Feature fusion for imbalanced ECG data analysis

Lü, Wei; Hou, Honghui; Chu, Jinghui

doi:10.1016/j.bspc.2017.11.010

Cited by 77 publications

(30 citation statements)

References 24 publications

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“…transformed five types of heartbeats' signals into time-frequency spectrograms and then trained a 2D-CNN for classifying arrhythmia types. Lu et al[18] transformed the 1D signals into 2D images by joining the dots of 1D signals. Ji et al[19] also used a 1D signal, converted all signals into 2D, and used R-CNN for ECG classification.…”

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confidence: 99%

Cardiac Disorder Classification by Electrocardiogram Sensing Using Deep Neural Network

2021

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Cardiac disease is the leading cause of death worldwide. Cardiovascular diseases can be prevented if an effective diagnostic is made at the initial stages. The ECG test is referred to as the diagnostic assistant tool for screening of cardiac disorder. The research purposes of a cardiac disorder detection system from 12-lead-based ECG Images. The healthcare institutes used various ECG equipment that present results in nonuniform formats of ECG images. The research study proposes a generalized methodology to process all formats of ECG. Single Shoot Detection (SSD) MobileNet v2-based Deep Neural Network architecture was used to detect cardiovascular disease detection. The study focused on detecting the four major cardiac abnormalities (i.e., myocardial infarction, abnormal heartbeat, previous history of MI, and normal class) with 98% accuracy results were calculated. The work is relatively rare based on their dataset; a collection of 11,148 standard 12-lead-based ECG images used in this study were manually collected from health care institutes and annotated by the domain experts. The study achieved high accuracy results to differentiate and detect four major cardiac abnormalities. Several cardiologists manually verified the proposed system’s accuracy result and recommended that the proposed system can be used to screen for a cardiac disorder.

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confidence: 99%

Cardiac Disorder Classification by Electrocardiogram Sensing Using Deep Neural Network

2021

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“…The clustering method (classifying engine) can employ one of several proposed methods such as linear discriminant (LD) [ 43 ], AdaBoost [ 44 ], multilayer perceptron (MLP) [ 45 , 46 ], genetic algorithm-back propagation neural network (GA-BPNN) [ 47 ], convolutional neural networks (CNN) [ 48 , 49 ], and support vector machine (SVM) [ 50 ]. Otherwise, an ensemble of classifiers may be applied to integrate the outcome of multiple classifiers working with feature subsets [ 51 , 52 , 53 ].…”

Section: Proposed Methodology Of Non-uniform Ecg Interpretationmentioning

confidence: 99%

Diagnostic Interpretation of Non-Uniformly Sampled Electrocardiogram

Augustyniak

2021

Sensors

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We present a set of three fundamental methods for electrocardiogram (ECG) diagnostic interpretation adapted to process non-uniformly sampled signal. The growing volume of ECGs recorded daily all over the world (roughly estimated to be 600 TB) and the expectance of long persistence of these data (on the order of 40 years) motivated us to challenge the feasibility of medical-grade diagnostics directly based on arbitrary non-uniform (i.e., storage-efficient) ECG representation. We used a refined time-independent QRS detection method based on a moving shape matching technique. We applied a graph data representation to quantify the similarity of asynchronously sampled heartbeats. Finally, we applied a correlation-based non-uniform to time-scale transform to get a multiresolution ECG representation on a regular dyadic grid and to find precise P, QRS and T wave delimitation points. The whole processing chain was implemented and tested with MIT-BIH Database (probably the most referenced cardiac database) and CSE Multilead Database (used for conformance testing of medical instruments) signals arbitrarily sampled accordingly to a perceptual model (set for variable sampling frequency of 100–500 Hz, compression ratio 3.1). The QRS detection shows an accuracy of 99.93% with false detection ratio of only 0.18%. The classification shows an accuracy of 99.27% for 14 most frequent MIT-BIH beat types and 99.37% according to AAMI beat labels. The wave delineation shows cumulative (i.e., sampling model and non-uniform processing) errors of: 9.7 ms for P wave duration, 3.4 ms for QRS, 6.7 ms for P-Q segment and 17.7 ms for Q-T segment, all the values being acceptable for medical-grade interpretive software.

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“…As the feature extraction process, which plays a critical role in ECG signal classification, is automated with convolutional neural networks, the use of CNN has become widespread in this field. These networks are used to classify patient-specific beats [6,38] and long duration ECG signals containing multiple rhythm classes [39,40,66], to detect different interval ECG segments [41], atrial fibrillation [41][42][43][44][45][46][47][48], and different types of ECG beats [8,49].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Application of deep learning techniques for heartbeats detection using ECG signals-analysis and review

Murat

Yıldırım

Talo

et al. 2020

Computers in Biology and Medicine

240

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Deep learning models have become a popular mode to classify electrocardiogram (ECG) data.Investigators have used a variety of deep learning techniques for this application. Herein, a detailed examination of deep learning methods for ECG arrhythmia detection is provided. Approaches used by investigators are examined, and their contributions to the field are detailed. For this purpose, journal papers have been surveyed according to the methods used. In addition, various deep learning models and experimental studies are described and discussed. A five-class ECG dataset containing 100,022 beats was then utilized for further analysis of deep learning techniques. The constructed models were examined with this dataset, and results are presented. This study therefore provides information concerning deep learning approaches used for arrhythmia classification, and suggestions for further research in this area.

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Feature fusion for imbalanced ECG data analysis

Cited by 77 publications

References 24 publications

Cardiac Disorder Classification by Electrocardiogram Sensing Using Deep Neural Network

Cardiac Disorder Classification by Electrocardiogram Sensing Using Deep Neural Network

Diagnostic Interpretation of Non-Uniformly Sampled Electrocardiogram

Application of deep learning techniques for heartbeats detection using ECG signals-analysis and review

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