Design and development of wireless stethoscope with data logging function

Machine Learning and IoT Applied to Cardiovascular Diseases Identification through Heart Sounds: A Literature Review

“…Table 5 presents the list of selected articles, containing a numerical identification, reference, authors' countries, databases, year of publication, and a short description of the research work. India IEEE Xplore Noise detection on sign Ayari et al [54] Tunisia/USA IEEE Xplore Mathematical component analysis algorithm for separation of cardiac sounds from pulmonary sounds Udawatta et al [55] Sri Lanka IEEE Xplore Digital stethoscope to amplify signal Malek et al [56] Malaysia IEEE Xplore Digital stethoscope in Arduino, ZigBee and signal processing by MatLab Singh and Singh [36] India IEEE Xplore Convolutional Neural Networks Das et al [57] India IEEE Xplore Algorithm to remove signal noise, regardless of sensor quality Gjoreski et al [58] Slovenia/Macedônia IEEE Xplore Machine Learning Pereira et al [59] Portugal/Brasil IEEE Xplore Machine Learning Banerjee et al [60] India IEEE Xplore Convolutional Neural Networks Suhn et al [61] Germany IEEE Xplore Carotid auscultation equipment Gautam and kumar [62] India IEEE Xplore Multilayer Multilayer Perceptron Artificial Neural Network Zhang et al [63] Singapore IEEE Xplore Heart rate estimation algorithm Doshi et al [64] India IEEE Xplore Neural Network Prasad et al [65] Switzerland IEEE Xplore Processing in the time domain employing a low-pass filter Rao et al [66] Switzerland IEEE Xplore Neural Network Hui et al [67] USA IEEE Xplore Investigates transient movement and heartbeat Humayun et al [25] Bangladesh/USA IEEE Xplore Use of convolutional neural network to detect abnormality of cardiac sound with stethoscope Shuvo et al [68] Bangladesh/Saudi Arabia/Yemen IEEE Xplore Convolutional Neural Network for automatic detection of different classes of cardiovascular diseases, direct by phonocardiography signal Tiwari et al [27] India/Saudi Arabia IEEE Xplore Hybrid model, with signal processing using the constant Q transform and Convolutional Neural Network Du et al [69] China JMIR Big Data and Machine Learning Chowdhury et al [26] Qatar/Malaysia PubMed Central Processing and classification using MATLAB Leng et al [30] Singapore PubMed Central Machine Learning Techniques Elgendi et al [70] Canada/India PubMed Central Developed a Wavelet-based algorithm SwarupandMakaryus [71] USA PubMed Central Use of digital stethoscope and mobile computing Raza et al…”

Machine Learning and IoT Applied to Cardiovascular Diseases Identification through Heart Sounds: A Literature Review

“…The model used the Arduino, connected by Bluetooth, to the notebook for processing and analysis of the signal by the MatLab software and Android smartphone. MatLab software was used in 24 works (IDs = 2, 3, 7, 8, 9, 11, 16,17,18,24,26,27,30,35,37,39,42,46,48,54,55,56,57,58).…”

“…Among the 58 articles selected, 34 works (58.62%,IDs = 1, 2, 5, 6, 10, 19,20,21,22,25,31,32,33,34,35,36,37,39,40,41,42,43,44,46,48,49,50,51,52,53,54,55,56,58) mention care using Machine Learning. Leng et al [30] (36) showed the possible interactions between the electronic stethoscope, the sensor-captured signal decomposition algorithm, machine learning techniques and cardiac sound segmentation.…”

“…Ren et al [42] (2) used convolutional neural networks (CNN) (19 articles cite CNN, IDs = 1, 2, 10, 19,23,31,32,33,39,41,43,46,47,49,51,52,54,55,58) for classification of PCG scale images for the task of classifying cardiac sounds between normal and abnormal. PCG signal representations were obtained by dataset from: Massachusetts Institute of Technology (MIT), Aalborg University, Aristotle University of Thessaloniki, University of Haute Alsace, Dalian University of Technology, and Shiraz University.…”

“…Among the 58 articles selected, 30 works (51.72%, IDs = 1, 3, 4, 5, 6, 7, 9, 10, 13,15,19,20,23,27,29,30,31,32,33,35,36,41,44,46,48,49,50,51,52,58) inform the possibility of using low-cost devices to assist in screening more sophisticated tests for the finding of a cardiac disjunction.…”