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Cardiovascular diseases (CVDs) are the leading cause of death worldwide, accounting for approximately 17.9 million deaths each year. Early detection is critical, creating a demand for accurate and inexpensive pre-screening methods. Deep…

声音 · 计算机科学 2025-12-09 Milan Marocchi , Matthew Fynn , Kayapanda Mandana , Yue Rong

The leading cause of mortality and morbidity worldwide is cardiovascular disease (CVD), with coronary artery disease (CAD) being the largest sub-category. Unfortunately, myocardial infarction or stroke can manifest as the first symptom of…

信号处理 · 电气工程与系统科学 2024-09-10 Matthew Fynn , Kayapanda Mandana , Javed Rashid , Sven Nordholm , Yue Rong , Goutam Saha

Cardiovascular diseases represent a leading cause of mortality worldwide, necessitating accurate and early diagnosis for improved patient outcomes. Current diagnostic approaches for cardiac abnormalities often present challenges in clinical…

信号处理 · 电气工程与系统科学 2025-11-11 Seyed Amir Latifi , Hassan Ghassemian , Maryam Imani

Cardiovascular diseases (CVDs) are the main cause of deaths all over the world. Heart murmurs are the most common abnormalities detected during the auscultation process. The two widely used publicly available phonocardiogram (PCG) datasets…

Coronary Artery Disease (CAD) diagnostic to be a major global cause of death, necessitating innovative solutions. Addressing the critical importance of early CAD detection and its impact on the mortality rate, we propose the potential of…

信号处理 · 电气工程与系统科学 2024-06-26 Atitaya Phoemsuk , Vahid Abolghasemi

Mobile electrocardiogram (ECG) recording technologies represent a promising tool to fight the ongoing epidemic of cardiovascular diseases, which are responsible for more deaths globally than any other cause. While the ability to monitor…

信号处理 · 电气工程与系统科学 2018-10-10 Jennifer N. John , Conner Galloway , Alexander Valys

Cardiovascular diseases (CVDs) can be effectively treated when detected early, reducing mortality rates significantly. Traditionally, phonocardiogram (PCG) signals have been utilized for detecting cardiovascular disease due to their…

信号处理 · 电气工程与系统科学 2023-08-30 Samiul Based Shuvo , Syed Samiul Alam , Syeda Umme Ayman , Arbil Chakma , Prabal Datta Barua , U Rajendra Acharya

Heart diseases constitute a global health burden, and the problem is exacerbated by the error-prone nature of listening to and interpreting heart sounds. This motivates the development of automated classification to screen for abnormal…

声音 · 计算机科学 2016-12-07 Yuhao Zhang , Sandeep Ayyar , Long-Huei Chen , Ethan J. Li

Electrocardiogram (ECG) is the most widely used diagnostic tool to monitor the condition of the cardiovascular system. Deep neural networks (DNNs), have been developed in many research labs for automatic interpretation of ECG signals to…

信号处理 · 电气工程与系统科学 2020-12-02 Linhai Ma , Liang Liang

In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of mortality globally. CVDs appear with minor symptoms and progressively get worse. The majority of people experience symptoms such as exhaustion,…

Coronary Artery Disease (CAD) is one of the leading causes of death worldwide, and so it is very important to correctly diagnose patients with the disease. For medical diagnosis, machine learning is a useful tool, however features and…

机器学习 · 计算机科学 2018-12-10 Ali Bou Nassif , Omar Mahdi , Qassim Nasir , Manar Abu Talib , Mohammad Azzeh

Raman spectroscopy (RS) has been widely used for disease diagnosis, e.g., cardiovascular disease (CVD), owing to its efficiency and component-specific testing capabilities. A series of popular deep learning methods have recently been…

图像与视频处理 · 电气工程与系统科学 2023-04-20 Bo Yu , Hechang Chen , Chengyou Jia , Hongren Zhou , Lele Cong , Xiankai Li , Jianhui Zhuang , Xianling Cong

Cardiovascular disease is one of the most challenging diseases in middle-aged and older people, which causes high mortality. Coronary artery disease (CAD) is known as a common cardiovascular disease. A standard clinical tool for diagnosing…

Artificial intelligence and deep learning are increasingly applied in the clinical domain, particularly for early and accurate disease detection using medical imaging and sound. Due to limited trained personnel, there is a growing demand…

图像与视频处理 · 电气工程与系统科学 2025-09-30 Shahran Rahman Alve , Muhammad Zawad Mahmud , Samiha Islam , Mohammad Monirujjaman Khan

This study proposes an efficient neural network with convolutional layers to classify significantly class-imbalanced clinical data. The data are curated from the National Health and Nutritional Examination Survey (NHANES) with the goal of…

定量方法 · 定量生物学 2020-04-24 Aniruddha Dutta , Tamal Batabyal , Meheli Basu , Scott T. Acton

Lung diseases, including lung cancer and COPD, are significant health concerns globally. Traditional diagnostic methods can be costly, time-consuming, and invasive. This study investigates the use of semi supervised learning methods for…

音频与语音处理 · 电气工程与系统科学 2025-08-12 Xiaoran Xu , In-Ho Ra , Ravi Sankar

Cardiovascular diseases are a pervasive global health concern, contributing significantly to morbidity and mortality rates worldwide. Among these conditions, arrhythmia, characterized by irregular heart rhythms, presents formidable…

信号处理 · 电气工程与系统科学 2024-04-25 Bhavith Chandra Challagundla

Coronary artery disease (CAD) is the dominant cause of death and hospitalization across the globe. Atherosclerosis, an inflammatory condition that gradually narrows arteries and has potentially fatal effects, is the most frequent cause of…

Coronary heart disease, which is a form of cardiovascular disease (CVD), is the leading cause of death worldwide. The odds of survival are good if it is found or diagnosed early. The current report discusses a comparative approach to the…

计算机视觉与模式识别 · 计算机科学 2021-12-03 Kelvin Kwakye , Emmanuel Dadzie

Cardiac auscultation is an essential point-of-care method used for the early diagnosis of heart diseases. Automatic analysis of heart sounds for abnormality detection is faced with the challenges of additive noise and sensor-dependent…

声音 · 计算机科学 2021-06-04 Farhat Binte Azam , Md. Istiaq Ansari , Ian Mclane , Taufiq Hasan
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