Embedded Stethoscope for Real Time Diagnosis of Cardiovascular Diseases

Monika, .; Rm, Bommi; L, Harish Prabu; Murali, Manish; Nirmala, S.

doi:10.1109/iccic.2017.8524281

Cited by 4 publications

(1 citation statement)

References 8 publications

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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%

Review on the Advancements of Stethoscope Types in Chest Auscultation

et al. 2023

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Stethoscopes were originally designed for the auscultation of a patient’s chest for the purpose of listening to lung and heart sounds. These aid medical professionals in their evaluation of the cardiovascular and respiratory systems, as well as in other applications, such as listening to bowel sounds in the gastrointestinal system or assessing for vascular bruits. Listening to internal sounds during chest auscultation aids healthcare professionals in their diagnosis of a patient’s illness. We performed an extensive literature review on the currently available stethoscopes specifically for use in chest auscultation. By understanding the specificities of the different stethoscopes available, healthcare professionals can capitalize on their beneficial features, to serve both clinical and educational purposes. Additionally, the ongoing COVID-19 pandemic has also highlighted the unique application of digital stethoscopes for telemedicine. Thus, the advantages and limitations of digital stethoscopes are reviewed. Lastly, to determine the best available stethoscopes in the healthcare industry, this literature review explored various benchmarking methods that can be used to identify areas of improvement for existing stethoscopes, as well as to serve as a standard for the general comparison of stethoscope quality. The potential use of digital stethoscopes for telemedicine amidst ongoing technological advancements in wearable sensors and modern communication facilities such as 5G are also discussed. Based on the ongoing trend in advancements in wearable technology, telemedicine, and smart hospitals, understanding the benefits and limitations of the digital stethoscope is an essential consideration for potential equipment deployment, especially during the height of the current COVID-19 pandemic and, more importantly, for future healthcare crises when human and resource mobility is restricted.

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Section: Ai and Audio Data Comparison Analysismentioning

confidence: 99%

Review on the Advancements of Stethoscope Types in Chest Auscultation

et al. 2023

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Identification of Brain Tumor on Mri images with and without Segmentation using DL Techniques

TAM,

Jayashankari

et al. 2023

E3S Web Conf.

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Brain cancer is a critical disease that results in the deaths of many individuals. Early detection and classification of brain tumors is essential for effective treatment and improved patient outcomes. However, current manual examination of MRI images for tumor detection can be time-consuming and imprecise. In this project, we propose a computer-based system that utilizes image processing techniques and convolutional neural networks (CNNs) for accurate and efficient brain tumor detection and classification. Our system involves several stages, including image pre-processing, segmentation, feature extraction, and classification. By training a CNN on a large dataset of MRI images with known tumor types, our system can accurately detect and classify brain tumors based on extracted features. The results of our experiments demonstrate the effectiveness of our systemin accurately detecting and classifying brain tumors, with potential to greatly improve the accuracy and speed of diagnosis, and ultimately lead to improved patient outcomes. To explicitly depict the tumor region, we have also added the segmentation procedure.

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Semantic Optimization in Lossless Data Compression for IoT-Driven Smart Healthcare Solutions

Hashmi

2023

2023 International Conference on Communication, Security and Artificial Intelligence (ICCSAI)

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Embedded Stethoscope for Real Time Diagnosis of Cardiovascular Diseases

Cited by 4 publications

References 8 publications

Review on the Advancements of Stethoscope Types in Chest Auscultation

Review on the Advancements of Stethoscope Types in Chest Auscultation

Identification of Brain Tumor on Mri images with and without Segmentation using DL Techniques

Semantic Optimization in Lossless Data Compression for IoT-Driven Smart Healthcare Solutions

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