Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals

Elhaj, Fatin A.; Salim, Naomie; Harris, Arief R.; Swee, Tan Tian; Ahmed, Taqwa

doi:10.1016/j.cmpb.2015.12.024

Cited by 382 publications

(173 citation statements)

References 35 publications

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“…For HF, it has both FF and NFF. Table 2 shows some of the methodologies that have been proposed for cardiovascular diseases classification [90][91][92][93][94] or ECG recognition [95][96][97][98][99]. Eleven machine learning or optimization algorithms have been applied in [90][91][92][93][94][95][96][97][98][99].…”

Section: Cardiovascular Diseasesmentioning

confidence: 99%

“…Due to the multitude of smart healthcare applications, only four applications in the field of diseases diagnosis, cardiovascular diseases [82][83][84][85][86][87][88][89][90][91][92][93][94][95][96][97][98][99], diabetes mellitus [100][101][102][103][104][105][106][107][108][109][110][111][112], Alzheimer's disease and other forms of dementias [113][114][115][116][117][118][119][120][121][122][123][124][125][126], and tuberculosis [127][128][129][130][13...…”

Section: Introductionmentioning

confidence: 99%

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Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications

et al. 2017

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Abstract:To promote sustainable development, the smart city implies a global vision that merges artificial intelligence, big data, decision making, information and communication technology (ICT), and the internet-of-things (IoT). The ageing issue is an aspect that researchers, companies and government should devote efforts in developing smart healthcare innovative technology and applications. In this paper, the topic of disease diagnosis in smart healthcare is reviewed. Typical emerging optimization algorithms and machine learning algorithms are summarized. Evolutionary optimization, stochastic optimization and combinatorial optimization are covered. Owning to the fact that there are plenty of applications in healthcare, four applications in the field of diseases diagnosis (which also list in the top 10 causes of global death in 2015), namely cardiovascular diseases, diabetes mellitus, Alzheimer's disease and other forms of dementia, and tuberculosis, are considered. In addition, challenges in the deployment of disease diagnosis in healthcare have been discussed.

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Section: Cardiovascular Diseasesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications

et al. 2017

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“…Desafortunadamente, la mayoría de las señales ECG de interés presentan características no estacionarias. Por lo tanto, si bien el análisis en el dominio de la frecuencia permite determinar las frecuencias características de la señal, se pierde la información de tipo temporal siendo por ello un método muy limitado que no resultaútil para el análisis de señales no estacionarias (Cohen, 1989a;Mahmoud et al, 2006;Elhaj et al, 2016). Para superar las limitaciones del método espectral basado en la transformada de Fourier surgen las transformadas tiempo-frecuencia, que representan de forma combinada la señal tanto en el dominio del tiempo como de la frecuencia, siendo aplicadas conéxito para el diagnóstico de señales ECG (Martin and Flandrin, 1985;Yochum et al, 2016).…”

Section: Introductionunclassified

Detección de Fibrilación Ventricular Mediante Tiempo-Frecuencia y Clasificador KNN sin Extracción de Parámetros

Mjahad

Muñoz

Mompean

et al. 2017

Rev. iberoam. autom. inform. ind.

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ResumenEste trabajo propone la detección de FV y su discriminación de TV y otros ritmos cardiacos basándose en la representación tiempo-frecuencia del ECG y su conversión en imágen como entrada a un clasificador de vecinos más cercanos (KNN) sin necesidad de extracción de parámetros adicionales. Tres variantes de datos de entrada al clasificador son evaluados. Los resultados clasifican la señal en cuatro clases diferentes: 'Normal' para latidos con ritmo sinusal, 'FV' para fibrilación ventricular, 'TV' para taquicardia ventricular y 'Otros' para el resto de ritmos. Los resultados para detección de FV mostraron 88,27 % de sensibilidad y 98,22 % de especificidad para la entrada de imagen equivalente reducida que es la más rápida computacionalmente a pesar de obtener resultados de clasificación ligeramente inferiores a las representaciones no reducidas. En el caso de TV, se alcanzó un 88,31 % de sensibilidad y 98,80 % de especificidad, un 98,14 % de sensibilidad y 96,82 % de especificidad para ritmo sinusal normal y 96,91 % de sensibilidad con 99,06 % de especificidad para la clase 'Otros'. Finalmente, se realiza una comparación con otros algoritmos.Palabras Clave: Sistemas biomédicos, Señales Electrocardiográficas, Representación tiempo-frecuencia, Señales no estacionarias, Análisis de imágenes, Clasificación Ventricular Fibrillation detection using time-frequency and the KNN classifier without parameter extraction. AbstractThis work describes new techniques to improve VF detection and its separation from Ventricular Tachycarida (VT) and other rhythms. It is based on time-frequency representation of the ECG and its use as input in an automatic classifier (K-nearest neighbours -KNN) without any further signal parameter extraction or additional characteristics. For comparison purposes, three time-frequency variants are analysed: pseudo Wigner-Ville representation (RTF), grey-scale image obtained from RTF (IRTF), and reduced image from IRTF (reduced IRTF). Four types of rhythms (classes) are defined: 'Normal' for sinus rhythm, 'VT' for ventricular tachycardia, 'VF' for ventricular fibrillation and 'Others' for the rest of rhythms. Classification results for VF detection in case of reduced IRTF are 88.27 % sensitivity and 98.22 % specificity. In case of VT, 88.31 % sensitivity and 98.80 % specificity is obtained, 98.14 % sensitivity and 96.82 % specificity for normal rhythms, and 96.91 % sensitivity and 99.06 % specificity for other rhythms. Finally, results are compared with other authors.

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“…These networks have been used in fields as broad as self-driving cars for detecting pedestrians [64], to automatically detecting arrhythmia conditions [65] in cardiac patients and electrocardiography (ECG/EKG) applications [66]. As we noted above, convolutional neural networks have been used to detect 'root-like' structures in brain imaging and may extend to X-ray CT imaging of plant roots.…”

Section: Deep Learningmentioning

confidence: 99%

Existing and Potential Statistical and Computational Approaches for the Analysis of 3D CT Images of Plant Roots

Valdes

Clarke

2018

Agronomy

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Abstract:Scanning technologies based on X-ray Computed Tomography (CT) have been widely used in many scientific fields including medicine, nanosciences and materials research. Considerable progress in recent years has been made in agronomic and plant science research thanks to X-ray CT technology. X-ray CT image-based phenotyping methods enable high-throughput and non-destructive measuring and inference of root systems, which makes downstream studies of complex mechanisms of plants during growth feasible. An impressive amount of plant CT scanning data has been collected, but how to analyze these data efficiently and accurately remains a challenge. We review statistical and computational approaches that have been or may be effective for the analysis of 3D CT images of plant roots. We describe and comment on different approaches to aspects of the analysis of plant roots based on images, namely, (1) root segmentation, i.e., the isolation of root from non-root matter; (2) root-system reconstruction; and (3) extraction of higher-level phenotypes. As many of these approaches are novel and have yet to be applied to this context, we limit ourselves to brief descriptions of the methodologies. With the rapid development and growing use of X-ray CT scanning technologies to generate large volumes of data relevant to root structure, it is timely to review existing and potential quantitative and computational approaches to the analysis of such data. Summaries of several computational tools are included in the Appendix.

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Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals

Cited by 382 publications

References 35 publications

Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications

Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications

Detección de Fibrilación Ventricular Mediante Tiempo-Frecuencia y Clasificador KNN sin Extracción de Parámetros

Existing and Potential Statistical and Computational Approaches for the Analysis of 3D CT Images of Plant Roots

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