2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET) 2016
DOI: 10.1109/wispnet.2016.7566396
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Development of embedded stethoscope for Heart Sound

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Cited by 5 publications
(3 citation statements)
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“…The analysis of different deep learning models suggested that all the proposed deep learning methods were successful and achieved high performance in classifying the unprocessed lung sounds [35,38]. Similarly, there is research on the use of embedded stethoscopes designed to serve as a platform for the computer-aided diagnosis of cardiac sounds for the detection of cardiac murmurs [67], with other research advancing to a portable device with the capability to diagnose cardiac pathology in real time, employing the signal conversion of analogue acoustic signals into a digital signal that can simultaneously be displayed on a computer using a MATLAB graphic user interface for visual representation, thereby enabling a critical analysis of the interpreted data [68]. This can be used as a clinical tool for the diagnosis of valvular and other structural heart diseases in educational settings [69].…”
Section: Ai and Audio Data Comparison Analysismentioning
confidence: 99%
“…The analysis of different deep learning models suggested that all the proposed deep learning methods were successful and achieved high performance in classifying the unprocessed lung sounds [35,38]. Similarly, there is research on the use of embedded stethoscopes designed to serve as a platform for the computer-aided diagnosis of cardiac sounds for the detection of cardiac murmurs [67], with other research advancing to a portable device with the capability to diagnose cardiac pathology in real time, employing the signal conversion of analogue acoustic signals into a digital signal that can simultaneously be displayed on a computer using a MATLAB graphic user interface for visual representation, thereby enabling a critical analysis of the interpreted data [68]. This can be used as a clinical tool for the diagnosis of valvular and other structural heart diseases in educational settings [69].…”
Section: Ai and Audio Data Comparison Analysismentioning
confidence: 99%
“…Compared with previous studies [17][18][19][20], this research designed and developed a complete wearable device instead of a single heart sound collector, and explored the wearing experience of the heart sound collection device, and the experimental participants gave a positive evaluation, all of whom found that the device is suitable for daily wear. In consistent with them the denoised heart sounds were clear, and additionally heart sound detection was added, using a convolutional neural network system to detect the pairs of collected heart sounds and obtain better results.…”
Section: Factor Analysis Of Heart Sound Collection Vestmentioning
confidence: 99%
“…Among them, the development of electronic stethoscope collection of heart sounds is relatively mature. For example, Beck C et al [17] designed and developed a multi-mode physiological parameter collection wearable device including heart sound auscultation, which can be used with MATLAB software to obtain real-time data or store data in software; Tiwari, Hemant Kumar et al [18] have designed an embedded stethoscope served as a platform www.ijacsa.thesai.org for the computer aided diagnosis of cardiac sound for the detection of cardiac murmur, the device can display heart sounds on the TFT LCD display in real time, and stored on the micro SD card. However, these devices are inconvenient for daily carrying due to their large size.…”
Section: Introductionmentioning
confidence: 99%