Fractal feature based ECG arrhythmia classification

Raghav, Shantanu; Mishra, Amit Kumar

doi:10.1109/tencon.2008.4766475

Cited by 11 publications

(3 citation statements)

References 19 publications

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“…FD gives a quantitative indication of the chaotic behavior of a signal, and is also related to the degree of interference of the signal, which is inversely related to the 'smoothness' of the signal (Mesin, Cescon, Gazzoni, Merletti, & Rainoldi, 2009a). At least eight different methods for estimating FD of sEMG waveforms have been applied in literature, including the box-counting method (Barnsley & Hurd, 1989), the Hurst exponent (Hurst, 1951), a method based on power spectral density (Kaplan, 1999;Raghav & Mishra, 2008;Spasic, 2007), the methods proposed by (Higuchi, 1988), (Sevcik, 2010), (Petrosian, 1995) and (Katz, 1988) and two variants of the latter (Castiglioni, 2010). These methods were compared and reviewed by (Coelho & Lima, 2014), who evidenced that the normalized version of the Katz's estimation method, followed by the Hurst exponent, significantly outperform the others in terms of generating more discriminatory features.…”

Section: Fractal Analysismentioning

confidence: 99%

Evaluation of performance fatigability through surface EMG in health and muscle disease: state of the art

Beretta-Piccoli

Cescon

D’Antona

2020

Arab Journal of Basic and Applied Sciences

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In literature, it is commonly reported that the progress of performance fatigability may be indirectly assessed through the changes in the features of the surface electromyogram (sEMG) signal. In particular, during isometric constant force contractions, changes in the sEMG signal are caused by several physiological factors, such as a decay in muscle fibers conduction velocity (CV), an increase of the degree of synchronization between the firing times of simultaneously active motor units (MUs), by the central nervous system, and a reduction of the recruitment threshold and a modulation of MUs firing rate. Amplitude and spectral parameters may be used to characterize the global contributions to performance fatigability, such as MU control properties and fiber membrane properties, or central and peripheral factors, respectively. In addition, being CV a physiological parameter, its estimation is of marked interest to the study of fatigue both in physiological and in presence of neuromuscular diseases.

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Section: Fractal Analysismentioning

confidence: 99%

Evaluation of performance fatigability through surface EMG in health and muscle disease: state of the art

Beretta-Piccoli

Cescon

D’Antona

2020

Arab Journal of Basic and Applied Sciences

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“…Generally, the higher the complexity of the signal, the larger the corresponding FD value [7]. Therefore, many scholars have introduced FD into the research of signal processing in the medical field, such as electroencephalograms (EEG) [8] and electrocardiograms (ECG) [9][10][11][12], and have made remarkable achievements.…”

Section: Introductionmentioning

confidence: 99%

Roughness Scaling Extraction Accelerated by Dichotomy-Binary Strategy and Its Application to Milling Vibration Signal

et al. 2022

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Fractal algorithms for signal analysis are developed from geometric fractals and can be used to describe various complex signals in nature. A roughness scaling extraction algorithm with first-order flattening (RSE-f1) was shown in our previous studies to have a high accuracy, strong noise resistance, and a unique capacity to recognize the complexity of non-fractals that are common in signals. In this study, its disadvantage of a long calculation duration was addressed by using a dichotomy-binary strategy. The accelerated RSE-f1 algorithm (A-RSE-f1) retains the three above-mentioned advantages of the original algorithm according to theoretical analysis and artificial signal testing, while its calculation speed is significantly accelerated by 13 fold, which also makes it faster than the typical Higuchi algorithm. Afterwards, the vibration signals of the milling process are analyzed using the A-RSE-f1 algorithm, demonstrating the ability to distinguish different machining statuses (idle, stable, and chatter) effectively. The results of this study demonstrate that the RSE algorithm has been improved to meet the requirements of practical engineering with both a fast speed and a high performance.

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“…However, none of the works in the open literature has extensively tested and validated a fractal dimension based ECG signal classification algorithm. In one of the earlier works [27], the authors have tried using local fractal dimension based nearest neighbor classifier for ECG signal based arrhythmia classification, and results were encouraging. The current work is an expanded version of the earlier work and here we have done some addition to the base algorithm and have validated it extensively.…”

Section: Introductionmentioning

confidence: 99%