Detection of Atrial Fibrillation from Single Lead ECG Signal Using Multirate Cosine Filter Bank and Deep Neural Network

Ghosh, Samit Kumar; Tripathy, Rajesh Kumar; Paternina, Mario R. Arrieta; Arrieta, Juan J.; Zamora-Méndez, Alejandro; Naik, Ganesh R.

doi:10.1007/s10916-020-01565-y

Cited by 49 publications

(34 citation statements)

References 46 publications

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“…The results showed that the method obtains 98.27% accuracy, 97.77% Sen and 98.67% Spe. Ghosh et al [48] used the multi-rate cosine filter group structure to estimate the coefficient of the ethos, and calculated the Fractional norm (FN) characteristic from the coefficient extracted from the subband. They then used the Hierarchical Extreme Learning Machine (H-ELM) to perform AF detection from FN characteristics.…”

Section: Discussionmentioning

confidence: 99%

Atrial Fibrillation Identification With PPG Signals Using a Combination of Time-Frequency Analysis and Deep Learning

Cheng

Chen

et al. 2020

IEEE Access

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Atrial fibrillation (AF) is the most common persistent arrhythmia and is likely to cause strokes and damage to heart function in patients. Electrocardiogram (ECG) is the gold standard for detecting AF. However, ECGs have short boards with short monitoring cycles and problems with gathering. It is also difficult to detect a burst AF through ECG. In contrast, photoplethysmography (PPG) is easy to perform and suitable for long-term monitoring. In this study, we propose a method that combines timefrequency analysis with deep learning and identifies AF based on PPG. The advantage of the method is that there is no need for the noise filtering and feature extraction of PPG, and it has a high generalization capability. The data for the experiment came from three publicly accessible databases. The first part of the experimental method uses data augmentation to convert the 10 s PPG segment into a time-frequency chromatograph by means of time-frequency analysis. The second part inputs the chromatograph into a hybrid framework that combines a convolutional neural network (CNN) and long short-term memory (LSTM) for AF/nonAF classification. The experimental results show that the method has a high classification accuracy, sensitivity, specificity, and F1 score, which are equal to 98.21%, 98.00%, 98.07% and 98.13%, respectively. The area under the receiver operating characteristic curve (AUC) is 0.9959. The model we propose not only aids doctors in diagnosing AF but also provides a method for identifying AF through portable wearable devices. INDEX TERMS Atrial fibrillation, photoplethysmography (PPG), time-frequency analysis, convolutional neural networks (CNN), long short-term memory (LSTM).

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Section: Discussionmentioning

confidence: 99%

Atrial Fibrillation Identification With PPG Signals Using a Combination of Time-Frequency Analysis and Deep Learning

Cheng

Chen

et al. 2020

IEEE Access

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“…AF diagnosis is typically ECG-based and is usually made by a cardiologist, possibly supported by computerized applications [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ], in hospitals or in clinical facilities. However, traditional medical ECG devices, even when used out-of-the-hospital (such as the Holter ECG recorders), are coupled to a limited amount of people, who are symptomatic or have cryptogenic stroke and, hence, for whom there is an indication for long-term monitoring.…”

Section: Introductionmentioning

confidence: 99%

“…However, in order to be useful in the preventive diagnosis of AF, they have to be associated with a reliable diagnostic software. As a result, several algorithms for automatic detection of AF have been proposed in the literature [ 5 , 6 , 7 , 8 , 9 ], several of which are based on machine and deep learning approaches [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ]. Most of them claim very high performances but, when critically analyzed, show some common limitations.…”

Section: Introductionmentioning

confidence: 99%

“…Most of them claim very high performances but, when critically analyzed, show some common limitations. Firstly, performances of some algorithms for AF identification have been tested only against sinus rhythm [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 13 , 14 , 15 ], without considering the main confounders that are the level of noise affecting ECGs and the presence of other kinds of arrhythmias [ 12 , 16 , 17 , 18 ]. Secondly, most algorithms only rely on HRV to identify AF [ 5 , 6 , 7 , 8 , 9 , 10 , 13 , 14 ], despite high HRV being also associated with many other arrhythmias (not AF-specific) [ 19 , 20 ] and AF being also associated with absence of electrocardiographic P wave and presence of electrocardiographic F waves.…”

Section: Introductionmentioning

confidence: 99%

“…Secondly, most algorithms only rely on HRV to identify AF [ 5 , 6 , 7 , 8 , 9 , 10 , 13 , 14 ], despite high HRV being also associated with many other arrhythmias (not AF-specific) [ 19 , 20 ] and AF being also associated with absence of electrocardiographic P wave and presence of electrocardiographic F waves. Finally, some algorithms have been tested only on ECGs recorded by traditional medical devices [ 11 , 12 , 13 , 14 , 15 , 16 , 18 ] and not by modern wearable or portable devices; thus, their applicability to the latter remains to be demonstrated.…”

Section: Introductionmentioning

confidence: 99%

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Artificial Neural Network for Atrial Fibrillation Identification in Portable Devices

Marinucci

Sbrollini

Marcantoni

et al. 2020

Sensors

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Atrial fibrillation (AF) is a common cardiac disorder that can cause severe complications. AF diagnosis is typically based on the electrocardiogram (ECG) evaluation in hospitals or in clinical facilities. The aim of the present work is to propose a new artificial neural network for reliable AF identification in ECGs acquired through portable devices. A supervised fully connected artificial neural network (RSL_ANN), receiving 19 ECG features (11 morphological, 4 on F waves and 4 on heart-rate variability (HRV)) in input and discriminating between AF and non-AF classes in output, was created using the repeated structuring and learning (RSL) procedure. RSL_ANN was created and tested on 8028 (training: 4493; validation: 1125; testing: 2410) annotated ECGs belonging to the “AF Classification from a Short Single Lead ECG Recording” database and acquired with the portable KARDIA device by AliveCor. RSL_ANN performance was evaluated in terms of area under the curve (AUC) and confidence intervals (CIs) of the received operating characteristic. RSL_ANN performance was very good and very similar in training, validation and testing datasets. AUC was 91.1% (CI: 89.1–93.0%), 90.2% (CI: 86.2–94.3%) and 90.8% (CI: 88.1–93.5%) for the training, validation and testing datasets, respectively. Thus, RSL_ANN is a promising tool for reliable identification of AF in ECGs acquired by portable devices.

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Automated Detection of Myocardial Infarction with Scalogram Technique and Deep Convolutional Neural Network

Mandal,

Mondal,

Roy

2024

Studies in Autonomic, Data-Driven and Industrial Computing

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Detection of Atrial Fibrillation from Single Lead ECG Signal Using Multirate Cosine Filter Bank and Deep Neural Network

Cited by 49 publications

References 46 publications

Atrial Fibrillation Identification With PPG Signals Using a Combination of Time-Frequency Analysis and Deep Learning

Atrial Fibrillation Identification With PPG Signals Using a Combination of Time-Frequency Analysis and Deep Learning

Artificial Neural Network for Atrial Fibrillation Identification in Portable Devices

Automated Detection of Myocardial Infarction with Scalogram Technique and Deep Convolutional Neural Network

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