Detection and Prediction of Ovulation From Body Temperature Measured by an In-Ear Wearable Thermometer

• Published in: IEEE transactions on biomedical engineering Vol. 67; no. 2; pp. 512 - 522
• Main Authors: Luo, Lan, She, Xichen, Cao, Jiexuan, Zhang, Yunlong, Li, Yijiang, Song, Peter X. K.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.02.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adult; Algorithms; Basal body temperature; Biomedical monitoring; Body temperature; Body Temperature - physiology; Ear; Ear - physiology; Ear canal; Empirical analysis; Equipment Design; Family planning; Female; Fertility; Hidden Markov Model (HMM); Hidden Markov models; High temperature; Humans; Low temperature; Machine learning; Markov Chains; Monitoring; Monitoring methods; Ovulation; Ovulation - physiology; Ovulation Detection - methods; Post-production processing; prediction; Prediction algorithms; Predictions; Signal Processing, Computer-Assisted; Sleep; Statistical analysis; Temperature distribution; Temperature measurement; Temperature sensors; Thermometers; Thermometry - instrumentation; Thermometry - methods; tracking data; wearable; Wearable Electronic Devices; Wearable technology; Young Adult
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2019.2916823

Objective: We present a non-invasive wearable device for fertility monitoring and propose an effective and flexible statistical learning algorithm to detect and predict ovulation using data captured by this device. Methods: The system consists of an earpiece, which measures the ear canal temperature every 5 min during night sleep hours, and a base station that transmits data to a smartphone application for analysis. We establish a data-cleaning protocol for data preprocessing and then fit a Hidden Markov Model (HMM) with two hidden states of high and low temperature to identify the more probable state of each time point via the predicted probabilities. Finally, a post-processing procedure is developed to incorporate biorhythm information to form a time-course biphasic profile for each subject. Results: The performance of the proposed algorithms applied to data collected by the device are compared with traditional methods in terms of match rate with self-reported ovulation days confirmed with an ovulation test kit. Empirical study results from a group of 34 users yielded significant improvements over the traditional methods in terms of detection accuracy (with sensitivity 92.31%) and prediction power (23.07-31.55% higher). Conclusion: We demonstrated the feasibility for reliable ovulation detection and prediction with high-frequency temperature data collected by a non-invasive wearable device. Significance: Traditional fertility monitoring methods are often either inaccurate or inconvenient. The wearable device and learning algorithm presented in this paper provide a user friendly and reliable platform for tracking ovulation, which may have a broad impact on both fertility research and real-world family planning.