Lung and Heart Sounds Analysis: State-of-the-Art and Future Trends.
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[1] Samuel Wilks,et al. THE EVOLUTION OF THE STETHOSCOPE , 1907 .
[2] L. Feigen,et al. Physical characteristics of sound and hearing. , 1971, The American journal of cardiology.
[3] M. Tavel,et al. Spectral analysis of heart sounds: relationships between some physical characteristics and frequency spectra of first and second heart sounds in normals and hypertensives. , 1984, Journal of biomedical engineering.
[4] D. Penney,et al. Comparison of the acoustic properties of six popular stethoscopes. , 1992, The Journal of the Acoustical Society of America.
[5] H. Pasterkamp,et al. Respiratory sounds. Advances beyond the stethoscope. , 1997, American journal of respiratory and critical care medicine.
[6] R. Adolph,et al. In defense of the stethoscope. , 1998, Chest.
[7] H. Melbye,et al. [Auscultation of the lungs--still a useful examination?]. , 2001, Tidsskrift for den Norske laegeforening : tidsskrift for praktisk medicin, ny raekke.
[8] Zhongwei Jiang,et al. A cardiac sound characteristic waveform method for in-home heart disorder monitoring with electric stethoscope , 2006, Expert Syst. Appl..
[9] M. Tavel,et al. Cardiac Auscultation: A Glorious Past—And It Does Have a Future! , 2006, Circulation.
[10] M. Mussell,et al. The need for standards in recording and analysing respiratory sounds , 1992, Medical and Biological Engineering and Computing.
[11] E. Andrès,et al. Analysis of Respiratory Sounds: State of the Art , 2008, Clinical medicine. Circulatory, respiratory and pulmonary medicine.
[12] Rachel Caissie,et al. New auditory training program rapidly teaches students to distinguish innocent and pathological murmurs with 90% accuracy , 2010 .
[13] Zhongwei Jiang,et al. Cardiac sound murmurs classification with autoregressive spectral analysis and multi-support vector machine technique , 2010, Comput. Biol. Medicine.
[14] Zhongwei Jiang,et al. The moment segmentation analysis of heart sound pattern , 2010, Comput. Methods Programs Biomed..
[15] J. Tielsch,et al. Computerized lung sound analysis as diagnostic aid for the detection of abnormal lung sounds: a systematic review and meta-analysis. , 2011, Respiratory medicine.
[16] Harun Uguz,et al. Adaptive neuro-fuzzy inference system for diagnosis of the heart valve diseases using wavelet transform with entropy , 2011, Neural Computing and Applications.
[17] D. Low,et al. A randomised control trial to determine if use of the iResus©application on a smart phone improves the performance of an advanced life support provider in a simulated medical emergency * , 2011, Anaesthesia.
[18] Sridhar Krishnan,et al. Adventitious Sounds Identification and Extraction Using Temporal–Spectral Dominance-Based Features , 2011, IEEE Transactions on Biomedical Engineering.
[19] Ki H. Chon,et al. Physiological Parameter Monitoring from Optical Recordings With a Mobile Phone , 2012, IEEE Transactions on Biomedical Engineering.
[20] Ridvan Saraçoglu,et al. Hidden Markov model-based classification of heart valve disease with PCA for dimension reduction , 2012, Eng. Appl. Artif. Intell..
[21] H. Naseri,et al. Detection and Boundary Identification of Phonocardiogram Sounds Using an Expert Frequency-Energy Based Metric , 2012, Annals of Biomedical Engineering.
[22] Gunnar Hartvigsen,et al. Mobile Health Applications to Assist Patients with Diabetes: Lessons Learned and Design Implications , 2012, Journal of diabetes science and technology.
[23] B. J. Visser,et al. Medical apps for smartphones: lack of evidence undermines quality and safety , 2012, Evidence-Based Medicine.
[24] Sridhar Krishnan,et al. Signal feature extraction by multi-scale PCA and its application to respiratory sound classification , 2012, Medical & Biological Engineering & Computing.
[25] Mekki Ksouri,et al. A new scheme for automatic classification of pathologic lung sounds , 2012 .
[26] Ting Li,et al. Segmentation of heart sounds based on dynamic clustering , 2012, Biomed. Signal Process. Control..
