Abdominal Electromyograms (EMGs) Dataset: Breathing Patterns of Sleeping Adults
Description
This data set provides Machine Learning for defining breathing patterns in sleep for adults using preprocessed abdominal electromyograms (EMGs). The data set of 40 records was casually picked from a vaster database (Computing in Cardiology Challenge 2018: Training/Test Sets. 2018. URL: https://archive.physionet.org/physiobank/database/challenge/2018/). The optimal exponential smoothing model was uniform for all records: additive errors, small undamped trends, and no seasonality. Cleared out by trends and noises, signals had autocorrelation functions with the power-law decay. That has allowed making their persistence factors evaluations (Hurst exponent). Most of the signals (38 of 40) showed frequent outliers: from a few percent up to 24.6 % of emissions. Wide data variability has been rated with the median absolute deviations, which is the most robust statistic in such a case. High variability looks a bit odd, considering low enough noise levels. The outliers' percentage, variability, SNR (signal-to-noise ratio), and persistency factors were statistically z-scored with medians and median absolute deviations. Further, their linear combinations form three independent Principal Components: numeric attributes z_1, z_2, and z_3 of the data set. Manhattan distances matrix among subjects' vectors in 4D attributes space allows imaging the data set as a weighted biconnected graph, the vertices of which are subjects. The weights of the graph's edges reflect distances between any pair of them. "Closeness centralities" of vertices, a well-known parameter in graphs theory, allowed us to cluster the data on two clusters with 11 and 29 subjects. They present two biconnected subgraphs, peripheral and core, respectively. The belonging to one of them has been reflected in binary (nominal) attribute z_4. There are 0 as the label of the peripheral subgraph and 1 for core one, respectively. The periodograms of EMGs permitted us to find ten subjects with regular breathing and 30 with irregular one, defining two inequal classes using nominal attribute z_5. So, we offer here the data set for Machine Learning in ARFF format, containing 40 instances with five attributes, the sense of which is described above.
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Steps to reproduce
This data set provides Machine Learning for defining breathing patterns in sleep for adults using preprocessed abdominal electromyograms (EMGs). The data set of 40 records was casually picked from a vaster database (Computing in Cardiology Challenge 2018: Training/Test Sets. 2018. URL: https://archive.physionet.org/physiobank/database/challenge/2018/). Primary data were preprocessed.