Heart Sound Data Acquisition and Preprocessing Techniques

Heart sound or phonocardiogram (PCG) signal quantifies the information about the mechanical activity of the heart, and the medical practitioners use the stethoscope to listen to this sound. The PCG signal can be used for clinical applications such as detection of various valvular diseases and non-clinical applications such as biometric system, stress and emotion detection, etc. The PCG signal acquisition and preprocessing are important tasks for the diagnosis of heart valve-related disorders and other applications. The heart sound preprocessing techniques include denoising of PCG signal, segmentation of first and second heart sound (S1, S2) and other heart sound components from the PCG signal, feature extraction from the segmented heart sound components, followed by classification. This chapter reviews the state-of-the-art approaches for heart sound acquisition and pre-processing techniques and also provides the information that is commonly used by the researchers for the validation of their PCG signal processing algorithms. Heart Sound Data Acquisition and Preprocessing Techniques: A Review

[1]  Aggelos K. Katsaggelos,et al.  Heart sound anomaly and quality detection using ensemble of neural networks without segmentation , 2016, 2016 Computing in Cardiology Conference (CinC).

[3]  John L. Semmlow,et al.  Robust detection of background noise in phonocardiograms , 2011, 2011 1st Middle East Conference on Biomedical Engineering.

[5]  P. Lancellotti,et al.  Mitral regurgitation in patients with aortic stenosis undergoing valve replacement , 2009, Heart.

[6]  Shubhajit Roy Chowdhury,et al.  Heart Sound Sensing Through MEMS Microphone , 2017 .

[7]  Tan Tian Swee,et al.  Acoustic cardiac signals analysis: a Kalman filter–based approach , 2012, International journal of nanomedicine.

[8]  Miguel Tavares Coimbra,et al.  A comparative study of electronic stethoscopes for cardiac auscultation , 2017, 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[9]  G. Castellanos-Dominguez,et al.  Selection of Dynamic Features Based on Time–Frequency Representations for Heart Murmur Detection from Phonocardiographic Signals , 2009, Annals of Biomedical Engineering.

[10]  Zhongwei Jiang,et al.  A cardiac sound characteristic waveform method for in-home heart disorder monitoring with electric stethoscope , 2006, Expert Syst. Appl..

[11]  Ping Wang,et al.  Phonocardiographic Signal Analysis Method Using a Modified Hidden Markov Model , 2007, Annals of Biomedical Engineering.

[12]  Ting Li,et al.  Segmentation of heart sounds based on dynamic clustering , 2012, Biomed. Signal Process. Control..

[13]  G. Castellanos-Dominguez,et al.  Digital Auscultation Analysis for Heart Murmur Detection , 2009, Annals of Biomedical Engineering.

[14]  Hironori Yamauchi,et al.  Signal and noise separation in medical diagnostic system based on independent component analysis , 2010, 2010 IEEE Asia Pacific Conference on Circuits and Systems.

[15]  S. Sanei,et al.  An adaptive singular spectrum analysis approach to murmur detection from heart sounds. , 2011, Medical engineering & physics.

[16]  K. I. Ramachandran,et al.  Effective Heart Sound Segmentation and Murmur Classification Using Empirical Wavelet Transform and Instantaneous Phase for Electronic Stethoscope , 2017, IEEE Sensors Journal.

[17]  N. Jatupaiboon,et al.  Electronic stethoscope prototype with adaptive noise cancellation , 2010, 2010 Eighth International Conference on ICT and Knowledge Engineering.

[18]  P. Wang,et al.  First Heart Sound Detection for Phonocardiogram Segmentation , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[19]  Juan Ignacio Godino-Llorente,et al.  Feature Extraction From Parametric Time–Frequency Representations for Heart Murmur Detection , 2010, Annals of Biomedical Engineering.

[20]  P. Pibarot,et al.  Pathophysiology and management of multivalvular disease , 2016, Nature Reviews Cardiology.