[27] N. P. Jawarkar,et al. Cell Phone Based Remote Early Detection of Respiratory Disorders for Rural Children Using Modified Stethoscope , 2012, 2012 International Conference on Communication Systems and Network Technologies.
[28] João Dinis,et al. LungSounds@UA Interface and Multimedia Database , 2012 .
[29] Wiesława Kuniszyk-Jóźkowiak,et al. Analysis of lung auscultatory phenomena using the Wigner-Ville Distribution , 2012 .
[30] J. Sanderson,et al. Acoustic cardiography helps to identify heart failure and its phenotypes. , 2013, International journal of cardiology.
[31] Chun Yu,et al. Soft Stethoscope for Detecting Asthma Wheeze in Young Children , 2013, Sensors.
[32] Nizamettin Aydin,et al. Pulmonary crackle detection using time-frequency and time-scale analysis , 2013, Digit. Signal Process..
[33] John L. Semmlow,et al. Path length entropy analysis of diastolic heart sounds , 2013, Comput. Biol. Medicine.
[34] Christian Brandt,et al. A robust heart sounds segmentation module based on S-transform , 2013, Biomed. Signal Process. Control..
[35] Miguel Tavares Coimbra,et al. Heart sound segmentation of pediatric auscultations using wavelet analysis , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
[36] Daniel Sánchez Morillo,et al. Computerized analysis of respiratory sounds during COPD exacerbations , 2013, Comput. Biol. Medicine.
[37] Bernadette A. Thomas,et al. Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010 , 2012, The Lancet.
[38] Ki H. Chon,et al. Respiratory Rate Estimation from the Built-in Cameras of Smartphones and Tablets , 2013, Annals of Biomedical Engineering.
[39] Kenneth Sundaraj,et al. A comparative study of the svm and k-nn machine learning algorithms for the diagnosis of respiratory pathologies using pulmonary acoustic signals , 2014, BMC Bioinformatics.
[40] Ram Bilas Pachori,et al. Classification of cardiac sound signals using constrained tunable-Q wavelet transform , 2014, Expert Syst. Appl..
[41] Chengli Que,et al. Identification of Velcro rales based on Hilbert-Huang transform , 2014 .
[42] Ki H. Chon,et al. Tracheal Sounds Acquisition Using Smartphones , 2014, Sensors.
[43] Hamid Krim,et al. Persistent Homology of Delay Embeddings and its Application to Wheeze Detection , 2014, IEEE Signal Processing Letters.
[44] Tülay Yildirim,et al. Classification of normal and abnormal lung sounds using wavelet coefficients , 2014, 2014 22nd Signal Processing and Communications Applications Conference (SIU).
[45] K. I. Ramachandran,et al. A novel heart sound activity detection framework for automated heart sound analysis , 2014, Biomed. Signal Process. Control..
[46] Bor-Shing Lin,et al. An FPGA-Based Rapid Wheezing Detection System , 2014, International journal of environmental research and public health.
[47] Yi-Fei Pu,et al. Identification of the normal and abnormal heart sounds using wavelet-time entropy features based on OMS-WPD , 2014, Future Gener. Comput. Syst..
[48] C. L. Ventola. Mobile devices and apps for health care professionals: uses and benefits. , 2014, P & T : a peer-reviewed journal for formulary management.
[49] Goutam Saha,et al. Detection of Lungs Status Using Morphological Complexities of Respiratory Sounds , 2014, TheScientificWorldJournal.
[50] Alvaro D. Orjuela-Cañón,et al. Artificial Neural Networks for Acoustic Lung Signals Classification , 2014, CIARP.
[51] Semra Içer,et al. Classification and analysis of non-stationary characteristics of crackle and rhonchus lung adventitious sounds , 2014, Digit. Signal Process..
[52] Feng Jin,et al. New approaches for spectro-temporal feature extraction with applications to respiratory sound classification , 2014, Neurocomputing.
[53] Juing-Shian Chiou,et al. Performance Evaluation of Heart Sound Cancellation in FPGA Hardware Implementation for Electronic Stethoscope , 2014, TheScientificWorldJournal.
[54] Zhongwei Jiang,et al. Automatic moment segmentation and peak detection analysis of heart sound pattern via short-time modified Hilbert transform , 2014, Comput. Methods Programs Biomed..
