Abstract
The paper deals with ECG prediction based on neural networks classification of different types of time courses of ECG signals. The main objective is to recognise normal cycles and arrhythmias and perform further diagnosis. We proposed two detection systems that have been created with usage of neural networks. The experimental part makes it possible to load ECG signals, preprocess them, and classify them into given classes. Outputs from the classifiers carry a predictive character. All experimental results from both of the proposed classifiers are mutually compared in the conclusion. We also experimented with the new method of time series transparent prediction based on fuzzy transform with linguistic IF-THEN rules. Preliminary results show interesting results based on the unique capability of this approach bringing natural language interpretation of particular prediction, that is, the properties of time series.
Highlights
Biometrical data is typically represented as an image or a quantification of measured physiological or behavioural characteristics
We proposed two detection systems that have been created with usage of neural networks
The training set consisted of modified ECG waveforms
Summary
Biometrical data is typically represented as an image or a quantification of measured physiological or behavioural characteristics. The usage of fuzzy logic for analysis and prediction of time series can be perceived as a complement method to neural network based methods. The inverse fuzzy transform serves us as a model of the trend-cycle of a given time series. By subtracting the trend-cycle (inverse fuzzy transform) values from the time series lags, we get pure seasonal components. This is how the fuzzy transform helps us to model and decompose a given time series. Both forecasted components, trend-cycle and seasonal, are composed together to obtain the forecast of time series lags These methods are integrated into an implementation, PC application called linguistic fuzzy logic forecaster (LFLF), which enables as to produce linguistic descriptions that describe properties of data treated like a time series
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