التفاصيل البيبلوغرافية
| العنوان: |
Improved Stress Classification Using Automatic Feature Selection from Heart Rate and Respiratory Rate Time Signals. |
| المؤلفون: |
Iqbal, Talha, Elahi, Adnan, Wijns, William, Amin, Bilal, Shahzad, Atif |
| المصدر: |
Applied Sciences (2076-3417); Mar2023, Vol. 13 Issue 5, p2950, 15p |
| مصطلحات موضوعية: |
HEART beat, RANK correlation (Statistics), AUTOMATIC classification, TIME series analysis, PRINCIPAL components analysis, FEATURE selection, RANDOM forest algorithms, HEART rate monitors |
| مصطلحات جغرافية: |
KENDALL (Fla.) |
| مستخلص: |
Time-series features are the characteristics of data periodically collected over time. The calculation of time-series features helps in understanding the underlying patterns and structure of the data, as well as in visualizing the data. The manual calculation and selection of time-series feature from a large temporal dataset are time-consuming. It requires researchers to consider several signal-processing algorithms and time-series analysis methods to identify and extract meaningful features from the given time-series data. These features are the core of a machine learning-based predictive model and are designed to describe the informative characteristics of the time-series signal. For accurate stress monitoring, it is essential that these features are not only informative but also well-distinguishable and interpretable by the classification models. Recently, a lot of work has been carried out on automating the extraction and selection of times-series features. In this paper, a correlation-based time-series feature selection algorithm is proposed and evaluated on the stress-predict dataset. The algorithm calculates a list of 1578 features of heart rate and respiratory rate signals (combined) using the tsfresh library. These features are then shortlisted to the more specific time-series features using Principal Component Analysis (PCA) and Pearson, Kendall, and Spearman correlation ranking techniques. A comparative study of conventional statistical features (like, mean, standard deviation, median, and mean absolute deviation) versus correlation-based selected features is performed using linear (logistic regression), ensemble (random forest), and clustering (k-nearest neighbours) predictive models. The correlation-based selected features achieved higher classification performance with an accuracy of 98.6% as compared to the conventional statistical feature's 67.4%. The outcome of the proposed study suggests that it is vital to have better analytical features rather than conventional statistical features for accurate stress classification. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
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