Design of an Auscultation System for Phonoangiography and Monitoring of Carotid Artery Diseases

<p>Auscultation System for Acquisition of Vascular Sounds – Towards Sound-Based Monitoring of the Carotid Artery</p>

“…Dimensions and mechanical configuration of the interface can have a considerable impact on the quality and reproducibility of acquisition. 22 It has been shown in Figure 12, that the presence of an acoustic membrane does change the spectral properties of the acquired sounds, since it appears to shift spectral components toward the higher frequencies. A possible explanation is microtremors introduced by the hand of the user which result in motion and friction between membrane and skin.…”

“…To determine the appropriate physical parameters (eg, shape, surface area, transmission path, acoustic membrane) of the interface, a preliminary phantom study was conducted for different configurations. 21,22 The comparison of various bell-shaped structures similar to an analog stethoscope and with and without incorporated acoustic membrane resulted in the design shown in Figure 3. The proposed interface consists of two bell-shaped structures of similar dimensions located next to each other.…”

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

“…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…”