Scholars@Duke
Ran Xiao

Ran Xiao

Assistant Consulting Professor in the School of Nursing
School of Nursing
ran.xiao@duke.edu
DUMC 3322,307 Trent Drive, Durham, NC 27710
Office hours By appointment only.  

Selected Publications

The role of telemetry monitoring: From diagnosing arrhythmia to predictive models of patient instability

Journal Article Journal of Electrocardiology · March 1, 2025 Over the past sixty years, telemetry monitoring has become integral to hospital care, offering critical insights into patient health by tracking key indicators like heart rate, respiratory rate, blood pressure, and oxygen saturation. Its primary applicatio ... Full text Cite

Digital Health Innovations to Promote Physical Activities in Patients with Cancer.

Journal Article Stud Health Technol Inform · July 24, 2024 This study evaluates the Exercise Planning and Tracking (EPT) tool, a digital health solution, designed to enhance physical activity (PA) in patients with cancer. Utilizing a tailored PA database, the tool aids in tracking and planning exercises and monito ... Full text Link to item Cite

Enabling Pre-Shock State Detection using Electrogram Signals from Implantable Cardioverter-Defibrillators

Conference WWW 2024 Companion - Companion Proceedings of the ACM Web Conference · May 13, 2024 Identifying electrical signatures preceding a ventricular arrhythmia from the implantable cardioverter-defibrillators (ICDs) can help predict an upcoming ICD shock. To achieve this, we first deployed a large-scale study (N=326) to continuously monitor the ... Full text Cite

Learning From Alarms: A Robust Learning Approach for Accurate Photoplethysmography-Based Atrial Fibrillation Detection Using Eight Million Samples Labeled With Imprecise Arrhythmia Alarms.

Journal Article IEEE journal of biomedical and health informatics · May 2024 Atrial fibrillation (AF) is a common cardiac arrhythmia with serious health consequences if not detected and treated early. Detecting AF using wearable devices with photoplethysmography (PPG) sensors and deep neural networks has demonstrated some success u ... Full text Cite

Photoplethysmography based atrial fibrillation detection: a continually growing field.

Journal Article Physiological measurement · April 2024 Objective. Atrial fibrillation (AF) is a prevalent cardiac arrhythmia associated with significant health ramifications, including an elevated susceptibility to ischemic stroke, heart disease, and heightened mortality. Photoplethysmography (PPG) has ... Full text Cite

Associations between EEG power and coherence with cognition and early precursors of speech and language development across the first months of life.

Journal Article PloS one · January 2024 The neural processes underpinning cognition and language development in infancy are of great interest. We investigated EEG power and coherence in infancy, as a reflection of underlying cortical function of single brain region and cross-region connectivity, ... Full text Cite

SQUWA: Signal Quality Aware DNN Architecture for Enhanced Accuracy in Atrial Fibrillation Detection from Noisy PPG Signals

Conference Proceedings of Machine Learning Research · January 1, 2024 Atrial fibrillation (AF), a common cardiac arrhythmia, significantly increases the risk of stroke, heart disease, and mortality. Photoplethysmography (PPG) offers a promising solution for continuous AF monitoring, due to its cost efficiency and integration ... Cite

Integrating multimodal information in machine learning for classifying acute myocardial infarction.

Journal Article Physiological measurement · April 2023 Objective. Prompt identification and recognization of myocardial ischemia/infarction (MI) is the most important goal in the management of acute coronary syndrome. The 12-lead electrocardiogram (ECG) is widely used as the initial screening tool for p ... Full text Cite

Log-Spectral Matching GAN: PPG-based Atrial Fibrillation Detection can be Enhanced by GAN-based Data Augmentation with Integration of Spectral Loss.

Journal Article IEEE journal of biomedical and health informatics · January 2023 Photoplethysmography (PPG) is a ubiquitous physiological measurement that detects beat-to-beat pulsatile blood volume changes and hence has a potential for monitoring cardiovascular conditions, particularly in ambulatory settings. A PPG dataset that is cre ... Full text Cite

Impact of Label Noise on the Learning Based Models for a Binary Classification of Physiological Signal.

