Xinyang Li

Xinyang Li
Imperial College London | Imperial · National Heart and Lung Institute

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44
Publications
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294
Citations

Publications

Publications (44)
Article
Full-text available
Aims Implantable cardioverter defibrillator (ICD) therapies have been associated with increased mortality and should be minimized when safe to do so. We hypothesized that machine learning-derived ventricular tachycardia (VT) cycle length (CL) variability metrics could be used to discriminate between sustained and spontaneously terminating VT. Meth...
Article
Understanding the mechanism sustaining cardiac fibrillation can facilitate the personalization of treatment. Granger causality analysis can be used to determine the existence of a hierarchical fibrillation mechanism that is more amenable to ablation treatment in cardiac time-series data. Conventional Granger causality based on linear predictability...
Article
Full-text available
There is increasing focus on applying deep learning methods to electrocardiograms (ECGs), with recent studies showing that neural networks (NNs) can predict future heart failure or atrial fibrillation from the ECG alone. However, large numbers of ECGs are needed to train NNs, and many ECGs are currently only in paper format, which are not suitable...
Article
Background Obesity is a growing global health problem that confers higher risks of atrial arrhythmias and sudden cardiac death. Despite this, the proarrhythmic substrate in obesity and its reversibility with weight loss has not been studied in-depth. Purpose To characterise the proarrhythmic substrate in obese patients, and its reversibility with...
Article
Full-text available
Background Small-scale studies have linked obesity (Ob) and metabolic ill-health with proarrhythmic repolarisation abnormalities. Whether these are observed at a population scale, modulated by individuals’ genetics, and confer higher risks of ventricular arrhythmias (VA) are not known. Methods and Results Firstly, using the UK Biobank, the associa...
Conference Paper
Background Obesity confers higher risks of atrial arrhythmias and sudden cardiac death. Despite this, the proarrhythmic substrate in obesity and its reversibility with weight loss has not been studied in-depth. To address this, the proarrhythmic substrate in obese patients, and its reversibility with bariatric surgery, was characterised using elect...
Article
Funding Acknowledgements Type of funding sources: Public Institution(s). Main funding source(s): National Institute for Health Research (NIHR) British Heart Foundation Background Obesity confers higher risks of atrial arrhythmias and is associated with abnormal ventricular repolarisation. Despite this, the proarrhythmic substrate in obesity and it...
Article
Full-text available
Background: Atrial fibrillation (AF) and ventricular fibrillation (VF) are complex heart rhythm disorders and may be sustained by distinct electrophysiological mechanisms. Disorganised self-perpetuating multiple-wavelets and organised rotational drivers (RDs) localising to specific areas are both possible mechanisms by which fibrillation is sustain...
Article
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Background Obesity is associated with electrophysiological remodeling, which manifests as detectable changes on the surface electrocardiogram (ECG). Objective To develop neural networks (NN) to predict body mass index (BMI) from ECGs and test the hypothesis that discrepancies between NN-predicted BMI and measured BMI are indicative of underlying a...
Article
Full-text available
Background Although obesity, defined by body mass index (BMI), has been associated with a higher risk of hospitalisation and more severe course of illness in Covid-19 positive patients amongst the British population, it is unclear if this translates into increased mortality. Furthermore, given that BMI is an insensitive indicator of adiposity, the...
Article
Full-text available
We describe a human and large animal Langendorff experimental apparatus for live electrophysiological studies and measure the electrophysiological changes due to gap junction uncoupling in human and porcine hearts. The resultant ex vivo intact human and porcine model can bridge the translational gap between smaller simple laboratory models and clin...
Article
Full-text available
Current treatment approaches for persistent atrial fibrillation (AF) have a ceiling of success of around 50%. This is despite 15 years of developing adjunctive ablation strategies in addition to pulmonary vein isolation to target the underlying arrhythmogenic substrate in AF. A major shortcoming of our current approach to AF treatment is its predom...
