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- W2597267079 abstract "In this paper classification algorithm of ECG signals based on segmentation of basic waveform using artificial neural networks was implemented. Inputs of classification process are different types of features: time-domain features, morphological features, statistical features. Comparison of results using these types of features as well as their combination was performed. The algorithms are implemented in MATLAB environment and performance of algorithms is evaluated on the MIT-BIH Arrhythmia Database. Feature extraction process was done using Wavelet transform (detection of P, Q, R, S, T waves for time-domain features and its coefficients for morphological features) and statistical functions. The best results were gained using morphological features in combination with their statistical features." @default.
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- W2597267079 date "2017-01-01" @default.
- W2597267079 modified "2023-09-27" @default.
- W2597267079 title "ECG Signal Classification Using Artificial Neural Networks: Comparison of Different Feature Types" @default.
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- W2597267079 doi "https://doi.org/10.1007/978-981-10-4166-2_72" @default.
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