ABSTRACT
Human bodily mechanisms and functions produce low-frequency vibrations. Our ability to perceive these vibrations is limited by our range of hearing. However, in-ear infrasonic hemodynography (IH) can measure low-frequency vibrations (<20 Hz) created by vital organs as an acoustic waveform. This is captured using a technology that can be embedded into wearable devices such as in-ear headphones. IH can acquire sound signals that travel within arteries, fluids, bones, and muscles in proximity to the ear canal, allowing for measurements of an individual's unique audiome. We describe the heart rate and heart rhythm results obtained in time-series analysis of the in-ear IH data taken simultaneously with ECG recordings in two dedicated clinical studies. We demonstrate a high correlation (r = 0.99) between IH and ECG acquired interbeat interval and heart rate measurements and show that IH can continuously monitor physiological changes in heart rate induced by various breathing exercises. We also show that IH can differentiate between atrial fibrillation and sinus rhythm with performance similar to ECG. The results represent a demonstration of IH capabilities to deliver accurate heart rate and heart rhythm measurements comparable to ECG, in a wearable form factor. The development of IH shows promise for monitoring acoustic imprints of the human body that will enable new real-time applications in cardiovascular health that are continuous and noninvasive.
ABSTRACT
INTRODUCTION: Adjudication of thousands of implantable cardioverter defibrillator (ICD)-treated arrhythmia episodes is labor intensive and, as a result, is most often left undone. The objective of this study was to evaluate an automatic classification algorithm for adjudication of ICD-treated arrhythmia episodes. METHODS: The algorithm uses a machine learning algorithm and was developed using 776 arrhythmia episodes. The algorithm was validated on 131 dual-chamber ICD shock episodes from 127 patients adjudicated by seven electrophysiologists (EPs). Episodes were classified by panel consensus as ventricular tachycardia/ventricular fibrillation (VT/VF) or non-VT/VF, with the resulting classifications used as the reference. Subsequently, each episode electrogram (EGM) data was randomly assigned to three EPs without the atrial lead information, and to three EPs with the atrial lead information. Those episodes were also classified by the automatic algorithm with and without atrial information. Agreement with the reference was compared between the three EPs consensus group and the algorithm. RESULTS: The overall agreement with the reference was similar between three-EP consensus and the algorithm for both with atrial EGM (94% vs 95%, P = 0.87) and without atrial EGM (90% vs 91%, P = 0.91). The odds of accurate adjudication, after adjusting for covariates, did not significantly differ between the algorithm and EP consensus (odds ratio 1.02, 95% confidence interval: 0.97-1.06). CONCLUSIONS: This algorithm performs at a level comparable to an EP panel in the adjudication of arrhythmia episodes treated by both dual- and single-chamber ICDs. This type of algorithm has the potential for automated analysis of clinical ICD episodes, and adjudication of EGMs for research studies and quality analyses.
