A novel Hot-Flash classification algorithm via multi-sensor features integration

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Abstract

We aim to evaluate the feasibility and performance of a novel hot flash (HF) classification algorithm based on multisensor features integration using commercial wearable sensors. First, we processed feature sets from wrist-based multi-sensor data (photoplethysmography, motion, temperature, skin conductance and). Then, we classified (Decision Tree) physiological-recorded HFs (N=27) recorded from three menopause women, and we assessed the algorithm performance against gold-standard HF expert evaluation. The results indicated that while skin conductance features alone explain most of the variance (~65%) in HF classification, the multi-sensor approach achieved above 90% sensitivity at 95.6% specificity in HF classification and showed advantages under conditions of signal corruption and different biobehavioral states (sleep vs wake). The proposed new multi-sensor approach showed being promising in HF classification using common commercially-available wearable sensors and target locations.Clinical Relevance - The development of user-centered accurate, automatic detection systems for HFs can advance the measurement and treatment of HFs.

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APA

Tsiartas, A., Baker, F. C., Smith, D., & De Zambotti, M. (2021). A novel Hot-Flash classification algorithm via multi-sensor features integration. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (pp. 2067–2070). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/EMBC46164.2021.9629627

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