Automated detection of abnormal heart sound signals using Fano-factor constrained tunable quality wavelet transform
Tài liệu tham khảo
Organization, 2018, 2018
Mandal, 2010, Development of cardiac prescreening device for rural population using ultralow-power embedded system, IEEE Trans Biomed Eng, 58, 745, 10.1109/TBME.2010.2089457
Nielsen, 2010, The development of a new cardiac auscultation test: how do screening and diagnostic skills differ?, Med Teacher, 32, 56, 10.3109/01421590802572767
Mangione, 2001, Cardiac auscultatory skills of physicians-in-training: a comparison of three English-speaking countries, Am J Med, 110, 210, 10.1016/S0002-9343(00)00673-2
Kumar, 2018, Improved computerized cardiac auscultation by discarding artifact contaminated pcg signal sub-sequence, Biomed Signal Process Control, 41, 48, 10.1016/j.bspc.2017.11.001
Hamidi, 2018, Classification of heart sound signal using curve fitting and fractal dimension, Biomed Signal Process Control, 39, 351, 10.1016/j.bspc.2017.08.002
Humayun, 2018
Li, 2019, Heart sound signal classification algorithm: a combination of wavelet scattering transform and twin support vector machine, IEEE Access, 7, 179339, 10.1109/ACCESS.2019.2959081
Humayun, 2020, Towards domain invariant heart sound abnormality detection using learnable filterbanks, IEEE J Biomed Health Inform, 10.1109/JBHI.2020.2970252
Springer, 2015, Logistic regression-hsmm-based heart sound segmentation, IEEE Trans Biomed Eng, 63, 822
Noman, 2019, A markov-switching model approach to heart sound segmentation and classification, IEEE J Biomed Health Inform, 24, 705, 10.1109/JBHI.2019.2925036
Varghees, 2017, Effective heart sound segmentation and murmur classification using empirical wavelet transform and instantaneous phase for electronic stethoscope, IEEE Sens J, 17, 3861, 10.1109/JSEN.2017.2694970
Chandra, 2017, Atrial fibrillation detection using convolutional neural networks, 1
Xiao, 2020, Heart sounds classification using a novel 1-d convolutional neural network with extremely low parameter consumption, Neurocomputing, 392, 153, 10.1016/j.neucom.2018.09.101
Messner, 2018, Heart sound segmentation – an event detection approach using deep recurrent neural networks, IEEE Trans Biomed Eng, 65, 1964, 10.1109/TBME.2018.2843258
Zhang, 2017, Heart sound classification based on scaled spectrogram and partial least squares regression, Biomed Signal Process Control, 32, 20, 10.1016/j.bspc.2016.10.004
Whitaker, 2017, Combining sparse coding and time-domain features for heart sound classification, Physiol Meas, 38, 1701, 10.1088/1361-6579/aa7623
Bozkurt, 2018, A study of time-frequency features for CNN-based automatic heart sound classification for pathology detection, Comput Biol Med, 100, 132, 10.1016/j.compbiomed.2018.06.026
Zhang, 2019, Abnormal heart sound detection using temporal quasi-periodic features and long short-term memory without segmentation, Biomed Signal Process Control, 53, 101560, 10.1016/j.bspc.2019.101560
Yuenyong, 2011, A framework for automatic heart sound analysis without segmentation, Biomed Eng Online, 10, 13, 10.1186/1475-925X-10-13
Zabihi, 2016, Heart sound anomaly and quality detection using ensemble of neural networks without segmentation, 613
Dominguez-Morales, 2017, Deep neural networks for the recognition and classification of heart murmurs using neuromorphic auditory sensors, IEEE Trans Biomed Circuits Syst, 12, 24, 10.1109/TBCAS.2017.2751545
Patidar, 2013, Segmentation of cardiac sound signals by removing murmurs using constrained tunable-q wavelet transform, Biomed Signal Process Control, 8, 559, 10.1016/j.bspc.2013.05.004
Jain, 2018, A robust algorithm for segmentation of phonocardiography signal using tunable quality wavelet transform, J Med Biol Eng, 38, 396, 10.1007/s40846-017-0320-7
Patidar, 2014, Classification of cardiac sound signals using constrained tunable-q wavelet transform, Expert Syst Appl, 41, 7161, 10.1016/j.eswa.2014.05.052
Zhang, 2017, Heart sound classification based on scaled spectrogram and tensor decomposition, Expert Syst Appl, 84, 220, 10.1016/j.eswa.2017.05.014
