A novel compression framework using energy-sensitive QRS complex detection method for a mobile ECG

Wang, Sikai; Pang, Bo; Liu, Ming; Zhang, Xu; Yuan, Fang; Shen, Wentao; Chen, Hongda

doi:10.1007/s11432-018-9838-3

Cited by 5 publications

(2 citation statements)

References 7 publications

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“…The QRS complex wave is the most important part of the cardiac system to determine. The ECG parameters normal values are shown in Table 1 [22], [23], [24].…”

Section: Fig 1 -The Classification Of Health Monitoring Systems [14]mentioning

confidence: 99%

Smart Healthcare for ECG Telemonitoring System

Saeed¹,

Ameen

2021

JSCDM

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Cardiovascular disorders are one of the major causes of sad death among older and middle-aged people. Over the past two decades, health monitoring services have evolved quickly and had the ability to change the way health care is currently provided. However, the most challenging aspect of the mobile and wearable sensor-based human activity recognition pipeline is the extraction of the related features. Feature extraction decreases both computational complexity and time. Deep learning techniques are used for automatic feature learning in a variety of fields, including health, image classification, and, most recently, for the extraction and classification of complex and straightforward human activity recognition in smart health care. This paper reviews the recent state of the art in electrocardiogram (ECG) smart health monitoring systems based on the Internet of things with the machine and deep learning techniques. Moreover, the paper provided possible research and challenges that can help researchers advance state of art in future work.

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“…The QRS complex wave is the most important part of the cardiac system to determine. The ECG parameters normal values are shown in Table 1 [22], [23], [24].…”

Section: Fig 1 -The Classification Of Health Monitoring Systems [14]mentioning

confidence: 99%

Smart Healthcare for ECG Telemonitoring System

Saeed¹,

Ameen

2021

JSCDM

View full text Add to dashboard Cite

show abstract

“…Hence, developing an automatic system for detecting IED can help for diagnosing and saving the lives of epileptic patients and also saves the time of neurophysiologists. The developed system should be multi-modal, low power, flexible, and has a high precision to satisfy the clinicians' requirements by detecting all IED discharges of EEG recording with small false positive detections (i.e., sensitivity above 95% and a false alarm rate below 5%) [4][5][6][7][8]. The challenge is how to tradeoff between the sensitivity and the false alarm rate to improve the system precision.…”

Section: Introductionmentioning

confidence: 99%

Detection of the interictal epileptic discharges based on wavelet bispectrum interaction and recurrent neural network

Sabor

Zhang

et al. 2021

Sci. China Inf. Sci.

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Detection of interictal epileptic discharges (IED) events in the EEG recordings is a critical indicator for detecting and diagnosing epileptic seizures. We propose a key technology to extract the most important features related to epileptic seizures and identifies the IED events based on the interaction between frequencies of EEG with the help of a two-level recurrent neural network. The proposed classification network is trained and validated using the largest publicly available EEG dataset from Temple University Hospital. Experimental results clarified that the interaction between β and β bands, β and γ bands, γ and γ bands, δ and δ bands, θ and α bands, and θ and β bands have a significant effect on detecting the IED discharges. Moreover, the obtained results showed that the proposed technique detects 95.36% of the IED epileptic events with a false-alarm rate of 4.52% and a precision of 87.33% by using only 25 significant features. Furthermore, the proposed system requires only 164 ms for detecting a 1-s IED event which makes it suitable for real-time applications

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A robust QRS detection and accurate R-peak identification algorithm for wearable ECG sensors

Zhao

Wang

et al. 2021

Sci. China Inf. Sci.

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A novel compression framework using energy-sensitive QRS complex detection method for a mobile ECG

Cited by 5 publications

References 7 publications

Smart Healthcare for ECG Telemonitoring System

Smart Healthcare for ECG Telemonitoring System

Detection of the interictal epileptic discharges based on wavelet bispectrum interaction and recurrent neural network

A robust QRS detection and accurate R-peak identification algorithm for wearable ECG sensors

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