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Ngoc Thang Bui, Tan Hung Vo, Byung-Gak Kim and Junghwan Oh
One of the best ways to monitor the health of the heart is to regularly record its electrical activity by using an electrocardiogram (ECG). Abnormal ECG signals may indicate conditions such as heart attack, arrhythmia, or heart defects. There are many EC...
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Donggeun You, Hyunwoo Heo, Hyungseup Kim, Yongsu Kwon, Sangmin Lee and Hyoungho Ko
A portable two-electrode ECG monitoring application.
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Ionel Zagan, Vasile Gheorghi?a Gaitan, Adrian-Ioan Petrariu, Nicolai Iuga and Adrian Brezulianu
The expansion of the concept of the Internet of Things (IoT), together with wireless sensor networks, has given rise to a wide range of IoT applications. This paper presents and describes the concept, theory of operation, and practical results of a Telec...
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Muhammad Asraful Hasan, Md Mamun
Pág. 195 - 203
Fetal monitoring may help with possible recognition of problems in the fetus. This research work focuses on the design of the Back-propagation Neural Network (BPNN) and Adaptive Linear Neural Network (ADALINE) to extract the Fetal Electrocardiogram (FECG...
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Mohammad Mahbubur Rahman Khan Mamun and Tarek Elfouly
Contemporary methods used to interpret the electrocardiogram (ECG) signal for diagnosis or monitoring are based on expert knowledge and rule-centered algorithms. In recent years, with the advancement of artificial intelligence, more and more researchers ...
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Yunfei Cheng, Ying Hu, Mengshu Hou, Tongjie Pan, Wenwen He and Yalan Ye
In the wearable health monitoring based on compressed sensing, atrial fibrillation detection directly from the compressed ECG can effectively reduce the time cost of data processing rather than classification after reconstruction. However, the existing m...
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Sangeetha Ramaswamy and Usha Devi Gandhi
A subset of Wireless Sensor Networks, Wireless Body Area Networks (WBAN) is an emerging technology. WBAN is a collection of tiny pieces of wireless body sensors with small computational capability, communicating short distances using ZigBee or Bluetooth,...
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Daniela Cardone, David Perpetuini, Chiara Filippini, Edoardo Spadolini, Lorenza Mancini, Antonio Maria Chiarelli and Arcangelo Merla
A procedure for a driver?s stress state monitoring was provided by means of thermal infrared imaging. It was validated on ECG-derived parameters through the application of supervised machine learning techniques.
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Roberta Avanzato, Francesco Beritelli, Alfio Lombardo and Carmelo Ricci
Today?s healthcare facilities require new digital tools to cope with the rapidly increasing demand for technology that can support healthcare operators. The advancement of technology is leading to the pervasive use of IoT devices in daily life, capable o...
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Reza Soleimani and Edgar Lobaton
Physiological and kinematic signals from humans are often used for monitoring health. Several processes of interest (e.g., cardiac and respiratory processes, and locomotion) demonstrate periodicity. Training models for inference on these signals (e.g., d...
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Agostino Giorgio, Cataldo Guaragnella and Maria Rizzi
The high mortality rate associated with cardiac abnormalities highlights the need of accurately detecting heart disorders in the early stage so to avoid severe health consequence for patients. Health trackers have become popular in the form of wearable d...
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Taki Hasan Rafi and Young-Woong Ko
Electrocardiography (ECG)-based arrhythmia classification intends to have a massive role in cardiovascular disease monitoring and early diagnosis. However, ECG datasets are mostly imbalanced and have regularization to use real-time patient data due to pr...
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Yue Ma, Qing Liu and Liu Yang
Seafarers are prone to reduce behavioral reliability under high workloads, resulting in human errors and accidents. To explore the changes in seafarers? workload and physiological activities under complex task conditions, a bridge simulator experiment wa...
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Siti Nurmaini, Radiyati Umi Partan, Wahyu Caesarendra, Tresna Dewi, Muhammad Naufal Rahmatullah, Annisa Darmawahyuni, Vicko Bhayyu and Firdaus Firdaus
An automated classification system based on a Deep Learning (DL) technique for Cardiac Disease (CD) monitoring and detection is proposed in this paper. The proposed DL architecture is divided into Deep Auto-Encoders (DAEs) as an unsupervised form of feat...
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