[21]  V Nigam,et al.  A simplicity-based fuzzy clustering approach for detection and extraction of murmurs from the phonocardiogram , 2008, Physiological measurement.

[22]  Thierry Dutoit,et al.  A novel method for pediatric heart sound segmentation without using the ECG , 2010, Comput. Methods Programs Biomed..

[23]  Tamer Ölmez,et al.  Heart sound classification using wavelet transform and incremental self-organizing map , 2008, Digit. Signal Process..

[24]  Ali Ghaffari,et al.  Home Search Collections Journals About Contact us My IOPscience Detection of pathological heart sounds , 2017 .

[25]  Derek Abbott,et al.  Optimal wavelet denoising for phonocardiograms , 2001 .

[26]  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.

[27]  Abdulkadir Sengur,et al.  A hybrid method based on artificial immune system and fuzzy k-NN algorithm for diagnosis of heart valve diseases , 2008 .

[28]  V. Fuster,et al.  Insights into degenerative aortic valve disease. , 2007, Journal of the American College of Cardiology.

[29]  Ashok Mondal,et al.  An Irregularity Measurement Based Cardiac Status Recognition Using Support Vector Machine , 2015, Journal of medical engineering.

[30]  Joyanta Kumar Roy,et al.  A simple technique for heart sound detection and real time analysis , 2017, 2017 Eleventh International Conference on Sensing Technology (ICST).

[31]  Messaoud Benidir,et al.  The Empirical Mode Decomposition and the SVD for Abnormal Heart Sound Signals detection and Time-Frequency Analysis. , 2015 .

[32]  Raymond L. Watrous,et al.  Detection of the first heart sound using a time-delay neural network , 2002, Computers in Cardiology.

[33]  Yineng Zheng,et al.  A novel hybrid energy fraction and entropy-based approach for systolic heart murmurs identification , 2015, Expert Syst. Appl..

[34]  Oh-Wook Kwon,et al.  Cardiac disorder classification by heart sound signals using murmur likelihood and hidden Markov model state likelihood , 2012, IET Signal Process..

[35]  Epaminondas Panas,et al.  Functional Form, Elasticity and Lexical Richness: Estimates and Implications , 2012 .

[36]  Jian Qin,et al.  Computer-assisted diagnosis for chronic heart failure by the analysis of their cardiac reserve and heart sound characteristics , 2015, Comput. Methods Programs Biomed..

[37]  Shuping Sun.  An innovative intelligent system based on automatic diagnostic feature extraction for diagnosing heart diseases , 2015, Knowl. Based Syst..

[38]  ChingShun Lin,et al.  Blind source separation of heart and lung sounds based on nonnegative matrix factorization , 2013, 2013 International Symposium on Intelligent Signal Processing and Communication Systems.

[39]  Ram Bilas Pachori,et al.  Automatic diagnosis of septal defects based on tunable-Q wavelet transform of cardiac sound signals , 2015, Expert Syst. Appl..

[40]  L. N. Sharma.  Multiscale analysis of heart sound for segmentation using multiscale Hilbert envelope , 2015, 2015 13th International Conference on ICT and Knowledge Engineering (ICT & Knowledge Engineering 2015).

[41]  Tamer Ölmez,et al.  Feature determination for heart sounds based on divergence analysis , 2009, Digit. Signal Process..

[42]  K. I. Ramachandran,et al.  A novel heart sound activity detection framework for automated heart sound analysis , 2014, Biomed. Signal Process. Control..

[43]  O. Alfieri,et al.  Treatment and management of mitral regurgitation , 2012, Nature Reviews Cardiology.

[44]  Masun Nabhan Homsi,et al.  Ensemble methods with outliers for phonocardiogram classification , 2017, Physiological measurement.

[45]  Shankar M. Krishnan,et al.  Neural network classification of homomorphic segmented heart sounds , 2007, Appl. Soft Comput..

