Compressed sensing approach for physiological signals: A review
The immense progress in physiological signal acquisition and processing in health
monitoring allowed a better understanding of patient disease detection and diagnosis. With …
monitoring allowed a better understanding of patient disease detection and diagnosis. With …
Review on compressive sensing algorithms for ECG signal for IoT based deep learning framework
SS Kumar, P Ramachandran - Applied Sciences, 2022 - mdpi.com
Nowadays, healthcare is becoming very modern, and the support of Internet of Things (IoT)
is inevitable in a personal healthcare system. A typical personal healthcare system acquires …
is inevitable in a personal healthcare system. A typical personal healthcare system acquires …
An effective arrhythmia classification via ECG signal subsampling and mutual information based subbands statistical features selection
S Mian Qaisar, SF Hussain - Journal of Ambient Intelligence and …, 2023 - Springer
The deployment of wireless health wearables is increasing in the framework of mobile
health monitoring. The power and processing efficiencies with data compression are key …
health monitoring. The power and processing efficiencies with data compression are key …
A novel approach for coronary artery disease diagnosis using hybrid particle swarm optimization based emotional neural network
Coronary artery disease (CAD) can cause serious conditions such as severe heart attack,
heart failure, and angina in patients with cardiovascular problems. These conditions may be …
heart failure, and angina in patients with cardiovascular problems. These conditions may be …
Cloud-based ECG monitoring using event-driven ECG acquisition and machine learning techniques
S Mian Qaisar, A Subasi - Physical and Engineering Sciences in Medicine, 2020 - Springer
An approach is proposed for the detection of chronic heart disorders from the
electrocardiogram (ECG) signals. It utilizes an intelligent event-driven ECG signal …
electrocardiogram (ECG) signals. It utilizes an intelligent event-driven ECG signal …
ECG signal denoising and features extraction using unbiased FIR smoothing
C Lastre-Domínguez, YS Shmaliy… - BioMed research …, 2019 - Wiley Online Library
Methods of the electrocardiography (ECG) signal features extraction are required to detect
heart abnormalities and different kinds of diseases. However, different artefacts and …
heart abnormalities and different kinds of diseases. However, different artefacts and …
Arrhythmia diagnosis by using level-crossing ECG sampling and sub-bands features extraction for mobile healthcare
S Mian Qaisar, S Fawad Hussain - Sensors, 2020 - mdpi.com
Mobile healthcare is an emerging technique for clinical applications. It is usually based on
cloud-connected biomedical implants. In this context, a novel solution is presented for the …
cloud-connected biomedical implants. In this context, a novel solution is presented for the …
ECG compression using wavelet-based compressed sensing with prior support information
Electrocardiogram (ECG) signal compression is a vital signal processing area, especially
with the growing usage of wireless body sensor networks (WBSN). ECG signals need to be …
with the growing usage of wireless body sensor networks (WBSN). ECG signals need to be …
Deep compressive sensing on ECG signals with modified inception block and LSTM
J Hua, J Rao, Y Peng, J Liu, J Tang - Entropy, 2022 - mdpi.com
In practical electrocardiogram (ECG) monitoring, there are some challenges in reducing the
data burden and energy costs. Therefore, compressed sensing (CS) which can conduct …
data burden and energy costs. Therefore, compressed sensing (CS) which can conduct …
A novel ECG signal compression using wavelet and discrete anamorphic stretch transforms
R Thilagavathy, B Venkataramani - Biomedical Signal Processing and …, 2022 - Elsevier
In this paper, a one-dimensional complex Discrete Anamorphic Stretch Transform (DAST) is
proposed for precompression of the ECG signal. Three 1D kernels (Linear, Sublinear, and …
proposed for precompression of the ECG signal. Three 1D kernels (Linear, Sublinear, and …