Monitoring significant ST changes through deep learning

Xiao, Ran; Xu, Yuan; Pelter, Michele M.; Fidler, Richard; Badilini, Fabio; Mortara, David; Hu, Xiao

doi:10.1016/j.jelectrocard.2018.07.026

Cited by 22 publications

(9 citation statements)

References 17 publications

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“…A possible solution to overcome this limitation is transfer learning where the task is to fine-tune a sophisticated pre-trained DL. 66 The required number of layers and the complexity of finetuning depend on specific applications. 67 In ref.…”

Section: Ppg Representationsmentioning

confidence: 99%

Photoplethysmography based atrial fibrillation detection: a review

et al. 2020

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Atrial fibrillation (AF) is a cardiac rhythm disorder associated with increased morbidity and mortality. It is the leading risk factor for cardioembolic stroke and its early detection is crucial in both primary and secondary stroke prevention. Continuous monitoring of cardiac rhythm is today possible thanks to consumer-grade wearable devices, enabling transformative diagnostic and patient management tools. Such monitoring is possible using low-cost easy-to-implement optical sensors that today equip the majority of wearables. These sensors record blood volume variations-a technology known as photoplethysmography (PPG)-from which the heart rate and other physiological parameters can be extracted to inform about user activity, fitness, sleep, and health. Recently, new wearable devices were introduced as being capable of AF detection, evidenced by large prospective trials in some cases. Such devices would allow for early screening of AF and initiation of therapy to prevent stroke. This review is a summary of a body of work on AF detection using PPG. A thorough account of the signal processing, machine learning, and deep learning approaches used in these studies is presented, followed by a discussion of their limitations and challenges towards clinical applications.npj Digital Medicine (2020) 3:3 ; https://doi.org/10.1038/s41746-019-0207-9 PHOTOPLETHYSMOGRAPHY PPG signal PPG waveform is generated during a cardiac cycle and typically measured at a peripheral site. Therefore, it is essentially a pulse pressure waveform that originates from the heart contraction and propagates through the vascular tree. As blood flow is controlled

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Section: Ppg Representationsmentioning

confidence: 99%

Photoplethysmography based atrial fibrillation detection: a review

et al. 2020

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“…The QRS complex has been detected using variational mode decomposition (VMD), K-Nearest Neighbor (KNN), Naive Bayes (NB) and Support Vector Machine (SVM) based approaches in [61], [62] where the best Sensitivity of 99.93% was achieved with 12-lead ECG data and 99.79% with single-lead ECG. On the other hand, ST-segment and its changes have been detected using Decision Tree (DT) [63] and Google's Inception based 2-D Convolutional Neural Network (CNN) [64], but didn't perform well in Sensitivity as compared to [41] which employed the ensemble NN-based isoelectric level detector. These different methods are summarized in Table 4 with reported performance metrics of Sensitivity (sen), Specificity (spe), Positive Predictive Value (ppv), F1-score (F1), Error (err), Root Mean Square Error (rmse) and Accuracy (acc).…”

Section: ) Machine Learning Approachesmentioning

confidence: 99%

Stages-Based ECG Signal Analysis From Traditional Signal Processing to Machine Learning Approaches: A Survey

et al. 2020

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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 detect and prevent life threatening cardiac circumstances. Traditional signal processing methods, machine learning and its subbranches, such as deep learning, are popular techniques for analyzing and classifying the ECG signal and mainly to develop applications for early detection and treatment of cardiac conditions and arrhythmias. A detailed literature survey regarding ECG signal analysis is presented in this paper. We first introduce a stages-based model for ECG signal analysis where a survey of ECG analysis related work is then presented in the form of this stage-based process model. The model describes both traditional time/frequency-domain and advanced machine learning techniques reported in the published literature at every stage of analysis, starting from ECG data acquisition to its classification for both simulations and real-time monitoring systems. We present a comprehensive literature review of real-time ECG signal acquisition, prerecorded clinical ECG data, ECG signal processing and denoising, detection of ECG fiducial points based on feature engineering and ECG signal classification along with comparative discussions among the reviewed studies. This study also presents a detailed literature review of ECG signal analysis and feature engineering for ECG-based body sensor networks in portable and wearable ECG devices for real-time cardiac status monitoring. Additionally, challenges and limitations are discussed and tools for research in this field as well as suggestions for future work are outlined. INDEX TERMS ECG analysis, cardiac arrhythmias, QRS and ST detection, ECG classification, deep learning.

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“…b There is a wide variability in results reporting. The results of [77] is for ventricular/supraventricular ectopic beats, [78] is for three types of arrhythmias, [82] is for five types of arrhythmias, [84] report precision, [90] report SNR and multiple results depending on added noise, the result of [91] is without noise in a noise resilience study, [92] report AUC, [93] report multiple accuracies for supraventricular/ventricular ectopic beats, [95] report sensitivity and specificity, [101] report results for two cases.…”

Section: A Electrocardiogrammentioning

confidence: 99%

“…4) Other tasks with public databases: ECG beat classification was also performed by a number of studies using public databases. In [92] the authors finetuned a Inception v3 trained on ImageNet, using signals from LTSTDB for classifying ST events. The training samples were over 500000 segments of ST and non-ST ECG signals with ten second duration that were converted to images.…”

Section: A Electrocardiogrammentioning

confidence: 99%

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Deep Learning in Cardiology

Bizopoulos

Koutsouris

2019

IEEE Rev. Biomed. Eng.

147

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The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.

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Monitoring significant ST changes through deep learning

Cited by 22 publications

References 17 publications

Photoplethysmography based atrial fibrillation detection: a review

Photoplethysmography based atrial fibrillation detection: a review

Stages-Based ECG Signal Analysis From Traditional Signal Processing to Machine Learning Approaches: A Survey

Deep Learning in Cardiology

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