Comparing Feature Based Classifiers and Convolutional Neural Networks to Detect Arrhythmia from Short Segments of ECG

Andreotti, Fernando; Carr, Oliver; Pimentel, Marco A. F.; Mahdi, Adam; Vos, Maarten De

doi:10.22489/cinc.2017.360-239

Cited by 111 publications

(125 citation statements)

References 18 publications

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“…With the initialization of variables W 0 and P 0 , we can apply the alternative optimization method to solve Eq. (8). When the value of W is fixed, the optimal P can be obtained by minimizing the following function:…”

Section: B the Iterative Proceduresmentioning

confidence: 99%

“…Once this is done, the similarity matrix W can be optimized by solving (15). We repeat the iterative procedure to update the above variables until the optimization problem (8) converges to the local minimum. The details of the complete optimization procedure are summarized in Algorithm 1.…”

Section: B the Iterative Proceduresmentioning

confidence: 99%

“…Unfortunately, AF is often asymptomatic in the early phase, which induces the difficulty in diagnosing AF in time. Currently, ECG signals [5]- [8] have been successfully utilized in AF diagnosis for clinical applications. However, the acquisition of ECG signals requires specific biomedical equipment, which limits the application of monitoring AF risk in daily life.…”

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confidence: 99%

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Atrial Fibrillation Detection From Face Videos by Fusing Subtle Variations

Shi

Alikhani

et al. 2020

IEEE Trans. Circuits Syst. Video Technol.

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Atrial fibrillation (AF) is one of the most common cardiac arrhythmias, which particularly occurs in the elderly individuals with heart disease. Though AF is often asymptomatic during normal activities, it has huge potential risks for stroke and other severe diseases. Thus, early detection of AF has great importance in the field of public health. Currently, electrocardiography (ECG) is the commonly used measure for the diagnosis of AF, which presents the irregular rhythm of waveform for AF patients. However, the measurement of the ECG signal requires special medical acquisition devices, which are not comfortable for practical monitoring in daily life. In this paper, we explore a very promising algorithm to detect AF from remote face videos by analyzing the color variations of face skin. The main challenge is that the current remote photoplethysmography (rPPG) technique is rather immature, which causes difficulty in extracting accurate pulse signals for describing the cardiac rhythm. To solve this problem, we first utilize various rPPG algorithms to capture pulse rhythms from different regions on the face video. We then investigate biomedical statistical methods to extract suitable features from each pulse signal. Due to the imprecision of video-extracted pulse signals, some traditional physiological features may lose their utility since they were originally proposed for ECG signals. Furthermore, some of them are very susceptible to the influence of noise. Thus, we propose a feature fusion algorithm to select and combine reasonable information from multiple physiological features, which aims to preserve the discriminability of detecting AF in the presence of the noise and outlier disturbances. The experimental results on a real-world database demonstrate the effectiveness of the proposed method in providing useful information for AF detection.

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Section: B the Iterative Proceduresmentioning

confidence: 99%

Section: B the Iterative Proceduresmentioning

confidence: 99%

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confidence: 99%

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Atrial Fibrillation Detection From Face Videos by Fusing Subtle Variations

Shi

Alikhani

et al. 2020

IEEE Trans. Circuits Syst. Video Technol.

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“…At the same time, many other participants of the Challenge also used neural networks [13,14,15,16] as feature detector in addition to their traditional feature extractors. One of them was Andreotti et al [17], who compared their featurebased classifiers to residual neural networks. They concluded that their neural networks outperform their featurebased classifiers, showing the strength of the purely neural network-based approach.…”

Section: Related Workmentioning

confidence: 99%

Strong deep learning baseline for single lead ECG processing

Csaba¹,

Hakkel²,

Goda³

et al. 2019

StuCoSReC. Proceedings of the 2019 6th Student Computer Science Research Conference

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Objective: Atrial fibrillation (AF) is one of the most common serious abnormal heart rhythm conditions, and the number of deaths related to atrial fibrillation has increased by an order of magnitude in the past decades. We aim to create a system, which can provide help for cardiologist, classifying and highlighting important segments in recordings. Approach: In this paper, we propose a novel approach for AF detection using only a deep neural architecture without any traditional feature extractor for real-time automated suggestions of possible cardiac failures that can detect class invariant anomalies in signals recorded by a single channel portable ECG device. Results: Detecting the four categories: Normal, AF, Other and Noisy in terms of the official, F1 metric of hidden dataset maintained by the organizers of PhysioNet Computing in Cardiology Challenge 2017, our proposed algorithm has scored 0.88, 0.80, 0.69, 0.64 points respectively, and 0.79 on average.

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“…Some relevant algorithms for the detection of AF are the ones based on the analysis of the P-wave [10,11], or those that utilize a large set of features obtained from ECGs in an artificial neural network [12][13][14] or in a deep learning approach [15,16]. Furthermore, it is worth mentioning that several recent clinical trials on large populations focusing on AF detection have been carried out [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

A Prototype Framework Design for Assisting the Detection of Atrial Fibrillation Using a Generic Low-Cost Biomedical Sensor

Pérez-Valero

García-Sánchez

Marín

2020

Sensors

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Cardiovascular diseases are the leading cause of death around the world. As a result, low-cost biomedical sensors have been gaining importance in business and research over the last few decades. Their main benefits include their small size, light weight, portability and low power consumption. Despite these advantages, they are not generally used for clinical monitoring mainly because of their low accuracy in data acquisition. In this emerging technological context, this paper contributes by discussing a methodology to help practitioners build a prototype framework based on a low-cost commercial sensor. The resulting application consists of four modules; namely, a digitalization module whose input is an electrocardiograph signal in portable document format (PDF) or joint photographic expert group format (JPEG), a module to further process and filter the digitalized signal, a selectable data calibration module and, finally, a module implementing a classification algorithm to distinguish between individuals with normal sinus rhythms and those with atrial fibrillation. This last module employs our recently published symbolic recurrence quantification analysis (SRQA) algorithm on a time series of RR intervals. Moreover, we show that the algorithm applies to any biomedical low-cost sensor, achieving good results without requiring any calibration of the raw data acquired. In addition, it has been validated with a well-accepted public electrocardiograph (ECG) data base, obtaining 87.65%, 91.84%, and 91.31% in terms of sensitivity, specificity and accuracy, respectively.

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Comparing Feature Based Classifiers and Convolutional Neural Networks to Detect Arrhythmia from Short Segments of ECG

Cited by 111 publications

References 18 publications

Atrial Fibrillation Detection From Face Videos by Fusing Subtle Variations

Atrial Fibrillation Detection From Face Videos by Fusing Subtle Variations

Strong deep learning baseline for single lead ECG processing

A Prototype Framework Design for Assisting the Detection of Atrial Fibrillation Using a Generic Low-Cost Biomedical Sensor

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