Abstract
A data compression algorithm for digital Holter recording using artificial neural networks (ANN) is proposed. A dual three-layer (one hidden layer) neural network which has a few units of hidden layer is used to extract the differences of waveforms as the activation levels of hidden layer units. The network is tuned using supervised signals, which are the same as input signals using the back propagation learning algorithm. One network is used for data compression, the other is always learning with current signals. If the ECG waveform changes, the neural network is changed. Once the activation levels of hidden layer units are stored, the original waveforms are reproduced with the network between the hidden and the output layers. For 24-h Holter recording of one channel, a memory capacity of 260 kb is needed with this procedure, which corresponds to a data compression ratio of 1:38.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
