QRS complex based human identification

Assadi, I.; Charef, Abdelfatah; Belgacem, N.; Naït‐Ali, Amine; Bensouici, Tahar

doi:10.1109/icsipa.2015.7412198

Cited by 7 publications

(8 citation statements)

References 12 publications

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“…Furthermore, similar approaches can be used to analyze electrocardiography (ECG) signals. Again, in [47], a fractional order transfer function was applied to the QRS complex signal (from ECG measurements) to obtain six parameters which are then fed into a knearest neighbor classifier for person identification. Ref.…”

Section: Biomedical Applicationsmentioning

confidence: 99%

Combining Fractional Derivatives and Machine Learning: A Review

Raubitzek

Mallinger²,

Neubauer³

2022

Entropy

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Fractional calculus has gained a lot of attention in the last couple of years. Researchers have discovered that processes in various fields follow fractional dynamics rather than ordinary integer-ordered dynamics, meaning that the corresponding differential equations feature non-integer valued derivatives. There are several arguments for why this is the case, one of which is 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 has provided many further insights and discoveries in various scientific disciplines. As these two modern-day topics hold a lot of potential for combined approaches in terms of describing complex dynamics, this article review combines approaches from 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 is already extensive literature on neural networks and fractional calculus. We sorted past combined approaches from the literature into three categories, i.e., preprocessing, machine learning and 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%

Combining Fractional Derivatives and Machine Learning: A Review

Raubitzek

Mallinger²,

Neubauer³

2022

Entropy

View full text Add to dashboard Cite

show abstract

“…Further, similar approaches can be used to analyze Electrocardiography (ECG) signals. Again in [46], a fractional order transfer function is applied to the QRS complex signal (from ECG measurements) to obtain six parameters which are then fed into a k nearest neighbor classifier for person identification. Ref.…”

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%

“…We also find several publications where fractional derivatives and machine learning can deal with heart rate variability. I.e., in both [46] and [47], the researchers are using ideas related to fractional derivatives to, e.g., classify arrhythmic ECG signal. This also refers to human variability in general.…”

Section: Variabilitymentioning

confidence: 99%

“…Thus, the connection between the renormalization group and, in this case, deep neural networks might not be generalizable to machine learning applications at large. Still, when talking about feature extraction, one can argue that fractional derivatives do just that in, e.g., [46,47]. These articles describe the feature extraction such that new parameters, e.g., the error energy of a signal when being modeled using a fractional transfer function, are found.…”

Section: Renormalization Groupmentioning

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.

show abstract