Does the choice of definition for defibrillation and CPR success impact the predictability of ventricular fibrillation waveform analysis?

Jin, Danian; Dai, Chenxi; Gong, Yushun; Lu, Yubao; Zhang, Lei; Quan, Weilun; Li, Yongqin

doi:10.1016/j.resuscitation.2016.11.022

Cited by 21 publications

(17 citation statements)

References 31 publications

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“…As mentioned above, one of the main causes of sudden death is caused by the VF arrhythmia [3,53]. The rapid and correct detection of VF and VT is of fundamental importance both for the use of an automatic external defibrillator and for monitoring the patient.…”

Section: Discussionmentioning

confidence: 99%

“…Defibrillation is the only definitive treatment for VF. It consists of applying a high voltage electric shock on the patient's chest, facilitating the restart of a normal electrical cardiac activity [1][2][3]. However, the success of defibrillation is inversely proportional to the interval of time lapsed from the beginning of the episode to the application of the discharge.…”

Section: Introductionmentioning

confidence: 99%

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Detection of Ventricular Fibrillation Using the Image from Time-Frequency Representation and Combined Classifiers without Feature Extraction

et al. 2018

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Due the fact that the required therapy to treat Ventricular Fibrillation (V F) is aggressive (electric shock), the lack of a proper detection and recovering therapy could cause serious injuries to the patient or trigger a ventricular fibrillation, or even death. This work describes the development of an automatic diagnostic system for the detection of the occurrence of V F in real time by means of the time-frequency representation (T F R) image of the ECG. The main novelties are the use of the T F R image as input for a classification process, as well as the use of combined classifiers. The feature extraction stage is eliminated and, together with the use of specialized binary classifiers, this method improves the results of the classification. To verify the validity of the method, four different classifiers in different combinations are used: Regression Logistic with L2 Regularization (L 2 R L R), adaptive neural network (A N N C), Bagging (B A G G), and K-nearest neighbor (K N N). The Hierarchical Method (HM) and Voting Majority Method (VMM) combinations are used. ECG signals used for evaluation were obtained from the standard MIT-BIH and AHA databases. When the classifiers were combined, it was observed that the combination of B A G G , K N N , and A N N C using the Hierarchical Method (HM) gave the best results, with a sensitivity of 95.58 ± 0.41%, a 99.31 ± 0.08% specificity, a 98.6 ± 0.04% of overall accuracy, and a precision of 98.25 ± 0.29% for V F . Whereas a sensitivity of 94.02 ± 0.58%, a specificity of 99.31 ± 0.08%, an overall accuracy of 99.14 ± 0.43%, and a precision of 98.59 ± 0.09% was obtained for V T with a run time between 0.07 s and 0.12 s. Results show that the use of T F R image data to feed the combined classifiers yields a reduction in execution time with performance values above to those obtained by individual classifiers. This is of special utility for V F detection in real time.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detection of Ventricular Fibrillation Using the Image from Time-Frequency Representation and Combined Classifiers without Feature Extraction

et al. 2018

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“…Una de las arritmias más mortíferas es la Fibrilación Ventricular (FV), producida por una actividad eléctrica desorganizada del corazón que puede desembocar en muerte súbita (Murakoshi and Aonuma, 2013;Jin et al, 2017). Durante la FV, los ventrículos se contraen de forma asíncrona con ausencia de latido efectivo, fallando el bombeo sanguíneo y produciendo la muerte en el paciente si no se trata de forma adecuada en el plazo de pocos minutos.…”

Section: Introductionunclassified

“…Muchas de las víctimas pueden sobrevivir si los testigos actúan de forma inmediata después de la aparición de los primeros síntomas. La evidencia científica indica que la desfibrilación temprana es determinante en la recuperación de la víctima, siendoéste elúnico tratamiento definitivo posible para la FV: la aplicación de un choque eléctrico de alta energía sobre el pecho del paciente para facilitar el reinicio de la actividad eléctrica cardíaca normal (Jekova, 2007a;Othman et al, 2013;Jin et al, 2017).…”

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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“…Similarly, the capability of AMSA to predict long term survival is controversial. On the contrary, being AMSA a specific predictor of defibrillation outcome, the primary goal of an AMSA-guided CPR is VF termination and/or ROSC achievement [17]. Future prospective interventional studies, therefore, should aim to respond to this question: can AMSA really predict defibrillation and guide CPR interventions?…”

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confidence: 99%

Amplitude spectrum area: The “clairvoyance” during resuscitation in the era of predictive medicine

2017

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Does the choice of definition for defibrillation and CPR success impact the predictability of ventricular fibrillation waveform analysis?

Cited by 21 publications

References 31 publications

Detection of Ventricular Fibrillation Using the Image from Time-Frequency Representation and Combined Classifiers without Feature Extraction

Detection of Ventricular Fibrillation Using the Image from Time-Frequency Representation and Combined Classifiers without Feature Extraction

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

Amplitude spectrum area: The “clairvoyance” during resuscitation in the era of predictive medicine

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