Key result
Artificial neural networks, particularly multi-layer feed forward neural networks like BPNN, are useful for classifying biomedical signals such as EEG, ECG, and EMG.
This review highlights the importance of artificial neural networks, particularly back propagation neural networks, in the classification and processing of biomedical signals like ECG and EEG.
May inform ANN-based ECG/EEG classification research; leaves open prospective clinical validation before practice change.
Feature extraction of biomedical signals plays a significant role in diagnosing of diseases or problems. This paper shows some basic features and processing techniques for biomedical signals and also contains the brief description of neural networks. The biomedical signals are related to the body organs which describe electrically working of associated organs. Examples of biomedical signals are EEG, ECG, EMG. For classifying the biomedical signals, we use ANNs because classification is the most growing research and application area of neural network. Neural networks are used in lot of areas of industry like finance, engineering, biology. Here describes the ANN and its importance. For solving the nonlinear classification problem, used BPNN which is multi- layer feed forward neural network.
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Chhatrapal Singh (2016) conducted a review in Biomedical signal classification. Artificial Neural Networks (ANNs) was evaluated. Artificial neural networks, particularly multi-layer feed forward neural networks like BPNN, are useful for classifying biomedical signals such as EEG, ECG, and EMG.
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