[55] Viju Raghupathi,et al. Big data analytics in healthcare: promise and potential , 2014, Health Information Science and Systems.
[56] Wen-Ying Zhang,et al. HEART SOUND CLASSIFICATION AND RECOGNITION BASED ON EEMD AND CORRELATION DIMENSION , 2014 .
[57] Mandeep Singh,et al. Classification of Heart Sound Signals Using Multi-modal Features☆ , 2015 .
[58] Yineng Zheng,et al. A novel hybrid energy fraction and entropy-based approach for systolic heart murmurs identification , 2015, Expert Syst. Appl..
[59] Murat Saraclar,et al. A Comparison of SVM and GMM-Based Classifier Configurations for Diagnostic Classification of Pulmonary Sounds , 2015, IEEE Transactions on Biomedical Engineering.
[60] Ioanna Chouvarda,et al. Detection of wheezes using their signature in the spectrogram space and musical features , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
[61] Cristina Jácome,et al. Automatic Crackle Detection Algorithm Based on Fractal Dimension and Box Filtering , 2015, CENTERIS/ProjMAN/HCist.
[62] Luis M. T. Jesus,et al. Computerised Lung Auscultation - Sound Software (CLASS) , 2015, CENTERIS/ProjMAN/HCist.
[63] Hong Zhao,et al. The detection of crackles based on mathematical morphology in spectrogram analysis. , 2015, Technology and health care : official journal of the European Society for Engineering and Medicine.
[64] Mirjana Bonkovic,et al. Two-level coarse-to-fine classification algorithm for asthma wheezing recognition in children's respiratory sounds , 2015, Biomed. Signal Process. Control..
[65] Rami J Oweis,et al. An alternative respiratory sounds classification system utilizing artificial neural networks , 2015, Biomedical journal.
[66] P. Ask,et al. Assessment of aortic valve stenosis severity using intelligent phonocardiography. , 2015, International journal of cardiology.
[67] Tomasz P. Zielinski,et al. Joint Application of Audio Spectral Envelope and Tonality Index in an E-Asthma Monitoring System , 2015, IEEE Journal of Biomedical and Health Informatics.
[68] Xi Liu,et al. Detection of adventitious lung sounds using entropy features and a 2-D threshold setting , 2015, 2015 10th International Conference on Information, Communications and Signal Processing (ICICS).
[69] Patrice Flaud,et al. Wheezing recognition algorithm using recordings of respiratory sounds at the mouth in a pediatric population , 2016, Comput. Biol. Medicine.
[70] Bruno Henrique Groenner Barbosa,et al. Classification of lung sounds using higher-order statistics: A divide-and-conquer approach , 2016, Comput. Methods Programs Biomed..
[71] Shi-Wen Deng,et al. Towards heart sound classification without segmentation via autocorrelation feature and diffusion maps , 2016, Future Gener. Comput. Syst..
[72] José Antonio Fiz,et al. Automatic Differentiation of Normal and Continuous Adventitious Respiratory Sounds Using Ensemble Empirical Mode Decomposition and Instantaneous Frequency , 2016, IEEE Journal of Biomedical and Health Informatics.
[73] K. V. Ahammed Muneer,et al. The Heart Defect Analysis Based on PCG Signals Using Pattern Recognition Techniques , 2016 .
[74] Goutam Saha,et al. Lung sound classification using cepstral-based statistical features , 2016, Comput. Biol. Medicine.
[75] Lionel Tarassenko,et al. Logistic Regression-HSMM-Based Heart Sound Segmentation , 2016, IEEE Transactions on Biomedical Engineering.
[76] K. Chon,et al. Monitoring of Heart and Breathing Rates Using Dual Cameras on a Smartphone , 2016, PloS one.
[77] T. Shimoda,et al. Lung sound analysis helps localize airway inflammation in patients with bronchial asthma , 2017, Journal of asthma and allergy.
[78] Achmad Rizal,et al. Lung Sound Classification Using Empirical Mode Decomposition and the Hjorth Descriptor , 2017 .
[79] G. Szalai,et al. The use of a smartphone application for fast lung cancer risk assessment† , 2017, European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery.