Towards End-to-End ECG Classification With Raw Signal Extraction and Deep Neural Networks

Xu, Sean Shensheng; Mak, Man‐Wai; Cheung, Chun Shun

doi:10.1109/jbhi.2018.2871510

Cited by 179 publications

(92 citation statements)

References 30 publications

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“…In general, ECG records include long durations (i.e., several hours or days) of heart activity samples, as needed for detecting and analyzing arrhythmia [3] . This task can become time-consuming, tedious, subjective, and costly, because it requires the assistance of trained experts [4, 5] . Therefore, enhanced fully automated computer-aided diagnosis systems (CADs) with high accuracy can be feasible and even essential solutions to assist clinical experts during the analysis process [6, 7] .…”

Section: Introductionmentioning

confidence: 99%

Accurate deep neural network model to detect cardiac arrhythmia on more than 10,000 individual subject ECG records

Yıldırım

Talo

Ciaccio

et al. 2020

Computer Methods and Programs in Biomedicine

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Highlights DNN model is proposed to detect arrhythmia. More than 10,000 individual subject ECG records subject records are used. Two different scenarios are employed: (i) reduced rhythms (seven rhythm types) and (ii) merged rhythms (four rhythm types). Achieved 92.24% and 96.13% accuracies for the reduced and merged rhythm classes, respectively. System can aid cardiologists in the accurate detection of arrhythmia accurately.

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Section: Introductionmentioning

confidence: 99%

Accurate deep neural network model to detect cardiac arrhythmia on more than 10,000 individual subject ECG records

Yıldırım

Talo

Ciaccio

et al. 2020

Computer Methods and Programs in Biomedicine

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“…Among the several standard ECG waves, the QRS complex in the center of an ECG cycle is the most visually distinct part. The correct detection of the R-peak in the QRS complex is the base of the following interpretive analysis such as heart rate extraction and heart rate variability analysis in disease diagnosis, well-being tracking as well as in research studies where autonomic nervous system activities are observed [8,30,35].…”

Section: Introductionmentioning

confidence: 99%

Robust ECG R-peak detection using LSTM

Laitala

Jiang

Syrjälä

et al. 2020

Proceedings of the 35th Annual ACM Symposium on Applied Computing

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Detecting QRS complexes or R-peaks from the electrocardiogram (ECG) is the basis for heart rate determination and heart rate variability analysis. Over the years, multiple different methods have been proposed as solutions to this problem. Vast majority of the proposed methods are traditional rule based algorithms that are vulnerable to noise. We propose a new R-peak detection method that is based on the Long Short-Term Memory (LSTM) network. LSTM networks excel at temporal modelling tasks that include long-term dependencies, making it suitable for ECG analysis. Additionally, we propose data generator for creating noisy ECG data that is used to train the robust R-peak detector. Our initial testing shows that the proposed method outperforms traditional algorithms while the greatest competitive edge is achieved with the noisy ECG signals.

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“…By processing not only feature extraction but also recognition on node, the amount of transmission data can be minimized. Machine learning approaches using Support Vector Machine (SVM) [19] and neural networks [57] are usually employed.…”

Section: Machine Learningmentioning

confidence: 99%

Recent Progress of Biomedical Processor SoC for Wearable Healthcare Application: A Review

Yoshimoto

Izumi

2019

IEICE Trans. Electron.

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This paper surveys advances in biomedical processor SoC technology for healthcare application and reviews state-of-the-art architecture and circuits used in SoC integration. Particularly, this paper categorizes and describes techniques for improving power efficiency in communication, computation, and sensing. Additionally, it surveys accuracy enhancement techniques for bio-signal measurement and recognition. Finally, we have discussed the potential new directions for development as well as research.

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Towards End-to-End ECG Classification With Raw Signal Extraction and Deep Neural Networks

Cited by 179 publications

References 30 publications

Accurate deep neural network model to detect cardiac arrhythmia on more than 10,000 individual subject ECG records

Accurate deep neural network model to detect cardiac arrhythmia on more than 10,000 individual subject ECG records

Robust ECG R-peak detection using LSTM

Recent Progress of Biomedical Processor SoC for Wearable Healthcare Application: A Review

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