Proving the Business Case for the Internet of Things

Bathroom scale from Georgia Tech monitors heart disease

Steve Rogerson
October 1, 2019

A bathroom scale developed by the Georgia Institute of Technology could monitor millions with heart failure.
The device asks the user to step onto the scale and touch the metal pads. It records an electrocardiogram (ECG) from the fingers and circulation pulsing that makes the body subtly bob up and down on the scale. Machine-learning tools compute whether heart failure symptoms have worsened.
This is how researchers at the Georgia Tech envision their experimental device reaching patients someday and, in a new study, they reported proof-of-concept success in recording and processing data from 43 patients with heart failure. A future marketable version of the medical monitoring scale would ideally notify a doctor, who would call the patient to adjust medication at home, hopefully sparing a long hospital stay and needless suffering.
The pulsing and bobbing signal is called a ballistocardiogram (BCG), a measurement researchers took more commonly about 100 years ago but gave up on as imaging technology far surpassed it. The researchers are making it useful again with modern computation.
“Our work is the first time that BCGs have been used to classify the status of heart failure patients,” said Omer Inan, the study’s principal investigator and an associate professor in Georgia Tech’s School of Electrical & Computer Engineering.
Heart failure affects 6.5 million Americans and is a slow-progressing disease, in which the heart works less and less effectively. Many people know it as congestive heart failure because a major symptom is fluid build up, which can overwhelm the lungs, impeding breathing and possibly causing death.
Patients endure repeat hospitalisations to adjust medications when their condition dips, or decompensates, making heart failure a major driver of hospital admissions and healthcare costs. Home monitoring reduces hospitalisations but currently requires an invasive procedure.
Georgia Tech research was behind the launch of such an implantable heart failure home monitoring device in 2011. But this method would potentially dispense with the procedure, cost much less, and be much simpler to use, lowering patients’ resistance to home monitoring.
Given its early stage, the study’s BCG-ECG scale performed well in hospital tests but also in in-home tests, which was promising, since it principally targets eventual home use.
The research team, which included collaborators from the University of California, San Francisco, and Northwestern University, published their results in the journal IEEE Transactions on Biomedical Engineering. The research was funded by the National Heart, Lung & Blood Institute at the National Institutes of Health.
The ECG part of the experimental scale is not new nor its great diagnostic information, but it alone does not say enough about heart failure. The BCG part is mostly new, and it appears valuable to heart failure monitoring but also challenging to record and interpret.
“The ECG has characteristic waves that clinicians have understood for 100 years and, now, computers read it a lot of the time,” Inan said. “Elements of the BCG signal aren’t really known well yet, and they haven’t been measured in patients with heart failure very much at all.”
The ECG is electrical; the body conducts its signals well, and the recordings are clear.
The BCG is a mechanical signal; body fat dampens it, and it faces a lot of interference in the body such as tissue variations and muscle movement. BCGs are also noisier in people with cardiovascular disease.
Patients with heart failure tend to be feebler and, initially, the researchers worried they would wobble on scales during home tests, adding even more noise to the BCGs. But the recordings were very productive.
Though a BCG read-out is scribble compared with an ECG’s near-uniform etchings, BCGs have some patterns that parallel an ECG’s. For example, the big upward spike in an ECG is followed by the BCG’s big J-wave.
The researchers processed BCGs with three machine- learning algorithms, revealing patterns that differ when a patient’s heart failure is compensated, that is, healthier, from when it is decompensated.
“In someone with decompensated heart failure, the cardiovascular system can no longer compensate for the reduced heart function, and then the flow of blood through the arteries is more disorderly, and we see it in the mechanical signal of the BCG,” Inan said. “That difference does not show up in the ECG because it’s an electrical signal. The most important characteristic was the degree to which the BCG is variable, which would mean inconsistent blood flow. If you chop up the recording into 20-second intervals and the individual segments differ from each other a lot, that’s a good marker of decompensation.”
These researchers coauthored the study: James Rehg, Burak Aydemir, Supriya Nagesh and Mobashir Hasan Shandhi from Georgia Tech; Joanna Fan and Liviu Klein from the University of California, San Francisco; Mozziyar Etemadi and Alex Heller from Northwestern University.
The picture shows what the experimental scale looks like. Study collaborator Liviu Klein is on the right and the picture comes from the University of California, San Francisco.