Comparison of different approaches for removal of baseline wander from ECG signal

Kaur, Manpreet; Singh, Birmohan; Seema, .

doi:10.1145/1980022.1980307

Cited by 87 publications

(38 citation statements)

References 5 publications

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“…• < θ < 90 • ), which comes from the idea that baseline drift can be fit by polynomial [10]. Unlike the sensitivity of the amplitude of the rising edge to baseline drift in IMS, ITA is unaffected by it.…”

Section: Proposed Methodsmentioning

confidence: 99%

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Adaptive thresholding with inverted triangular area for real-time detection of the heart rate from photoplethysmogram traces on a smartphone

Jiang

Wittek

Zhao

et al. 2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-Photoplethysmogram (PPG) signals acquired by smartphone cameras are weaker than those acquired by dedicated pulse oximeters. Furthermore, the signals have lower sampling rates, have notches in the waveform and are more severely affected by baseline drift, leading to specific morphological characteristics. This paper introduces a new feature, the inverted triangular area, to address these specific characteristics. The new feature enables real-time adaptive waveform detection using an algorithm of linear time complexity. It can also recognize notches in the waveform and it is inherently robust to baseline drift. An implementation of the algorithm on Android is available for free download. We collected data from 24 volunteers and compared our algorithm in peak detection with two competing algorithms designed for PPG signals, IncrementalMerge Segmentation (IMS) and Adaptive Thresholding (ADT). A sensitivity of 98.0 % and a positive predictive value of 98.8 % were obtained, which were 7.7 % higher than the IMS algorithm in sensitivity, and 8.3 % higher than the ADT algorithm in positive predictive value. The experimental results confirmed the applicability of the proposed method.

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Section: Proposed Methodsmentioning

confidence: 99%

“…When the finger is correctly placed, we record the mean brightness of each frame as PPG signal. Then we interpolate the collected signal to the average sampling rate in the first 5 seconds and only use a 3-point moving average filter to smooth it in order not to modify the waveform [10].…”

Section: Interpolating and Smoothingmentioning

confidence: 99%

Adaptive thresholding with inverted triangular area for real-time detection of the heart rate from photoplethysmogram traces on a smartphone

Jiang

Wittek

Zhao

et al. 2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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“…Uma vez que foi iniciado a aquisição dos biopotenciais, os sinais XYZ vão diretamente para os amplificadores biofísicos de baixo ruído. Após isso, existe uma etapa de pré-filtragem de ruídos de alta frequência originadas da rede elétrica, do contato dos eletrodos com a pele, das atividades musculares e dos próprios componentes eletrônicos e ao fenômeno de base-line drift causado pelas baixas frequências [6]. O próximo passo é converter estes sinais para o domínio digital, tarefa feita por um conversor analógico/digital.…”

Section: Materiais E Métodosunclassified

Detecção De Potenciais Tardios De Ativação Ventricular: Um Protótipo Para Eletrocardiograma De Alta Resolução

Franco¹,

Monteiro²,

Antonaccio³

et al. 2018

Anais Do v Congresso Brasileiro De Eletromiografia E Cinesiologia E X Simpósio De Engenharia Biomédica

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“…Estas interferencias en la señal deben ser reducidas para no modificar el resultado de procesos posteriores (Sharma et al, 2010;Kabir and Shahnaz, 2012). En este caso, la primera etapa de procesado de la señal ECG consiste en un filtro de respuesta impulsional infinita (IIR) de orden 8 con una respuesta Butterworth (Kaur et al, 2011;Ravindra Pratap Narwaria and Singhal, 2011). Las figuras 2b y 2f muestran el efecto de la aplicación de este filtro de eliminación de la línea de base a una señal de ECG sinusal normal (figura 2a) y FV (figura 2e), respectivamente.…”

Section: Reducción De Las Oscilaciones De La Línea Baseunclassified

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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Comparison of different approaches for removal of baseline wander from ECG signal

Cited by 87 publications

References 5 publications

Adaptive thresholding with inverted triangular area for real-time detection of the heart rate from photoplethysmogram traces on a smartphone

Adaptive thresholding with inverted triangular area for real-time detection of the heart rate from photoplethysmogram traces on a smartphone

Detecção De Potenciais Tardios De Ativação Ventricular: Um Protótipo Para Eletrocardiograma De Alta Resolução

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

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