A new method for classification of ECG arrhythmias using neural network with adaptive activation function

ECG-based heartbeat classification for arrhythmia detection: A survey

“…The simplest way to extract features in the time domain is to utilize the points of the segmented ECG curve, i.e., the heartbeat, as features [73,74]. However, the use of samples of the curve as features is a technique that is not very efficient, since besides producing a vector of the features with high dimensions (depending on the amount of samples used to represent the heartbeat), it suffers from several problems related to the scale or displacement of the signal with respect to the central point (peak R).…”

Classification of electrocardiogram and auscultatory blood pressure signals using machine learning models

“…A new ANN model with adaptive activation functions to classify ECG arrhythmias was proposed in Özbay and Tezel (2010). The activation functions were used in hidden neurons, in an attempt to improve the performance of the classical MLP model (Özbay & Tezel, 2010). Using the MIT-BIH data sets, an accuracy rate of 98.19% was obtained (Özbay & Tezel, 2010).…”

“…The MLP model was able to produce a high accuracy (99%) rate (Özbay et al, 2011). A new ANN model with adaptive activation functions to classify ECG arrhythmias was proposed in Özbay and Tezel (2010). The activation functions were used in hidden neurons, in an attempt to improve the performance of the classical MLP model (Özbay & Tezel, 2010).…”

Optimización y clasificación de señales EMG a través de métodos de reconocimiento de patrones

“…Se ha demostrado que una red MLP con una capa oculta y la función sigmoidal es un aproximador universal de cualquier función continua en un conjunto compacto. Las redes neuronales MLP pueden trabajar como cualquier clasificador no lineal o como una función de regresión [12], [13] y [14].…”