Abstract
The classification of biological signals is important in detecting abnormal conditions in observed biological subjects. The classifiers are trained on feature vectors, which often constitute the parameters of the observed time series data models. Since the feature extraction is usually the most time-consuming step in training a classifier, in this paper, signal folding and the associated folding operator are introduced to reduce the variability in near-cyclostationary biological signals so that these signals can be represented by models that have a lower order. This leads to a substantial reduction in computational complexity, so the classifier can be learned an order of magnitude faster and still maintain its decision accuracy. The performance of different classifiers involving signal folding as a pre-processing step is studied for sleep apnea detection in one-lead ECG signals assuming ARIMA modeling of the time series data. It is shown that the R-peak-based folding of ECG segments has superior performance to other more general, similarity based signal folding methods. The folding order can be optimized for the best classification accuracy. However, signal folding requires precise scaling and alignment of the created signal fragments.
Highlights
Biological signals can be gathered from a wide range of physiological sensors which are frequently used in different diagnostic and experimental settings
The classification of ECG signals seems to be mainly concerned with detecting heart arrhythmia, other diagnostic objectives are considered such as detecting stages of myocardial infarction [25], stages of sleep [26], and changes in ECG due to hypertension [27]
singular value decomposition (SVD), discrete Fourier transform (DFT) and dynamic time warping with optimized segment length wavelets with de-noising recurrent NN with attention bi-orthogonal wavelet filter bank followed by KNN, support vector machine (SVM), and ensemble bagged trees fixed frequency range empirical wavelet transform with CNN
Summary
Biological signals can be gathered from a wide range of physiological sensors which are frequently used in different diagnostic and experimental settings. The digitized sensor outputs are time series data encoding information about the underlying biological processes and phenomena. It is, of interest to develop robust signal and data processing methods for the automatic extraction of relevant information from the collected data. In addition to various methods of classical statistical and causal inference, the regression and classification of time series data require their representation as feature vectors, such as the parameters of time series data models. Time series data are often modeled as an autoregressive integrated moving average (ARIMA). A comprehensive overview of ARIMA modeling can be found in [1].
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