[46]  Francesca N. Delling,et al.  Heart Disease and Stroke Statistics—2018 Update: A Report From the American Heart Association , 2018, Circulation.

[47]  S Ari,et al.  A robust heart sound segmentation algorithm for commonly occurring heart valve diseases , 2008, Journal of medical engineering & technology.

[48]  Shyamala C. Doraisamy,et al.  Wavelet Packet Entropy for Heart Murmurs Classification , 2012, Adv. Bioinformatics.

[49]  S. Daliman,et al.  Analysis of heart sounds based on continuous wavelet transform , 2003, Proceedings. Student Conference on Research and Development, 2003. SCORED 2003..

[50]  Irini Reljin,et al.  Application of wavelet and EMD-based denoising to phonocardiograms , 2013, International Symposium on Signals, Circuits and Systems ISSCS2013.

[51]  Ajoy Kumar Ray,et al.  Development of Cardiac Prescreening Device for Rural Population Using Ultralow-Power Embedded System , 2011, IEEE Transactions on Biomedical Engineering.

[52]  J. Pakiela,et al.  Improved auscultation skills in paramedic students using a modified stethoscope. , 2012, The Journal of emergency medicine.

[53]  Tanzila Saba,et al.  Microscopic abnormality classification of cardiac murmurs using ANFIS and HMM , 2018, Microscopy research and technique.

[54]  C. J. Malanga.  Structure and Function of Cardiac Cells , 2007 .

[55]  Christian Brandt,et al.  A robust heart sounds segmentation module based on S-transform , 2013, Biomed. Signal Process. Control..

[56]  Syed Anas Imtiaz,et al.  Algorithms for Automatic Analysis and Classification of Heart Sounds–A Systematic Review , 2019, IEEE Access.

[57]  Amir Sepehri,et al.  An automatic tool for pediatric heart sounds segmentation , 2011, 2011 Computing in Cardiology.

[58]  Wei Chen,et al.  An improved empirical mode decomposition-wavelet algorithm for phonocardiogram signal denoising and its application in the first and second heart sound extraction , 2013, 2013 6th International Conference on Biomedical Engineering and Informatics.

[59]  S. Ari,et al.  On An Algorithm for Boundary Estimation of Commonly Occurring Heart Valve Diseases in Time Domain , 2006, 2006 Annual IEEE India Conference.

[60]  Jyotirmoy Chatterjee,et al.  A comparative study of phonocardiogram analysis techniques based on mixed signal processor , 2010, 2010 Annual IEEE India Conference (INDICON).

[61]  Harun Uguz,et al.  A Biomedical System Based on Artificial Neural Network and Principal Component Analysis for Diagnosis of the Heart Valve Diseases , 2012, Journal of Medical Systems.

[62]  Anurag Agarwal,et al.  DropConnected neural networks trained on time-frequency and inter-beat features for classifying heart sounds , 2017, Physiological measurement.

[63]  Nan Guo,et al.  Design of the MEMS Piezoresistive Electronic Heart Sound Sensor , 2016, Sensors.

[64]  Nathan Intrator,et al.  Cluster analysis and classification of heart sounds , 2009, Biomed. Signal Process. Control..

[65]  Peter Hult,et al.  Feature Extraction for Systolic Heart Murmur Classification , 2006, Annals of Biomedical Engineering.

[66]  F Plesinger,et al.  Heart sounds analysis using probability assessment , 2017, Physiological measurement.

[67]  Yongwan Park,et al.  Separation of Heart Sound Signal from Noise in Joint Cycle Frequency–Time–Frequency Domains Based on Fuzzy Detection , 2010, IEEE Transactions on Biomedical Engineering.

[68]  Maryam Imani,et al.  Classification of heart sound signal using curve fitting and fractal dimension , 2018, Biomed. Signal Process. Control..

