Stages-based ECG signal analysis from traditional signal processing to machine learning approaches: A survey
M Wasimuddin, K Elleithy, AS Abuzneid… - IEEE …, 2020 - ieeexplore.ieee.org
Electrocardiogram (ECG) gives essential information about different cardiac conditions of
the human heart. Its analysis has been the main objective among the research community to …
the human heart. Its analysis has been the main objective among the research community to …
An overview on state-of-the-art electrocardiogram signal processing methods: Traditional to AI-based approaches
VA Ardeti, VR Kolluru, GT Varghese… - Expert Systems with …, 2023 - Elsevier
Over the last decade, cardiovascular diseases (CVD's) are the leading cause of death
globally. Early prediction of CVD's can help in reducing the complications of high-risk …
globally. Early prediction of CVD's can help in reducing the complications of high-risk …
ENCASE: An ENsemble ClASsifiEr for ECG classification using expert features and deep neural networks
S Hong, M Wu, Y Zhou, Q Wang… - 2017 Computing in …, 2017 - ieeexplore.ieee.org
We propose ENCASE to combine expert features and DNNs (Deep Neural Networks)
together for ECG classification. We first explore and implement expert features from …
together for ECG classification. We first explore and implement expert features from …
A novel low-latency and energy-efficient task scheduling framework for internet of medical things in an edge fog cloud system
In healthcare, there are rapid emergency response systems that necessitate real-time
actions where speed and efficiency are critical; this may suffer as a result of cloud latency …
actions where speed and efficiency are critical; this may suffer as a result of cloud latency …
[HTML][HTML] Industrial quality healthcare services using Internet of Things and fog computing approach
Healthcare in general refers to services provided for preservation or enhancement of health
through anticipation, finding, treatment, recovery, or healing of disease, illness, injury, or any …
through anticipation, finding, treatment, recovery, or healing of disease, illness, injury, or any …
Combining deep neural networks and engineered features for cardiac arrhythmia detection from ECG recordings
Objective: We aim to combine deep neural networks and engineered features (hand-crafted
features based on medical domain knowledge) for cardiac arrhythmia detection from short …
features based on medical domain knowledge) for cardiac arrhythmia detection from short …
Multiclass ECG signal analysis using global average-based 2-D convolutional neural network modeling
Cardiovascular diseases have been reported to be the leading cause of mortality across the
globe. Among such diseases, Myocardial Infarction (MI), also known as “heart attack”, is of …
globe. Among such diseases, Myocardial Infarction (MI), also known as “heart attack”, is of …
Machine learning-data mining integrated approach for premature ventricular contraction prediction
Cardiac arrhythmias impose a significant burden on the healthcare environment due to the
increasing ratio of mortality worldwide. Arrhythmia and abnormal ECG heartbeat are the …
increasing ratio of mortality worldwide. Arrhythmia and abnormal ECG heartbeat are the …
A unique feature extraction using MRDWT for automatic classification of abnormal heartbeat from ECG big data with multilayered probabilistic neural network classifier
HM Rai, K Chatterjee - Applied Soft Computing, 2018 - Elsevier
This paper employs a novel adaptive feature extraction techniques of electrocardiogram
(ECG) signal for detection of cardiac arrhythmias using multiresolution discrete wavelet …
(ECG) signal for detection of cardiac arrhythmias using multiresolution discrete wavelet …
Recognizing real time ECG anomalies using Arduino, AD8232 and Java
Living being remains alive, only, as long as its heart is functional. Hence, proper functioning
of Heart is essential. The functioning of Heart can be checked by continuous monitoring of …
of Heart is essential. The functioning of Heart can be checked by continuous monitoring of …