Alonso-Arévalo, 2021, Robust heart sound segmentation based on spectral change detection and genetic algorithms, Biomed Signal Process Control, 63, 102208, 10.1016/j.bspc.2020.102208
Kleć, 2018, Early detection of heart symptoms with convolutional neural network and scattering wavelet transformation, 24
Selesnick, 2011, Wavelet transform with tunable q-factor, IEEE Trans Signal Process, 59, 3560, 10.1109/TSP.2011.2143711
Nishad, 2018, Application of tqwt based filter-bank for sleep apnea screening using ecg signals, J Ambient Intell Humaniz Comput, 1
Patidar, 2017, An integrated alcoholic index using tunable-q wavelet transform based features extracted from eeg signals for diagnosis of alcoholism, Appl Soft Comput, 50, 71, 10.1016/j.asoc.2016.11.002
Hassan, 2016, Epileptic seizure detection in eeg signals using tunable-q factor wavelet transform and bootstrap aggregating, Comput Methods Programs Biomed, 137, 247, 10.1016/j.cmpb.2016.09.008
Zeng, 2020, A new approach for the detection of abnormal heart sound signals using tqwt, vmd and neural networks, Artif Intell Rev, 1
Teich, 1985, Pulse-number distribution for the neural spike train in the cat's auditory nerve, J Acoust Soc Am, 77, 1110, 10.1121/1.392176
Fano, 1947, Ionization yield of radiations. II. The fluctuations of the number of ions, Phys Rev, 72, 26, 10.1103/PhysRev.72.26
Teich, 2001, Heart rate variability: measures and models, Nonlinear Biomed Signal Process, 2, 159
Rubin, 2016, Classifying heart sound recordings using deep convolutional neural networks and mel-frequency cepstral coefficients, 813
Patidar, 2015, Automatic diagnosis of septal defects based on tunable-q wavelet transform of cardiac sound signals, Expert Syst Appl, 42, 3315, 10.1016/j.eswa.2014.11.046
Giannakopoulos, 2014
Sharma, 2020, Trends in audio signal feature extraction methods, Appl Acoust, 158, 107020, 10.1016/j.apacoust.2019.107020
Ke, 2017, Lightgbm: a highly efficient gradient boosting decision tree, 3146
Liu, 2016, An open access database for the evaluation of heart sound algorithms, Physiol Meas, 37, 2181, 10.1088/0967-3334/37/12/2181
Bentley, 2011
Brochu, 2010
Snoek, 2012, Practical bayesian optimization of machine learning algorithms, Adv Neural Inf Process Syst, 25, 2951
Feng, 2019, Dynamic synthetic minority over-sampling technique-based rotation forest for the classification of imbalanced hyperspectral data, IEEE J Sel Topics Appl Earth Obs Remote Sens, 12, 2159, 10.1109/JSTARS.2019.2922297
Ibarra-Hernández, 2018, A benchmark of heart sound classification systems based on sparse decompositions, 1097505
Wu, 2019, Applying an ensemble convolutional neural network with savitzky-golay filter to construct a phonocardiogram prediction model, Appl Soft Comput, 78, 29, 10.1016/j.asoc.2019.01.019
Baydoun, 2020, Analysis of heart sound anomalies using ensemble learning, Biomed Signal Process Control, 62, 102019, 10.1016/j.bspc.2020.102019
Xiao, 2019, Heart sounds classification using a novel 1-d convolutional neural network with extremely low parameter consumption, Neurocomputing
Das, 2019, Supervised model for cochleagram feature based fundamental heart sound identification, Biomed Signal Process Control, 52, 32, 10.1016/j.bspc.2019.01.028
Kay, 2016, Dropconnected neural network trained with diverse features for classifying heart sounds, 617
Homsi, 2016, Automatic heart sound recording classification using a nested set of ensemble algorithms, 817
Clifford, 2016, Classification of normal/abnormal heart sound recordings: the physionet/computing in cardiology challenge 2016, 609
Potes, 2016, Ensemble of feature-based and deep learning-based classifiers for detection of abnormal heart sounds, 621
Deng, 2020, Heart sound classification based on improved mfcc features and convolutional recurrent neural networks, Neural Netw, 10.1016/j.neunet.2020.06.015
Gomes, 2012, Classifying heart sounds using peak location for segmentation and feature construction, 480
Deng, 2012, A robust heart sound segmentation and classification algorithm using wavelet decomposition and spectrogram, 1
Pikrakis, 2006, A computationally efficient speech/music discriminator for radio recordings, 107