Artificial intelligence-based approach for atrial fibrillation detection using normalised and short-duration time-frequency ECG

Rahul, Jagdeep; Sharma, Lakhan Dev

doi:10.1016/j.bspc.2021.103270

Cited by 54 publications

(19 citation statements)

References 40 publications

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“…Thus, it contributes to the reduction of heart disease-related deaths. Consequently, numerous investigations proposed various categorization methods for arrhythmia [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 34 ]. As shown in Table 5 , a set of previously trained models with a small number of layers was examined, analysed, and compared to the proposed multimodel in terms of accuracy and other criteria [ 10 , 11 , 14 , 15 , 16 , 17 , 34 ].…”

Section: Experimental Study Results and Discussionmentioning

confidence: 99%

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Efficient Lightweight Multimodel Deep Fusion Based on ECG for Arrhythmia Classification

Hammad

Meshoul

Dziwiński

et al. 2022

Sensors

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An arrhythmia happens when the electrical signals that organize the heartbeat do not work accurately. Most cases of arrhythmias may increase the risk of stroke or cardiac arrest. As a result, early detection of arrhythmia reduces fatality rates. This research aims to provide a lightweight multimodel based on convolutional neural networks (CNNs) that can transfer knowledge from many lightweight deep learning models and decant it into one model to aid in the diagnosis of arrhythmia by using electrocardiogram (ECG) signals. Thus, we gained a multimodel able to classify arrhythmia from ECG signals. Our system’s effectiveness is examined by using a publicly accessible database and a comparison to the current methodologies for arrhythmia classification. The results we achieved by using our multimodel are better than those obtained by using a single model and better than most of the previous detection methods. It is worth mentioning that this model produced accurate classification results on small collection of data. Experts in this field can use this model as a guide to help them make decisions and save time.

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Section: Experimental Study Results and Discussionmentioning

confidence: 99%

“…Rahul and Sharma [ 34 ] used bidirectional LSTM for classifying ECG signals as normal or as one type of arrhythmia. They first convert each ECG signal to 2D images.…”

Section: Previous Methods For Arrythmia Classificationmentioning

confidence: 99%

Efficient Lightweight Multimodel Deep Fusion Based on ECG for Arrhythmia Classification

Hammad

Meshoul

Dziwiński

et al. 2022

Sensors

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“…The authors stated that this method outperforms those based on other representations (by STFT, CWT, and Stockwell transform) and other model architectures (CNN, attention-based, etc.). In another report ( Rahul and Sharma, 2022 ), the AFIB detector using STFT representation and biLSTM model slightly outperformed the method using raw ECG (accuracy of 99.84 and 98.85%, respectively).…”

Section: Ecg Analysismentioning

confidence: 97%

Golden Standard or Obsolete Method? Review of ECG Applications in Clinical and Experimental Context

et al. 2022

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Cardiovascular system and its functions under both physiological and pathophysiological conditions have been studied for centuries. One of the most important steps in the cardiovascular research was the possibility to record cardiac electrical activity. Since then, numerous modifications and improvements have been introduced; however, an electrocardiogram still represents a golden standard in this field. This paper overviews possibilities of ECG recordings in research and clinical practice, deals with advantages and disadvantages of various approaches, and summarizes possibilities of advanced data analysis. Special emphasis is given to state-of-the-art deep learning techniques intensely expanded in a wide range of clinical applications and offering promising prospects in experimental branches. Since, according to the World Health Organization, cardiovascular diseases are the main cause of death worldwide, studying electrical activity of the heart is still of high importance for both experimental and clinical cardiology.

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“…As deep learning becomes the popular tool for specific electrocardiogram (ECG) tasks [6] such as disease detection [7,8,9,10], sleep staging [11,12], biometric human identification [13,14], and denoising [15], people start considering learning general representations of ECG using pre-training models [16,17,18,19].…”

Section: Introductionmentioning

confidence: 99%

A Simple Self-Supervised ECG Representation Learning Method via Manipulated Temporal-Spatial Reverse Detection

Zhang¹,

Geng²,

Hong³

2022

Preprint

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Learning representations from electrocardiogram (ECG) serves as a fundamental step for many downstream machine learning-based ECG analysis tasks. However, the learning process is always restricted by lack of high-quality labeled data in reality. Existing methods addressing data deficiency either cannot provide satisfied representations for downstream tasks or require too much effort to construct similar and dissimilar pairs to learn informative representations. In this paper, we propose a straightforward but effective approach to learn ECG representations. Inspired by the temporal and spatial characteristics of ECG, we flip the original signals horizontally, vertically, and both horizontally and vertically. The learning is then done by classifying the four types of signals including the original one. To verify the effectiveness of the proposed temporal-spatial (T-S) reverse detection method, we conduct a downstream task to detect atrial fibrillation (AF) which is one of the most common ECG tasks. The results show that the ECG representations learned with our method lead to remarkable performances on the downstream task. In addition, after exploring the representational feature space and investigating which parts of the ECG signal contribute to the representations, we conclude that the temporal reverse is more effective than the spatial reverse for learning ECG representations.

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Artificial intelligence-based approach for atrial fibrillation detection using normalised and short-duration time-frequency ECG

Cited by 54 publications

References 40 publications

Efficient Lightweight Multimodel Deep Fusion Based on ECG for Arrhythmia Classification

Efficient Lightweight Multimodel Deep Fusion Based on ECG for Arrhythmia Classification

Golden Standard or Obsolete Method? Review of ECG Applications in Clinical and Experimental Context

A Simple Self-Supervised ECG Representation Learning Method via Manipulated Temporal-Spatial Reverse Detection

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