Detecting heart failure using wearables: a pilot study

• Type of Material: Article
• Authors: Amit Shah, Emory University; Nino Isakadze, Johns Hopkins University; Oleksiy Levantsevych, Emory University; Adriana Vest, Emory University; Gari Clifford, Emory University; Shamim Nemati, Emory University
• Language: English
• Date: 2020-04-01
• Publisher: IOP PUBLISHING LTD
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2020 Institute of Physics and Engineering in Medicine
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1088/1361-6579/ab7f93
• Title of Journal or Parent Work: PHYSIOLOGICAL MEASUREMENT
• Volume: 41
• Issue: 4
• Start Page: 044001
• End Page: 044001
• Keywords: Science & Technology; heart failure; Life Sciences & Biomedicine; Engineering, Biomedical; Biophysics; Engineering; mobile health technology; Physiology; Technology; biomedical informatics

Abstract

Objective: Heart failure (HF) can be difficult to diagnose by physical examination alone. We examined whether wristband technologies may facilitate more accurate bedside testing. Approach: We studied on a cohort of 97 monitored in-patients and performed a cross-sectional analysis to predict HF with data from the wearable and other clinically available data. We recorded photoplethysmography (PPG) and accelerometry data using the wearable at 128 samples per second for 5 min. HF diagnosis was ascertained via chart review. We extracted four features of beat-to-beat variability and signal quality, and used them as inputs to a machine learning classification algorithm. Main results: The median [interquartile] age was 60 [51 68] years, 65% were men, and 54% had heart failure; in addition, 30% had acutely decompensated HF. The best 10-fold cross-validated testing performance for the diagnosis of HF was achieved using a support vector machine. The waveform-based features alone achieved a pooled test area under the curve (AUC) of 0.80; when a high-sensitivity cut-point (90%) was chosen, the specificity was 50%. When adding demographics, medical history, and vital signs, the AUC improved to 0.87, and specificity improved to 72% (90% sensitivity). Significance: In a cohort of monitored in-patients, we were able to build an HF classifier from data gathered on a wristband wearable. To our knowledge, this is the first study to demonstrate an algorithm using wristband technology to classify HF patients. This supports the use of such a device as an adjunct tool in bedside diagnostic evaluation and risk stratification.