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1D Convolutional Neural Network for Detecting Ventricular Heartbeats

Suárez-León, A. A.; Núñez, José R. (Universidad de la Costa, 2020)

This paper shows a novel approach for detecting ventricular heartbeats using a 1D Convolutional Neural Network (1D-CNN). The algorithm input is the raw ECG signal, i.e., no signal pre-processing nor feature extraction are ...
Wavelet transform selection method for biological signal treatment

Jiménez, Gonzalo; COLLAZOS MORALES, CARLOS ANDRES; De-La-Hoz-Franco, Emiro; Ariza Colpas, Paola Patricia; Ramayo González, Ramón Enrique; Maldonado-Franco, Adriana (Universidad de la Costa, 2020)

This paper presents the development and evaluation of an algorithm for compressing fetal electrocardiographic signals, taken superﬁcially on the mother’s abdomen. This method for acquiring ECG signals produces a great ...

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1D Convolutional Neural Network for Detecting Ventricular Heartbeats
...
Suárez-León, A. A. | 2020

This paper shows a novel approach for detecting ventricular heartbeats using a 1D Convolutional Neural Network (1D-CNN). The algorithm input is the raw ECG signal, i.e., no signal pre-processing nor feature extraction are involved. The output of the 1D-CNN is filtered using a combination of linear and nonlinear filters to produce the final output. The MIT-BIH arrhythmia database was used for both algorithm training/tuning and evaluation. The assessment methodology followed the interpatient paradigm, where the algorithm was trained and evaluated using independent subsets. The performance of the proposed method was evaluated for two tasks; QRS detection, and heartbeat classification. QRS detection resulted in a sensitivity of 99.0% and a positive predictivity of 96.5%. The performance assessment of the ventricular ectopic beat detection resulted in a sensitivity of 85.8% and a positive predictivity of 64.5%. Although there is still room for improvement, the results suggest that convolutional neural networks are a promising approach for building heartbeat classifiers.

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Wavelet transform selection method for biological signal treatment
...
Jiménez, Gonzalo | 2020

This paper presents the development and evaluation of an algorithm for compressing fetal electrocardiographic signals, taken superﬁcially on the mother’s abdomen. This method for acquiring ECG signals produces a great volumen of information that makes it diﬃcult for the records to be stored and transmitted. The proposed algorithm aims for lossless compression of the signal by applying Wavelet Packet Transform to keep errors below the unit, with compression rates over 20:1 and with conserved energy in reconstruction as comparison parameter. For algorithm validation, the signal ﬁles provided by PhysioBank DataBase are used.

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ListarArtículos científicos por tema "ECG"

1D Convolutional Neural Network for Detecting Ventricular Heartbeats ﻿

Wavelet transform selection method for biological signal treatment ﻿

Envíos recientes

1D Convolutional Neural Network for Detecting Ventricular Heartbeats

Wavelet transform selection method for biological signal treatment

1D Convolutional Neural Network for Detecting Ventricular Heartbeats

Wavelet transform selection method for biological signal treatment