Automated real-time method for ventricular heartbeat classification

Ortín, Silvia; Soriano, Miguel C.; Alfaras, Miquel; Mirasso, Claudio R.

doi:10.1016/j.cmpb.2018.11.005

Cited by 26 publications

(29 citation statements)

References 41 publications

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“…Our results are comparable to the state of the art approaches reported in the literature [40][41][42]. Also similar kinds of cross dataset experiments were reported by [44] for beats of ventricular origin resulting in sensitivity and precision metrics comparable to our results, i.e. precision 88.8 for Lead II configuration.…”

Section: Discussionsupporting

confidence: 91%

Robust deep learning pipeline for PVC beats localization

Petryshak

Kachko

Maksymenko

et al. 2021

THC

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BACKGROUND: Premature ventricular contraction (PVC) is among the most frequently occurring types of arrhythmias. Existing approaches for automated PVC identification suffer from a range of disadvantages related to hand-crafted features and benchmarking on datasets with a tiny sample of PVC beats. OBJECTIVE: The main objective is to address the drawbacks described above in the proposed framework, which takes a raw ECG signal as an input and localizes R peaks of the PVC beats. METHODS: Our method consists of two neural networks. First, an encoder-decoder architecture trained on PVC-rich dataset localizes the R peak of both Normal and anomalous heartbeats. Provided R peaks positions, our CardioIncNet model does the delineation of healthy versus PVC beats. RESULTS: We have performed an extensive evaluation of our pipeline with both single- and cross-dataset paradigms on three public datasets. Our approach results in over 0.99 and 0.979 F1-measure on both single- and cross-dataset paradigms for R peaks localization task and above 0.96 and 0.85 F1 score for the PVC beats classification task. CONCLUSIONS: We have shown a method that provides robust performance beyond the beats of Normal nature and clearly outperforms classical algorithms both in the case of a single and cross-dataset evaluation. We provide a Github1 repository for the reproduction of the results.

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Section: Discussionsupporting

confidence: 91%

Robust deep learning pipeline for PVC beats localization

Petryshak

Kachko

Maksymenko

et al. 2021

THC

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“…Containing morphological or statistical features, ECG provides comprehensive information for analyzing and diagnosing cardiovascular diseases (Luz et al, 2016 ; Liang et al, 2020 ). In previous studies, automatic ECG classification has been achieved using machine learning techniques, such as Deep Neural Network (DNN) (Kiranyaz et al, 2016 ; Rahhal et al, 2016 ), Support Vector Machine (SVM) (Zhang et al, 2014 ; Raj et al, 2016 ), and Recurrent Neural Network (RNN) (Alfaras et al, 2019 ; Ortín et al, 2019 ). According to the Association for the Advancement of Medical Instrumentation, there are five classes of ECG type of interest: normal, ventricular, supraventricular, fusion of normal and ventricular, and unknown beats.…”

Section: Wearable Sensorsmentioning

confidence: 99%

Adaptive Extreme Edge Computing for Wearable Devices

et al. 2021

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Wearable devices are a fast-growing technology with impact on personal healthcare for both society and economy. Due to the widespread of sensors in pervasive and distributed networks, power consumption, processing speed, and system adaptation are vital in future smart wearable devices. The visioning and forecasting of how to bring computation to the edge in smart sensors have already begun, with an aspiration to provide adaptive extreme edge computing. Here, we provide a holistic view of hardware and theoretical solutions toward smart wearable devices that can provide guidance to research in this pervasive computing era. We propose various solutions for biologically plausible models for continual learning in neuromorphic computing technologies for wearable sensors. To envision this concept, we provide a systematic outline in which prospective low power and low latency scenarios of wearable sensors in neuromorphic platforms are expected. We successively describe vital potential landscapes of neuromorphic processors exploiting complementary metal-oxide semiconductors (CMOS) and emerging memory technologies (e.g., memristive devices). Furthermore, we evaluate the requirements for edge computing within wearable devices in terms of footprint, power consumption, latency, and data size. We additionally investigate the challenges beyond neuromorphic computing hardware, algorithms and devices that could impede enhancement of adaptive edge computing in smart wearable devices.

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“…While in one hand, the choices are profoundly based on the input features that are considered based on the morphological features garnered from the time domain [8], [19], [20], [29], complex heartbeat representations [21], wavelet transforms [9], [13], [14], [15], [16], [23], [30], or higher order statistics (HOS) [9], [11], [12], [14] and frequency-domain features [11], [12], [21], [22], [31]. In the other dimension, the feature selection models like the independent component analysis (ICA) [23], [30], PSO (particle swarm optimization) [21] and the GA-BPNN (genetic algorithm back propagation neural networks) are used.…”

Section: Related Workmentioning

confidence: 99%

Regression Heuristics by Optimal Tridimensional Features of Electrocardiogram for Arrhythmia Detection

2019

IJEAT

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Computer aided predictive analytics are vital in noncommunicable diseases. In particular, early diagnosis of arrhythmia (heart related disease) is crucial to prevent sudden deaths due to heart failure. The critical context to prevent deaths caused by arrhythmia is early prediction of the arrhythmia scope. The clinical experts widely consider the Electro Cardio Gram (ECG) report as primary parameter to scale the scope of arrhythmia. However, the diagnosis accuracy of clinical experts is highly correlate on their expertise. Unlike the other domains, the sensitivity that is the accuracy in disease-prone is very much crucial in clinical practices. Particularly, the accuracy and sensitivity are more vital in computer-aided heart disease prediction methods. Hence, the recent research contributions are quantifying the possibilities of optimizing machine-learning approaches to achieve significance in computer-aided methods to perform predictive analysis on arrhythmia detection. Regarding this context, this manuscript is defining a Regression Heuristics by Tridimensional Features of the electrocardiogram reports, which has intended to perform arrhythmia prediction. The experimental study evincing the significance of the proposed model that scaled against the contemporary methods.

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Automated real-time method for ventricular heartbeat classification

Cited by 26 publications

References 41 publications

Robust deep learning pipeline for PVC beats localization

Robust deep learning pipeline for PVC beats localization

Adaptive Extreme Edge Computing for Wearable Devices

Regression Heuristics by Optimal Tridimensional Features of Electrocardiogram for Arrhythmia Detection

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