[69]  Liang Zhong,et al.  The electronic stethoscope , 2015, BioMedical Engineering OnLine.

[70]  E. Bassiachvili,et al.  A MEMS capacitive, passively powered heart rate monitor: Design and analysis , 2008, 2008 1st Microsystems and Nanoelectronics Research Conference.

[71]  Leontios J. Hadjileontiadis,et al.  Efficient Heart Sound Segmentation and Extraction Using Ensemble Empirical Mode Decomposition and Kurtosis Features , 2014, IEEE Journal of Biomedical and Health Informatics.

[72]  Robert Glenn Stockwell,et al.  A basis for efficient representation of the S-transform , 2007, Digit. Signal Process..

[73]  Y. Tseng,et al.  Detection of the third and fourth heart sounds using Hilbert-Huang transform , 2012, BioMedical Engineering OnLine.

[74]  Ram Bilas Pachori,et al.  egmentation of cardiac sound signals by removing murmurs using onstrained tunable-Q wavelet transform , 2013 .

[75]  Ahmad Mozaffari,et al.  A Fuzzy Model with Thermodynamic Based Consequents and a Niching Swarm-Based Supervisor to Capture the Uncertainties of Damavand Power System , 2014, Int. J. Appl. Metaheuristic Comput..

[76]  Weidong Wang,et al.  Design and Implementation of T-type MEMS heart sound sensor , 2019, Sensors and Actuators A: Physical.

[77]  M. Rouhani,et al.  A comparison of different feature extraction methods for diagnosis of valvular heart diseases using PCG signals , 2012, Journal of medical engineering & technology.

[78]  Giacomo Capizzi,et al.  Automatic heart activity diagnosis based on Gram polynomials and probabilistic neural networks , 2018, Biomedical engineering letters.

[79]  Hung-Wen Chiu,et al.  Using time-frequency features to recognize abnormal heart sounds , 2016, 2016 Computing in Cardiology Conference (CinC).

[80]  Zhongwei Jiang,et al.  The moment segmentation analysis of heart sound pattern , 2010, Comput. Methods Programs Biomed..

[81]  Veena N. Hegde,et al.  RANDOM NOISE CANCELLATION IN BIOMEDICAL SIGNALS USING VARIABLE STEP SIZE GRIFFITH LMS ADAPTIVE LINE ENHANCER , 2012 .

[82]  Hassan Ghassemian,et al.  Modeling of heart systolic murmurs based on multivariate matching pursuit for diagnosis of valvular disorders , 2011, Comput. Biol. Medicine.

[83]  I. Hartimo,et al.  Heart sound segmentation algorithm based on heart sound envelogram , 1997, Computers in Cardiology 1997.

[84]  Philippe Ravaud,et al.  A prospective survey of patients with valvular heart disease in Europe: The Euro Heart Survey on Valvular Heart Disease. , 2003, European heart journal.

[85]  Sukryool Kang,et al.  Automated Identification of Innocent Still's Murmur in Children , 2017, IEEE Transactions on Biomedical Engineering.

[86]  Ridvan Saraçoglu,et al.  Hidden Markov model-based classification of heart valve disease with PCA for dimension reduction , 2012, Eng. Appl. Artif. Intell..

[87]  K Basak,et al.  Phonocardiogram signal analysis using adaptive line enhancer methods on Mixed Signal Processor , 2010, 2010 International Conference on Signal Processing and Communications (SPCOM).

[88]  Wenjie Zhang,et al.  Heart sound classification based on scaled spectrogram and partial least squares regression , 2017, Biomed. Signal Process. Control..

[89]  A. Mondal,et al.  Boundary estimation of cardiac events S1 and S2 based on Hilbert transform and adaptive thresholding approach , 2013, 2013 Indian Conference on Medical Informatics and Telemedicine (ICMIT).

[90]  Gari D Clifford,et al.  Combining sparse coding and time-domain features for heart sound classification , 2017, Physiological measurement.