Arrhythmias discrimination based on fractional order system and KNN classifier

Assadi, I.; Charef, Abdelfatah; Bensouici, Tahar; Belgacem, N.

doi:10.1049/cp.2015.1781

Cited by 10 publications

(13 citation statements)

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“…Ref. [47] describes an approach to do the same to obtain five parameters from a QRS complex signal to differentiate between healthy and three types of arrhythmic signals using a k nearest neighbor classifier.…”

Section: Biomedical Applicationsmentioning

confidence: 99%

“…Although the publications by [43][44][45][46][47][48] have already been categorized as preprocessing, they also serve as a hybrid machine learning and fractional dynamics approach. Specifically, as authors employed the transfer function from a fractional order model to first, model a signal and then to obtain the functions parameters or the corresponding error and signal energy of the model as features for classification.…”

Section: Machine Learning and Fractional Dynamicsmentioning

confidence: 99%

“…Regarding biomedical applications, we find that here, similar to preprocessing spectral data, researchers use fractional derivatives to preprocess their medical images, as done in [50] for CT images. Another example is to preprocess EEG or ECG signals, used to classify diseases or malfunctions as done in [45] and [47].…”

Section: Preprocessingmentioning

confidence: 99%

“…Also, we considered both data manipulation and feature extraction as being preprocessing. Here one can differ between manipulating the original data using a fractional derivative (e.g., [41]) and extracting features, i.e., error energy and parameters from the original data (e.g., [47]).…”

Section: Preprocessingmentioning

confidence: 99%

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Combining Fractional Derivatives and Supervised Machine Learning: A Review

Raubitzek¹,

Mallinger²,

Neubauer³

2022

Preprint

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Fractional calculus gained a lot of attention in the last couple of years. Researchers discovered that processes in various fields follow rather fractional dynamics than ordinary integer-ordered dynamics, meaning the corresponding differential equations feature non-integer valued derivatives. There are several arguments for why this is the case, one of them being that fractional derivatives’ inherit spatiotemporal memory and/or the ability to express complex naturally occurring phenomena. Another popular topic nowadays is machine learning, i.e., learning behavior and patterns from historical data. In our ever-changing world with ever-increasing amounts of data, machine learning is a powerful tool for data analysis, problem-solving, modeling, and prediction. It further provides many insights and discoveries in various scientific disciplines. As these two modern-day topics provide a lot of potential for combined approaches to describe complex dynamics, this article reviews combined approaches of fractional derivatives and machine learning from the past, puts them into context, and thus provides a list of possible combined approaches and the corresponding techniques. Note, however, that this article does not deal with neural networks, as there already is profound literature on neural networks and fractional calculus. We sorted past combined approaches from the literature into three categories, i.e., preprocessing, machine learning &amp; fractional dynamics, and optimization. The contributions of fractional derivatives to machine learning are manifold as they provide powerful preprocessing and feature augmentation techniques, can improve physically informed machine learning, and are capable of improving hyperparameter optimization. Thus, this article serves to motivate researchers dealing with data-based problems, to be specific machine learning practitioners, to adopt new tools and enhance their existing approaches.

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Section: Biomedical Applicationsmentioning

confidence: 99%

Section: Machine Learning and Fractional Dynamicsmentioning

confidence: 99%

Section: Preprocessingmentioning

confidence: 99%

Section: Preprocessingmentioning

confidence: 99%

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Combining Fractional Derivatives and Supervised Machine Learning: A Review

Raubitzek¹,

Mallinger²,

Neubauer³

2022

Preprint

View full text Add to dashboard Cite

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“…Afterwards, a constant number of points that account for 0.278 seconds is selected before and after each extracted R-point . Therefore, a final beat of 200 sample points or 0.556 seconds is resulted, which is considered sufficient to capture most of the heartbeat information [39].…”

Section: Beats Extractionmentioning

confidence: 99%

On Designing an ECG-based Intelligent System: Utilizing the Heart’s Electrical Activity to Recognize Humans and Detect Arrhythmia

Abdeldayem¹

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The electrocardiogram (ECG) signal is the bioelectrical signal that reflects the heart's activity. It has been extensively used as a diagnostic tool since it holds information about the cardiac health condition. However, recent researches have shown that it exhibits an inter-subject variability property. Therefore, it can be used as a biometric-based modality for either identification or verification purposes. Nevertheless, some of the challenges are faced while employing such a signal. For instance, ECG signal is prone to noise, accordingly, noise filters should be designed to remove the noise while keeping the signal properties. Moreover, factors such as medications, health condition, and emotional state can affect the intra-subject variability of the signal. Therefore, experiments that are performed should consider these factors to produce a robust system. In this thesis, we utilize the ECG signals to propose three approaches: one clinical approach, and two biometric approaches. In the first approach, we automatically detect cardiac arrhythmia, a disease when the heart fails to contract at the normal rhythm, using the signal textures. These texture operators include both temporal and spectro-temporal features. The temporal texture includes the one-dimensional local binary pattern (1D LBP), while the spectro-temporal textures include the investigation of the texture of short-time Fourier transform (STFT) and generalized Morse wavelet transform (CWT). Different classifiers as well as different ECG lead configurations were studied. Classification accuracies of 92.97%, 99.24%, and 99.81% on MITDB were achieved using the 1D LBP, CWT, and STFT textures respectively. On the other hand, the ECG signal showed some advantages over other biometric modalities such as face and iris. These advantages incorporate the embedded liveness detection, the difficulty to counterfeit when compared to other traits, and the hybrid information it holds about the subject identity as well as the health, mental, and emotional conditions. In this work, we propose two biometric approaches. The first approach utilizes the spectrotemporal properties of the signal for both subject identification and validation. A two-dimensional convolutional neural network (2D CNN) was employed to extract the high-level features to distinguish subjects. Both the STFT and CWT were investigated as features. Validating on eight databases, an overall identification rate of 97.85%, equal error rate of 0.0268, and area under curve of 0.99% were achieved for STFT features. The second approach utilizes the cyclostationary properties of the signal since the ECG signal is non-stationary. The superiority of this approach is that it follows the blind-segmentation where no ECG wave points are detected, as well as being more robust to the noise in the ECG signal. The spectral function of the signal is extracted and used as features to feed a 2D CNN. Seven databases were used to validate the robustness and generalization of the approach, where an average identification rate ...

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