Heart rate is an important sign of fetal health, yet few technologies exist to easily and inexpensively track fetal heart rates outside of doctors’ offices. This can create risks for pregnancies in low-resource regions where doctors are far away or inaccessible.
A team led by University of Washington researchers has created DopFone, a system that uses an off-the-shelf smartphone’s existing speaker and microphone to accurately estimate fetal heart rate. The phone mimics a Doppler ultrasound, emitting a tone and listening for the subtle variations in its echo caused by fetal heart beats. A machine learning model then estimates the heart rate. In a clinical test with 23 pregnant women, DopFone estimated heart rate with an average error of 2 beats per minute, or bpm. The accepted clinical range is within 8 bpm.
The team published its research Dec. 2 in the Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies.
“Eventually DopFone could let people test fetal heart rate regularly, rather than relying on the intermittent tests at a doctor’s office, or not getting tested at all,” said lead author Poojita Garg, a UW doctoral student in the Paul G. Allen School of Computer Science & Engineering. “Patients might then send this data to doctors so that they can better judge patients’ health when they’re not in a clinic.”
Traditional Doppler ultrasounds, the clinical standard for fetal heart rate monitoring, work by sending high-frequency sound into a person’s body and tracking how the echo changes in frequency. They’re very accurate at measuring fetal heart rate but require costly equipment and a skilled technician to operate it.
To use DopFone, a user places the phone’s microphone against their abdomen for one minute. The phone emits a subaudible 18 kilohertz tone. The team chose this low frequency because — unlike a Doppler’s high frequencies, above 2,000 kilohertz — it sits within the range smartphone microphones can record while still traveling well through tissue. As the tone is reflected through the user’s abdomen, the fetus’s heartbeat creates small shifts in the sound.
A machine learning model then estimates the heart rate using the audio and the patient’s demographic information
The team tested DopFone in UW Medicine’s maternal-fetal medicine division on 23 pregnant patients between 19 and 39 weeks of pregnancy. On average its readings were within 2.1 bpm of the medical Doppler ultrasound. Its accuracy was slightly diminished for patients with high body mass indexes, though those readings were still within normal limits. Because an irregular fetal heartbeat is often an emergency, DopFone was not tested on patients with irregularities.
Next, the team plans to gather more data outside a lab to better train the model. Eventually they want to deploy it as a publicly available app.
“This women’s health space is often overlooked,” Garg said. “So I want to focus on accessible alternatives that can be available to people in low resource areas, whether that’s here in the U.S. or in other countries. Because health belongs to everyone.”
Co-authors include Mingzhuo Ma, a UW graduate student in electrical and computer engineering; Hayley Jane MacKinnon and Dr. Michael Richley, both OB/GYNs in UW Medicine’s maternal-fetal medicine division; and Vikram Iyer, a UW assistant professor in the Allen School. Shwetak Patel, a UW professor in the Allen School and in electrical and computer engineering, and Alexander T. Adams of the Georgia Institute of Technology, were senior authors. This research was funded by the UW Gift Fund.
For more information, contact Garg at pgarg70@uw.edu.