to this end. A model-based(More)
A method is proposed for the detection of transients in biological signals. The method is based on enhancing the transient-to-background ratio by a series of operations such as background whitening, wavelet-based multiresolution decomposition and application of Teager's energy operator. The transients are extracted by judiciously thresholding this processed(More)
a r t i c l e i n f o a b s t r a c t Keywords: Lung sounds Crackle detection Time–frequency and time–scale analysis Dual-tree complex wavelet transform Denoising Ensemble methods Support vector machines Pulmonary crackles are used as indicators for the diagnosis of different pulmonary disorders in auscultation. Crackles are very common adventitious(More)
In this study, different feature sets are used in conjunction with (k-nearest neighbors) k-NN and artificial neural network (ANN) classifiers to address the classification problem of respiratory sound signals. A comparison is made between the performances of k-NN and ANN classifiers with different feature sets derived from respiratory sound data acquired(More)
Functional near infrared spectroscopy (fNIRS) is a technique that tries to detect cognitive activity by measuring changes in the concentrations of the oxygenated and deoxygenated hemoglobin in the brain. We develop Bayesian statistical tools for making multilevel inferences, that is, inferences generalizable to a population within the context of fNIRS(More)
In this study, wavelet networks have been used to parameterize and quantify pulmonary crackles with an aim to depict the waveform with a small set of meaningful parameters. Complex Morlet wavelets are used at the nodes of both single and double-node networks to model the waveforms with the double-node rendering smaller modeling error. The features extracted(More)
Pulmonary crackles and their parameters are very useful in the diagnosis of pulmonary disorders. A new automatic method has been proposed for the elimination of background vesicular sound from crackle signal with a view to introduce minimum distortion to crackle parameters. A region of interest is designated and a distortion metric based on the correlation(More)