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Roza, V. C. C., de Almeida, A. M. & Postolache, O. A. (2017). Design of an artificial neural network and feature extraction to identify arrhythmias from ECG. In 12th IEEE International Symposium on Medical Measurements and Applications, MeMeA 2017. (pp. 391-396). Rochester: IEEE.

V. C. Roza et al.,  "Design of an artificial neural network and feature extraction to identify arrhythmias from ECG", in 12th IEEE Int. Symp. on Medical Measurements and Applications, MeMeA 2017, Rochester, IEEE, 2017, pp. 391-396

@inproceedings{roza2017_1769545309862,
	author = "Roza, V. C. C. and de Almeida, A. M. and Postolache, O. A.",
	title = "Design of an artificial neural network and feature extraction to identify arrhythmias from ECG",
	booktitle = "12th IEEE International Symposium on Medical Measurements and Applications, MeMeA 2017",
	year = "2017",
	editor = "",
	volume = "",
	number = "",
	series = "",
	doi = "10.1109/MeMeA.2017.7985908",
	pages = "391-396",
	publisher = "IEEE",
	address = "Rochester",
	organization = "",
	url = "https://ieeexplore.ieee.org/document/7985908/"
}

TY  - CPAPER
TI  - Design of an artificial neural network and feature extraction to identify arrhythmias from ECG
T2  - 12th IEEE International Symposium on Medical Measurements and Applications, MeMeA 2017
AU  - Roza, V. C. C.
AU  - de Almeida, A. M.
AU  - Postolache, O. A.
PY  - 2017
SP  - 391-396
DO  - 10.1109/MeMeA.2017.7985908
CY  - Rochester
UR  - https://ieeexplore.ieee.org/document/7985908/
AB  - This paper presents a design of an artificial neural network (ANN) and feature extraction methods to identify two types of arrhythmias in datasets obtained through electrocardiography (ECG) signals, namely arrhythmia dataset (AD) and supraventricular arrhythmia dataset (SAD). No special ANN toolkit was used; instead, each neuron and necessary calculus were modeled and individually programmed. Thus, four temporal-based features are used: heart rate (HR), R-peaks root mean square (R-RMS), RR-peaks variance (RR-VAR), and QSR-complex standard deviation (QSR-SD). The network architecture presents four neurons in the input layer, eight in hidden layer and an output layer with two neurons. The proposed classification method uses the MIT-BIH Dataset (Massachusetts Institute of Technology-Beth Israel Hospital) for training, validation and execution or test phases. Preliminary results show the high efficiency of the proposed ANN design and its classification method, reaching accuracies between 98.76% and 98.91%, when in the identification of NSRD and arrhythmic ECG; and accuracies of 86.37% (AD) and 76.35% (SAD), when analyzing only classifications between both arrhythmias.
ER  -