ABSTRACT
Ambulatory monitoring devices are enabling a new paradigm of health care by collecting and analyzing long-term data for reliable diagnostics. These devices are becoming increasingly popular for continuous monitoring of cardiac diseases. Recent advancements have enabled solutions that are both affordable and reliable, allowing monitoring of vulnerable populations from the comfort of their homes. They provide early detection of important physiological events, leading to timely alerts for seeking medical attention. In this review, the authors aim to summarize the recent developments in the area of ambulatory and remote monitoring solutions for cardiac diagnostics. The authors cover solutions based on wearable devices, smartphones, and other ambulatory sensors. The authors also present an overview of the limitations of current technologies, their effectiveness, and their adoption in the general population, and discuss some of the recently proposed methods to overcome these challenges. Lastly, we discuss the possibilities opened by this new paradigm, for the future of health care and personalized medicine.
Subject(s)
Blood Pressure Monitoring, Ambulatory , Electrocardiography, Ambulatory , Wearable Electronic Devices , Arrhythmias, Cardiac/diagnosis , Cardiorespiratory Fitness , Exercise Test , Humans , Smartphone , Technology TransferABSTRACT
Repolarization alternans (RA) has been implicated in the pathogenesis of ventricular arrhythmias and sudden cardiac death. We developed a 12-lead, blue-tooth/Smart-Phone (Android) based electrocardiogram (ECG) acquisition and monitoring system (cvrPhone), and an application to estimate RA, in real-time. In in-vivo swine studies (N = 17), 12-lead ECG signals were recorded at baseline and following coronary artery occlusion. RA was estimated using the Fast Fourier Transform (FFT) method using a custom developed algorithm in JAVA. Underlying ischemia was detected using a custom developed ischemic index. RA from each lead showed a significant (p < 0.05) increase within 1 min of occlusion compared to baseline (n = 29). Following myocardial infarction, spontaneous ventricular tachycardia episodes (n = 4) were preceded by significant (p < 0.05) increase of RA prior to the onset of the tachy-arrhythmias. Similarly, the ischemic index exhibited a significant increase following myocardial infarction (p < 0.05) and preceding a tachy-arrhythmic event. In conclusion, RA can be effectively estimated using surface lead electrocardiograms by analyzing beat-to-beat variability in ECG morphology using a smartphone based platform. cvrPhone can be used to detect myocardial ischemia and arrhythmia susceptibility using a user-friendly, clinically acceptable, mobile platform.
Subject(s)
Arrhythmias, Cardiac/diagnosis , Death, Sudden, Cardiac/pathology , Monitoring, Physiologic , Smartphone , Algorithms , Animals , Arrhythmias, Cardiac/physiopathology , Death, Sudden, Cardiac/prevention & control , Disease Models, Animal , Electrocardiography , Heart Ventricles/physiopathology , Humans , Myocardial Infarction/diagnosis , Myocardial Infarction/physiopathology , Swine , Tachycardia, VentricularABSTRACT
BACKGROUND: Sleep disordered breathing manifested as sleep apnea (SA) is prevalent in the general population, and while it is associated with increased morbidity and mortality risk in some patient populations, it remains under-diagnosed. The objective of this study was to assess the accuracy of respiration-rate (RR) and tidal-volume (TV) estimation algorithms, from body-surface ECG signals, using a smartphone based ambulatory respiration monitoring system (cvrPhone). METHODS: Twelve lead ECG signals were collected using the cvrPhone from anesthetized and mechanically ventilated swine (n = 9). During ECG data acquisition, the mechanical ventilator tidal-volume (TV) was varied from 250 to 0 to 750 to 0 to 500 to 0 to 750 ml at respiratory rates (RR) of 6 and 14 breaths/min, respectively, and the RR and TV values were estimated from the ECG signals using custom algorithms. RESULTS: TV estimations from any two different TV settings showed statistically significant difference (p < 0.01) regardless of the RR. RRs were estimated to be 6.1±1.1 and 14.0±0.2 breaths/min at 6 and 14 breaths/min, respectively (when 250, 500 and 750 ml TV settings were combined). During apnea, the estimated TV and RR values were 11.7±54.9 ml and 0.0±3.5 breaths/min, which were significantly different (p<0.05) than TV and RR values during non-apnea breathing. In addition, the time delay from the apnea onset to the first apnea detection was 8.6±6.7 and 7.0±3.2 seconds for TV and RR respectively. CONCLUSIONS: We have demonstrated that apnea can reliably be detected using ECG-derived RR and TV algorithms. These results support the concept that our algorithms can be utilized to detect SA in conjunction with ECG monitoring.
