[HTML][HTML] Application of convolutional neural network for decoding of 12-lead electrocardiogram from a frequency-modulated audio stream (sonified ECG)
V Krasteva, I Iliev, S Tabakov - Sensors, 2024 - mdpi.com
Research of novel biosignal modalities with application to remote patient monitoring is a
subject of state-of-the-art developments. This study is focused on sonified ECG modality …
subject of state-of-the-art developments. This study is focused on sonified ECG modality …
Development of signal processing algorithms for de-noising of ECG signals in wearable systems and testing for arrhythmia using CNN model
A Shekhar, S Hota, P Mahalakshmi… - 2021 Innovations in …, 2021 - ieeexplore.ieee.org
Wireless Data Communication is an emerging prospect of technology in the current world
scenario. Processing an ECG signal to drive out informative insights needs the signal to be …
scenario. Processing an ECG signal to drive out informative insights needs the signal to be …
Classification of arrhythmia ecg signals using convolutional neural network
The electrocardiogram (ECG) has been established as a reliable tool for monitoring
cardiovascular health. Vast amount of ECG recordings can pose a challenge for its …
cardiovascular health. Vast amount of ECG recordings can pose a challenge for its …
[PDF][PDF] Arrhythmia detection from 2-lead ECG using convolutional denoising autoencoders
K Ochiai, S Takahashi, Y Fukazawa - Proc. KDD, 2018 - kdd.org
Cardiac arrhythmia is the cause of death a significant number of deaths. As such, automatic
arrhythmia detection from an electrocardiogram (ECG) is an important research topic. There …
arrhythmia detection from an electrocardiogram (ECG) is an important research topic. There …
A Cloud‐Based Machine Learning Approach to Reduce Noise in ECG Arrhythmias for Smart Healthcare Services
ECG (electrocardiogram) identifies and traces targets and is commonly employed in cardiac
disease detection. It is necessary for monitoring precise target trajectories. Estimations of …
disease detection. It is necessary for monitoring precise target trajectories. Estimations of …
Densely connected convolutional networks for detection of atrial fibrillation from short single-lead ECG recordings
The development of new technology such as wearables that record high-quality single
channel ECG, provides an opportunity for ECG screening in a larger population, especially …
channel ECG, provides an opportunity for ECG screening in a larger population, especially …
[HTML][HTML] An embedded system using convolutional neural network model for online and real-time ECG signal classification and prediction
This paper presents an automatic ECG signal classification system that applied the Deep
Learning (DL) model to classify four types of ECG signals. In the first part of our work, we …
Learning (DL) model to classify four types of ECG signals. In the first part of our work, we …
Automatic parameter acquisition of 12 leads ECG using continuous data processing deep neural network
JW Kim, SM Park, SW Choi - Journal of Biomedical Engineering …, 2020 - koreascience.kr
The deep neural networks (DNN) that can replicate the behavior of the human expert who
recognizes the characteristics of ECG waveform have been developed and studied to …
recognizes the characteristics of ECG waveform have been developed and studied to …
Detecting Abnormal PCG Signals and Extracting Cardiac Information Employing Deep Learning and the Shannon Energy Envelope
M Chowdhury, K Poudel, Y Hu - 2020 IEEE Signal Processing …, 2020 - ieeexplore.ieee.org
Phonocardiogram (PCG) is a computerized system that represents the heart sound
recording. PCG reflects the acoustic behavior of the heart graphically through intensity …
recording. PCG reflects the acoustic behavior of the heart graphically through intensity …
Fast CNN Based Electrocardiogram Signal Quality Assessment Using Fourier Magnitude Spectrum for Resource-Constrained ECG Diagnosis Devices
Automatic assessment of electrocardiogram (ECG) signal quality plays a vital role in
reducing false alarms and improving the trustworthiness of unobtrusive health monitoring …
reducing false alarms and improving the trustworthiness of unobtrusive health monitoring …