A telemedicine tool to detect pulmonary pathology using computerized pulmonary acoustic signal analysis

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

doi:10.1016/j.asoc.2015.05.031

Cited by 21 publications

(2 citation statements)

References 23 publications

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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%

Triple-Classification of Respiratory Sounds Using Optimized S-Transform and Deep Residual Networks

et al. 2019

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Digital respiratory sounds provide valuable information for telemedicine and smart diagnosis in an non-invasive way of pathological detection. As the typical continuous abnormal respiratory sound, wheeze is clinically correlated with asthma or chronic obstructive lung diseases. Meanwhile, the discontinuous adventitious crackle is clinically correlated with pneumonia, bronchitis, and so on. The detection and classification of both attract many studies for decades. However, due to the contained artifacts and constrained feature extraction methods, the reliability and accuracy of the classification of wheeze, crackle, and normal sounds need significant improvement. In this paper, we propose a novel method for the identification of wheeze, crackle, and normal sounds using the optimized S-transform (OST) and deep residual networks (ResNets). First, the raw respiratory sound is processed by the proposed OST. Then, the spectrogram of OST is rescaled for the Resnet. After the feature learning and classification are fulfilled by the ResNet, the classes of respiratory sounds are recognized. Because the proposed OST highlights the features of wheeze, crackle, and respiratory sounds, and the deep residual learning generates discriminative features for better recognition, this proposed method provides reliable access for respiratory disease-related telemedicine and E-health diagnosis. The experimental results show that the proposed OST and ResNet is excellent for the multi-classification of respiratory sounds with the accuracy, sensitivity, and specificity up to 98.79%, 96.27%, and 100%, respectively. The comparison results of the triple-classification of respiratory sounds indicate that the proposed method outperforms the deep-learning-based ensembling convolutional neural network (CNN) by 3.23% and the empirical mode decomposition-based artificial neural network (ANN) by 4.63%, respectively. INDEX TERMS Deep residual networks (ResNet), optimized S-transform (OST), respiratory sounds classification, crackle and wheeze detection.

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Section: Introductionmentioning

confidence: 99%

Triple-Classification of Respiratory Sounds Using Optimized S-Transform and Deep Residual Networks

et al. 2019

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“…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%

A Novel Fuzzy Algorithm to Introduce New Variables in the Drug Supply Decision‐Making Process in Medicine

et al. 2018

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One of the main challenges in medicine is to guarantee an appropriate drug supply according to the real needs of patients. Closed-loop strategies have been widely used to develop automatic solutions based on feedback variables. However, when the variable of interest cannot be directly measured or there is a lack of knowledge behind the process, it turns into a difficult issue to solve. In this research, a novel algorithm to approach this problem is presented. The main objective of this study is to provide a new general algorithm capable of determining the influence of a certain clinical variable in the decision making process for drug supply and then defining an automatic system able to guide the process considering this information. Thus, this new technique will provide a way to validate a given physiological signal as a feedback variable for drug titration. In addition, the result of the algorithm in terms of fuzzy rules and membership functions will define a fuzzy-based decision system for the drug delivery process. The method proposed is based on a Fuzzy Inference System whose structure is obtained through a decision tree algorithm. A four-step methodology is then developed: data collection, preprocessing, Fuzzy Inference System generation, and the validation of results. To test this methodology, the analgesia control scenario was analysed. Specifically, the viability of the Analgesia Nociception Index (ANI) as a guiding variable for the analgesic process during surgical interventions was studied. Real data was obtained from fifteen patients undergoing cholecystectomy surgery.

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Future Prospects for Respiratory Sound Research

Marques

Jácome

2018

Breath Sounds

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A telemedicine tool to detect pulmonary pathology using computerized pulmonary acoustic signal analysis

Cited by 21 publications

References 23 publications

Triple-Classification of Respiratory Sounds Using Optimized S-Transform and Deep Residual Networks

Triple-Classification of Respiratory Sounds Using Optimized S-Transform and Deep Residual Networks

A Novel Fuzzy Algorithm to Introduce New Variables in the Drug Supply Decision‐Making Process in Medicine

Future Prospects for Respiratory Sound Research

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