Automated Microstress Assessment During Pregnancy Using ECG Sensing and Optimized Deep Networks

Elevated stress levels during pregnancy increase the risk of delivering a premature or low-birthweight infant. Recently, microecological momentary assessment (micro-EMA) has been explored extensively. However, capturing more distinct physiological responses to micro-EMA is still challenging. In this...

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Veröffentlicht in:	IEEE sensors letters 2024-09, Vol.8 (9), p.1-4
Hauptverfasser:	Mehta, Chirag, Ananthoju, Pranav Sai, PJ, Swarubini, Ganapathy, Nagarajan
Format:	Artikel
Sprache:	eng
Schlagworte:	1D-CNN Biomedical monitoring classifiers Decision trees Electrocardiography Feature extraction Human factors Machine learning microecological momentary assessment (micro-EMA) Physiological responses Pregnancy Real time Sensor applications Sensors Signal classification Stress Support vector machines
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