Journal Article Sensors (Basel, Switzerland) · September 2022 Label noise is omnipresent in the annotations process and has an impact on supervised learning algorithms. This work focuses on the impact of label noise on the performance of learning models by examining the effect of random and class-dependent label nois ... Full text Cite

Time Synchronization of Multimodal Physiological Signals through Alignment of Common Signal Types and Its Technical Considerations in Digital Health.

Journal Article Journal of imaging · April 2022 BackgroundDespite advancements in digital health, it remains challenging to obtain precise time synchronization of multimodal physiological signals collected through different devices. Existing algorithms mainly rely on specific physiological feat ... Full text Cite

Characterization of Parkinson's Disease Subtypes and Related Attributes.

Journal Article Frontiers in neurology · January 2022 Parkinson's disease is a progressive neurodegenerative disease with complex, heterogeneous motor and non-motor symptoms. The current evidence shows that there is still a marked heterogeneity in the subtyping of Parkinson's disease using both clinical and d ... Full text Cite

Technical considerations for evaluating clinical prediction indices: a case study for predicting code blue events with MEWS.

Journal Article Physiological measurement · June 2021 Objective.There have been many efforts to develop tools predictive of health deterioration in hospitalized patients, but comprehensive evaluation of their predictive ability is often lacking to guide implementation in clinical practice. In this work ... Full text Cite

Electroencephalography measures of relative power and coherence as reaching skill emerges in infants born preterm.

Journal Article Scientific reports · February 2021 Electroencephalography (EEG) measures of relative power and coherence are associated with motor experience in infants with typical development, but these relationships have not been assessed in infants born preterm. The goal of our study was to investigate ... Full text Cite

Explainability Metrics of Deep Convolutional Networks for Photoplethysmography Quality Assessment.

Journal Article IEEE access : practical innovations, open solutions · January 2021 Photoplethysmography (PPG) is a noninvasive way to monitor various aspects of the circulatory system, and is becoming more and more widespread in biomedical processing. Recently, deep learning methods for analyzing PPG have also become prevalent, achieving ... Full text Cite

A Supervised Approach to Robust Photoplethysmography Quality Assessment.

Journal Article IEEE journal of biomedical and health informatics · March 2020 Early detection of Atrial Fibrillation (AFib) is crucial to prevent stroke recurrence. New tools for monitoring cardiac rhythm are important for risk stratification and stroke prevention. As many of new approaches to long-term AFib detection are now based ... Full text Cite

Generalizability of SuperAlarm via Cross-Institutional Performance Evaluation.

Journal Article IEEE access : practical innovations, open solutions · January 2020 Bedside patient monitors are ubiquitous tools in modern critical care units to provide timely patient status. However, current systems suffer from high volume of false alarms leading to alarm fatigue, one of top technical hazards in clinical settings. Many ... Full text Cite

Characterization of pallidocortical motor network in Parkinson's disease through complex network analysis.

Journal Article Journal of neural engineering · November 2019 ObjectiveDeep brain stimulation (DBS) has been demonstrated by numerous clinical trials to be an advanced therapy for selected patients with Parkinson's disease (PD), while its maximal therapeutic effect is capped by the inadequate understanding o ... Full text Cite

Continuous monitoring of cerebrovascular reactivity through pulse transit time and intracranial pressure.

Journal Article Physiological measurement · January 2019 ObjectiveCerebrovascular reactivity (CR) is a mechanism that maintains stable blood flow supply to the brain. Pressure reactivity index (PRx), the correlation coefficient between slow waves of invasive arterial blood pressure (ABP) and intracrania ... Full text Cite

Monitoring significant ST changes through deep learning.

Journal Article Journal of electrocardiology · November 2018 Full text Cite

Relationships between variance in electroencephalography relative power and developmental status in infants with typical development and at risk for developmental disability: An observational study

Journal Article Gates Open Research · September 11, 2018 Background: Electroencephalography (EEG) is a non-invasive tool that has the potential to identify and quantify atypical brain development. We introduce a new measure here, variance of relative power of resting-state EEG. We sou ... Full text Cite

Predict In-Hospital Code Blue Events using Monitor Alarms through Deep Learning Approach.

