Auscultation is used to evaluate heart health, and can indicate when it's needed to refer a patient to a cardiologist. Advanced phonocardiograph (PCG) signal processing algorithms are developed to assist the physician in the initial diagnosis but they are primarily designed and demonstrated with research quality equipment. Therefore, there is a need to demonstrate the applicability of those techniques with consumer grade instrument. Furthermore, routine monitoring would benefit from a wireless PCG sensor that allows continuous monitoring of cardiac signals of patients in physical activity, e.g., treadmill or weight exercise. In this work, a low-cost portable and wireless healthcare monitoring system based on PCG signal is implemented to validate and evaluate the most advanced algorithms. Off-the-shelf electronics and a notebook PC are used with MATLAB codes to record and analyze PCG signals which are collected with a notebook computer in tethered and wireless mode. Physiological parameters based on the S1 and S2 signals and MATLAB codes are demonstrated. While the prototype is based on MATLAB, the later is not an absolute requirement.
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http://dx.doi.org/10.7717/peerj.1178 | DOI Listing |
Semin Ophthalmol
December 2024
Kallam Anji Reddy Molecular Genetics Laboratory, Prof. Brien Holden Eye Research Center, L V Prasad Eye Institute, Hyderabad, Telangana, India.
Background: The anterior segment of the eye plays a crucial role in maintaining the normal intraocular pressure and vision. Developmental defects in the anterior segment structures lead to anterior segment dysgenesis (ASD) and primary congenital glaucoma (PCG), which share overlapping clinical features. Several genes have been mapped and characterized in ASD, some of which are also involved in other glaucoma phenotypes.
View Article and Find Full Text PDFPhysiol Meas
December 2024
Biomedical Engineering, Technion Israel Institute of Technology, Julius Silver Building, Haifa, 32000, ISRAEL.
Objective: Phonocardiography has recently gained popularity in low-cost and remote monitoring, including passive fetal heart monitoring. The development of methods which analyse phonocardiographic data tries to capitalize on this opportunity, and in recent years a multitude of such algorithms and models have been published. In these approaches there is little to no standardization and multiple parts of these models have to be reimplemented on a case-by-case basis.
View Article and Find Full Text PDFBioengineering (Basel)
October 2024
Huiyironggong Technology Co., Ltd., Jinan 250098, China.
Early and highly precise detection is essential for delaying the progression of coronary artery disease (CAD). Previous methods primarily based on single-modal data inherently lack sufficient information that compromises detection precision. This paper proposes a novel multi-modal learning method aimed to enhance CAD detection by integrating ECG, PCG, and coupling signals.
View Article and Find Full Text PDFSensors (Basel)
October 2024
Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan 250061, China.
Coronary artery disease (CAD) is an irreversible and fatal disease. It necessitates timely and precise diagnosis to slow CAD progression. Electrocardiogram (ECG) and phonocardiogram (PCG), conveying abundant disease-related information, are prevalent clinical techniques for early CAD diagnosis.
View Article and Find Full Text PDFComput Methods Programs Biomed
December 2024
Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721302, West Bengal, India. Electronic address:
Background And Objective: Phonocardiogram (PCG) signal analysis is a non-invasive and cost-efficient approach for diagnosing cardiovascular diseases. Existing PCG-based approaches employ signal processing and machine learning (ML) for automatic disease detection. However, machine learning techniques are known to underperform in cross-corpora arrangements.
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