Abstract
This paper presents an efficient approach for distinguishing ECG signals based on certain diseases by implementing Pan Tompkins algorithm and neural networks. Pan Tompkins algorithm is used for feature extraction on electrocardiography (ECG) signals, while neural networks help in detection and classification of the signal into four cardiac diseases: Sleep Apnea, Arrhythmia, Supraventricular Arrhythmia and Long-Term Atrial Fibrillation (AF) and normal heart beat. The paper also presents a new approach towards signal classification using the existing neural networks classifiers.
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Karthik, R., Tyagi, D., Raut, A., Saxena, S., Bharath, K.P., Rajesh Kumar, M. (2019). Implementation of Neural Network and Feature Extraction to Classify ECG Signals. In: Panda, G., Satapathy, S., Biswal, B., Bansal, R. (eds) Microelectronics, Electromagnetics and Telecommunications. Lecture Notes in Electrical Engineering, vol 521. Springer, Singapore. https://doi.org/10.1007/978-981-13-1906-8_33
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DOI: https://doi.org/10.1007/978-981-13-1906-8_33
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