2 research outputs found
Neural Network Entropy (NNetEn): EEG Signals and Chaotic Time Series Separation by Entropy Features, Python Package for NNetEn Calculation
Entropy measures are effective features for time series classification
problems. Traditional entropy measures, such as Shannon entropy, use
probability distribution function. However, for the effective separation of
time series, new entropy estimation methods are required to characterize the
chaotic dynamic of the system. Our concept of Neural Network Entropy (NNetEn)
is based on the classification of special datasets (MNIST-10 and
SARS-CoV-2-RBV1) in relation to the entropy of the time series recorded in the
reservoir of the LogNNet neural network. NNetEn estimates the chaotic dynamics
of time series in an original way. Based on the NNetEn algorithm, we propose
two new classification metrics: R2 Efficiency and Pearson Efficiency. The
efficiency of NNetEn is verified on separation of two chaotic time series of
sine mapping using dispersion analysis (ANOVA). For two close dynamic time
series (r = 1.1918 and r = 1.2243), the F-ratio has reached the value of 124
and reflects high efficiency of the introduced method in classification
problems. The EEG signal classification for healthy persons and patients with
Alzheimer disease illustrates the practical application of the NNetEn features.
Our computations demonstrate the synergistic effect of increasing
classification accuracy when applying traditional entropy measures and the
NNetEn concept conjointly. An implementation of the algorithms in Python is
presented.Comment: 24 pages, 18 figures, 2 table
Machine Learning Sensors for Diagnosis of COVID-19 Disease Using Routine Blood Values for Internet of Things Application
Healthcare digitalization requires effective applications of human sensors, when various parameters of the human body are instantly monitored in everyday life due to the Internet of Things (IoT). In particular, machine learning (ML) sensors for the prompt diagnosis of COVID-19 are an important option for IoT application in healthcare and ambient assisted living (AAL). Determining a COVID-19 infected status with various diagnostic tests and imaging results is costly and time-consuming. This study provides a fast, reliable and cost-effective alternative tool for the diagnosis of COVID-19 based on the routine blood values (RBVs) measured at admission. The dataset of the study consists of a total of 5296 patients with the same number of negative and positive COVID-19 test results and 51 routine blood values. In this study, 13 popular classifier machine learning models and the LogNNet neural network model were exanimated. The most successful classifier model in terms of time and accuracy in the detection of the disease was the histogram-based gradient boosting (HGB) (accuracy: 100%, time: 6.39 sec). The HGB classifier identified the 11 most important features (LDL, cholesterol, HDL-C, MCHC, triglyceride, amylase, UA, LDH, CK-MB, ALP and MCH) to detect the disease with 100% accuracy. In addition, the importance of single, double and triple combinations of these features in the diagnosis of the disease was discussed. We propose to use these 11 features and their binary combinations as important biomarkers for ML sensors in the diagnosis of the disease, supporting edge computing on Arduino and cloud IoT service