A New Automated Signal Quality-Aware ECG Beat Classification Method for Unsupervised ECG Diagnosis Environments

Satija, Udit; Ramkumar, Barathram; Manikandan, M. Sabarimalai

doi:10.1109/jsen.2018.2877055

Cited by 88 publications

(33 citation statements)

References 43 publications

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“…Although the electricity amount is, in fact, very small, it can be picked up reliably with ECG electrodes attached to the skin (in microvolts, or uV) [ 2 ]. ECG signals contain no less than two critical pieces of statistics, including correlated to biomedicine’s healthiness [ 3 , 4 , 5 ] and associated with personal credentials or biometrics [ 6 , 7 , 8 ]. As a result of its easiness, several ECG categorizations processes have been established, counting manuals methods [ 9 , 10 ] and machine learning approaches [ 11 , 12 , 13 , 14 , 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Deep CNN Model for Abnormal Arrhythmia Detection Based on Cardiac ECG Signal

Ullah

Rehman

et al. 2021

Sensors

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Electrocardiogram (ECG) signals play a vital role in diagnosing and monitoring patients suffering from various cardiovascular diseases (CVDs). This research aims to develop a robust algorithm that can accurately classify the electrocardiogram signal even in the presence of environmental noise. A one-dimensional convolutional neural network (CNN) with two convolutional layers, two down-sampling layers, and a fully connected layer is proposed in this work. The same 1D data was transformed into two-dimensional (2D) images to improve the model’s classification accuracy. Then, we applied the 2D CNN model consisting of input and output layers, three 2D-convolutional layers, three down-sampling layers, and a fully connected layer. The classification accuracy of 97.38% and 99.02% is achieved with the proposed 1D and 2D model when tested on the publicly available Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database. Both proposed 1D and 2D CNN models outperformed the corresponding state-of-the-art classification algorithms for the same data, which validates the proposed models’ effectiveness.

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Section: Introductionmentioning

confidence: 99%

A Hybrid Deep CNN Model for Abnormal Arrhythmia Detection Based on Cardiac ECG Signal

Ullah

Rehman

et al. 2021

Sensors

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“…There are at least two types of important information contained in the ECG signal, including those related to health or biomedical [2][3][4] and those related to the person identification or biometrics [5][6][7]. Due to its convenience, many ECG classification algorithms have been developed, including handcraft [4,8,9] and machine learning [10][11][12][13][14][15] methods. The handcraft method is rather difficult to utilize on non-stationary signals, such as ECG, while machine learning methods normally require high computational resources.…”

Section: Introductionmentioning

confidence: 99%

“…Many researches have been conducted on the implementation of handcraft techniques, including the extraction of time-based ECG features using Fourier [8] and wavelet [4,9] transforms. Both Fourier and wavelet transform can be used for ECG beats detection (QRS detection), as well as feature extraction, such as R-peak, RR-interval, T-wave region, and QT-zone.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Algorithm for Cardiac Arrhythmia Classification Using Ensemble of Depthwise Separable Convolutional Neural Networks

et al. 2020

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Many algorithms have been developed for automated electrocardiogram (ECG) classification. Due to the non-stationary nature of the ECG signal, it is rather challenging to use traditional handcraft methods, such as time-based analysis of feature extraction and classification, to pave the way for machine learning implementation. This paper proposed a novel method, i.e., the ensemble of depthwise separable convolutional (DSC) neural networks for the classification of cardiac arrhythmia ECG beats. Using our proposed method, the four stages of ECG classification, i.e., QRS detection, preprocessing, feature extraction, and classification, were reduced to two steps only, i.e., QRS detection and classification. No preprocessing method was required while feature extraction was combined with classification. Moreover, to reduce the computational cost while maintaining its accuracy, several techniques were implemented, including All Convolutional Network (ACN), Batch Normalization (BN), and ensemble convolutional neural networks. The performance of the proposed ensemble CNNs were evaluated using the MIT-BIH arrythmia database. In the training phase, around 22% of the 110,057 beats data extracted from 48 records were utilized. Using only these 22% labeled training data, our proposed algorithm was able to classify the remaining 78% of the database into 16 classes. Furthermore, the sensitivity ( S n ), specificity ( S p ), and positive predictivity ( P p ), and accuracy ( A c c ) are 99.03%, 99.94%, 99.03%, and 99.88%, respectively. The proposed algorithm required around 180 μs, which is suitable for real time application. These results showed that our proposed method outperformed other state of the art methods.

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“…Continuous cardiac monitoring has become increasingly important for early detection of cardiovascular diseases by recording electrocardiogram (ECG) signals [1–3]. In continuous monitoring, ECG signal is corrupted by different noises which include baseline wander (BW), power line interference (PLI), muscle artefact (MA) and instrumentation noise (IN) [2, 3]. Example of these noises is illustrated in Fig.…”

Section: Introductionmentioning

confidence: 99%

Detection and classification of ECG noises using decomposition on mixed codebook for quality analysis

Kumar

Sharma

2020

Healthcare Technology Letters

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A New Automated Signal Quality-Aware ECG Beat Classification Method for Unsupervised ECG Diagnosis Environments

Cited by 88 publications

References 43 publications

A Hybrid Deep CNN Model for Abnormal Arrhythmia Detection Based on Cardiac ECG Signal

A Hybrid Deep CNN Model for Abnormal Arrhythmia Detection Based on Cardiac ECG Signal

An Efficient Algorithm for Cardiac Arrhythmia Classification Using Ensemble of Depthwise Separable Convolutional Neural Networks

Detection and classification of ECG noises using decomposition on mixed codebook for quality analysis

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