2015
DOI: 10.1016/j.asoc.2015.05.031
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A telemedicine tool to detect pulmonary pathology using computerized pulmonary acoustic signal analysis

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Cited by 22 publications
(2 citation statements)
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“…In pre-processing stage, the artifacts including con hearts sounds, background noises and contact interference are removed by filters, such as the band-pass filter of Butterworth [4] or adaptive filters [5]. Next, feature extraction is performed mainly based on spectral features [6], [7], eigen value of singular spectrum analysis (SSA) [8], Mel-frequency cepstral coefficients (MFCCs) [9], ensemble empirical mode decomposition [10], wavelet based musical features [11], statistical features of S-transform [12], local binary patterns (LBP) features [13], energy envelope [14], entropy-based features [15] or combination features among the above mentioned methods. In the last classification stage, conventional methods is based on the empirical threshold [16], [17].…”
Section: Introductionmentioning
confidence: 99%
“…In pre-processing stage, the artifacts including con hearts sounds, background noises and contact interference are removed by filters, such as the band-pass filter of Butterworth [4] or adaptive filters [5]. Next, feature extraction is performed mainly based on spectral features [6], [7], eigen value of singular spectrum analysis (SSA) [8], Mel-frequency cepstral coefficients (MFCCs) [9], ensemble empirical mode decomposition [10], wavelet based musical features [11], statistical features of S-transform [12], local binary patterns (LBP) features [13], energy envelope [14], entropy-based features [15] or combination features among the above mentioned methods. In the last classification stage, conventional methods is based on the empirical threshold [16], [17].…”
Section: Introductionmentioning
confidence: 99%
“…Decision-making process in hospital management for prioritization of risks and assessment of failures has been also approached [9,10]. Moreover, AI has been used to automatic diagnosis and classification of illness [11,12] and also for medical sensors fault detection [13]. Specifically, in medicine, the classifiers proposed to support a decisionmaking process must be suitable for being understood and 2 Complexity evaluated from a clinician point of view [14].…”
Section: Introductionmentioning
confidence: 99%