Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks

By Serkan Kiranyaz, Turker Ince, Moncef Gabbouj. IEEE Transactions on Biomedical Engineering, 2016

1,648 citations recorded by the source corpus.

Subfields: Cardiology and Cardiovascular Medicine

View Paper: https://www.semanticscholar.org/paper/6852ff4b312c0ab3fc11728f6d868a5bac8e83be

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In Cardiology and Cardiovascular Medicine

Citations in the corpus

A Review of Vibration-Based Damage Detection in Civil Structures: From Traditional Methods to Machine Learning and Deep Learning Applications (2020, methodology): “A detailed description of BP in CNNs can be found in [90], [185] and [186].”
ECG Heartbeat Classification: A Deep Transferable Representation (2018, background): “An end-to-end deep learning framework allows the machine to learn the features that are best suited to the specific task that it is dedicated to carry out [8], [9], [10].”
Artificial Intelligence in Cardiology. (2018, background): “Tensor factorization Natural incorporation of multimodal and multidimensional data Modest number of applications thus far in published cardiovascular reports Choice of factorization algorithm is crucial for results Subtyping of congestive heart failure with preserved ejection fraction (34)”
LSTM-Based ECG Classification for Continuous Monitoring on Personal Wearable Devices (2018, methodology): “In [19], a heartbeat trio is fed into the network in order to capture the effect of nearby heartbeats in classifying the current heartbeat.”
Deep-ECG: Convolutional Neural Networks for ECG biometric recognition (2019, methodology): “…have considered the of deep learning strategies for ECG analysis, on the classiﬁcation of heartbeats in healthy non-healthy [30] using techniques such as CNNs [24, autoencoders [34], or deep belief networks [18]. of our knowledge, only the work in [11] uses a deep technique for ECG biometrics.”
Automatic Sleep Stage Scoring with Single-Channel EEG Using Convolutional Neural Networks (2016, methodology): “…we present a CNN architecture which we developed for automatic sleep stage scoring using a single channel of EEG. Sleep is central to human health, and the health consequences of reduced sleep, abnormal sleep patterns or desynchronized circadian rhythms can be emotional, cognitive, or somatic [30].”
Deep learning for healthcare applications based on physiological signals: A review (2018, background): “, 2016 [147] Heart beat classification CNN MIT/BIH arrhythmia DB [129] accuracy: 99%”
Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks (2016, positive): “for the purpose of ECG classification and anomaly detection [32] and exhibit a superior performance in terms of both ac-”
Deep Learning on 1-D Biosignals: a Taxonomy-based Survey (2018, background): “, [60] obtained an average accuracy of 99.”