Conference Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · July 2018 Bedside monitors in hospital intensive care units (ICUs) are known to produce excessive false alarms that could desensitize caregivers, resulting in delayed or even missed clinical interventions to life-threatening events. Our previous studies proposed a f ... Full text Cite

Evaluation of ECG algorithms designed to improve detect of transient myocardial ischemia to minimize false alarms in patients with suspected acute coronary syndrome.

Journal Article Journal of electrocardiology · March 2018 BackgroundPatients hospitalized for suspected acute coronary syndrome (ACS) are at risk for transient myocardial ischemia. During the "rule-out" phase, continuous ECG ST-segment monitoring can identify transient myocardial ischemia, even when asym ... Full text Cite

Electroencephalography power and coherence changes with age and motor skill development across the first half year of life.

Journal Article PloS one · January 2018 Existing research in infants has correlated electroencephalography (EEG) measures of power and coherence to cognitive development and to locomotor experience, but only in infants older than 5 months of age. Our goal was to explore the relationship between ... Full text Cite

Relationships between variance in electroencephalography relative power and developmental status in infants with typical development and at risk for developmental disability: An observational study.

Journal Article Gates open research · January 2018 Background: Electroencephalography (EEG) is a non-invasive tool that has the potential to identify and quantify atypical brain development. We introduce a new measure here, variance of relative power of resting-state EEG. We sought to assess whether ... Full text Cite

Characterization of infant mu rhythm immediately before crawling: A high-resolution EEG study.

Journal Article NeuroImage · February 2017 Crawling is an important milestone in infant motor development. However, infants with developmental motor disorders can exhibit delays, or even miss, in the acquisition of crawling skill. And little information is available from the neurodevelopmental doma ... Full text Cite

Combining multiple features for error detection and its application in brain-computer interface.

Journal Article Biomedical engineering online · February 2016 BackgroundBrain-computer interface (BCI) is an assistive technology that conveys users' intentions by decoding various brain activities and translating them into control commands, without the need of verbal instructions and/or physical interaction ... Full text Cite

EEG resolutions in detecting and decoding finger movements from spectral analysis.

Journal Article Frontiers in neuroscience · January 2015 Mu/beta rhythms are well-studied brain activities that originate from sensorimotor cortices. These rhythms reveal spectral changes in alpha and beta bands induced by movements of different body parts, e.g., hands and limbs, in electroencephalography (EEG) ... Full text Cite

Spectra of infant EEG within the first year of life: A pilot study.

Conference Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · January 2015 Rhythmic activities in electroencephalography (EEG) have been extensively studied in adults and classic rhythms are found to correlate with specific human brain functions. However, less has been investigated in infant EEG, and EEG rhythms in infants at ear ... Full text Cite

Decoding individual finger movements from one hand using human EEG signals.

Journal Article PloS one · January 2014 Brain computer interface (BCI) is an assistive technology, which decodes neurophysiological signals generated by the human brain and translates them into control signals to control external devices, e.g., wheelchairs. One problem challenging noninvasive BC ... Full text Cite

Classification of finger pairs from one hand based on spectral features in human EEG.

Conference Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · January 2014 Individual finger movements are well-articulated movements of fine body parts, the successful decoding of which can provide extra degrees of freedom to drive brain computer interface (BCI) applications. Past studies present some unique features revealed fr ... Full text Cite

Evaluation of EEG features in decoding individual finger movements from one hand.

Journal Article Computational and mathematical methods in medicine · January 2013 With the advancements in modern signal processing techniques, the field of brain-computer interface (BCI) is progressing fast towards noninvasiveness. One challenge still impeding these developments is the limited number of features, especially movement-re ... Full text Cite

Discriminating multiple motor imageries of human hands using EEG.

Conference Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · January 2012 We investigated the feasibility of discriminating four different motor imagery (MI) types from both hands using electroencephalography (EEG) through exploring underlying features related to MIs of thumb and fist from one hand. New spectral and spatial feat ... Full text Cite

Cross-Institutional Evaluation of SuperAlarm Algorithm for Predicting In-Hospital Code Blue Events

Conference Background Bedside patient monitors are essential tools in acute care settings that provide timely information about patients’ physiologic condition. However, current patient monitors are known to produce excessive alarms with majority of them being either ... Cite