Article
Full-text available
Aims: Conflicting data exist supporting differing mechanisms for sustaining ventricular fibrillation (VF), ranging from disorganised multiple-wavelet activation to organised rotational activities (RAs). Abnormal gap junction (GJ) coupling and fibrosis are important in initiation and maintenance of VF. We investigated whether differing ventricular...
Article
Full-text available
Background - The mechanisms sustaining myocardial fibrillation remain disputed, partly due to a lack of mapping tools that can accurately identify the mechanism with low spatial resolution clinical recordings. Granger causality (GC) analysis, an econometric tool for quantifying causal relationships between complex time-series, was developed as a no...
Chapter
Full-text available
Atrial fibrillation (AF) and ventricular fibrillation (VF) are complex heart rhythm disorders with increasing prevalence. Mechanisms sustaining these arrhythmias are different, and subsequently, the required treatments options differ. Although many algorithms have been developed for differentiating fibrillation from normal sinus rhythm, very few me...
Article
Full-text available
The analysis of complex mechanisms underlying ventricular fibrillation (VF) and atrial fibrillation (AF) requires sophisticated tools for studying spatio-temporal action potential (AP) propagation dynamics. However, fibrillation analysis tools are often custom-made or proprietary, and vary between research groups. With no optimal standardised frame...
Article
Background Ventricular fibrosis is known to play a critical role in initiation and maintenance of ventricular fibrillation (VF). Post myocardial infarction the quantity of fibrosis negatively correlates with survival. There is a lack of data on how the quantity and degree of fibrosis influences the mechanisms of VF itself. VF mechanisms remain deba...
Article
Background Clinical identification of fibrillation drivers remains challenging in both atrial and ventricular fibrillation (VF). In this study, we developed novel tools using granger causality (GC) analysis for quantifying the causal relationship between neighbouring fibrillatory signals. We tested whether it was adaptable to low resolution, limite...
Article
Background The underlying mechanism of ventricular fibrillation (VF) remains unclear. There are both experimental and clinical data to support the existence of rotational drivers (RDs), though other opposing studies suggest that VF is the result of disorganized myocardial activation. Abnormal electrical coupling between cardiomyocytes through gap j...
Conference Paper
Full-text available
Background Mechanisms that maintain ventricular fibrillation (VF) remain debated. There are data supporting both critical areas sustaining rotational drivers (RDs) and the contrary hypothesis of disorganised myocardial activation driving VF. Abnormal electrical coupling between cardiomyocytes through gap junctions (GJ) and ventricular fibrosis are...
Article
Full-text available
Background: Dissimilar ventricular rhythms refer to the occurrence of different ventricular tachyarrhythmias in the right and left ventricles or different rates of the same tachyarrhythmia in the two ventricles. Objective: We investigated the inducibility of dissimilar ventricular rhythms, their underlying mechanisms, and the impact of anti-arrhyth...
Data
Phase maps of dissimilar ventricular rhythms (VT in RV and VF in LV).
Chapter
Different mental states result in different synchronizations or desynchronizations between multiple brain regions, and subsequently, electroencephalogram (EEG) connectivity analysis gains increasing attention in brain computer interfaces (BCIs). Conventional connectivity analysis is usually conducted at the scalp-level and in an unsupervised manner...
Article
Full-text available
Atrial and ventricular fibrillation are complex arrhythmias, and their underlying mechanisms remain widely debated and incompletely understood. This is partly because the electrical signals recorded during myocardial fibrillation are themselves complex and difficult to interpret with simple analytical tools. There are currently a number of analytic...
Article
Full-text available
Electroencephalogram (EEG) has widely been used to monitor subjects/patients’ mental states. Using the monitor results as feedback, neuro-feedback enables patients to learn to regulate their physiological and psychological states so that improvements in physical and psychological subjects/patients’ states could be achieved. By analyzing EEG compone...
Article
Full-text available
To detect the mental task of interest, spatial filtering has been widely used to enhance the spatial resolution of electroencephalography (EEG). However, the effectiveness of spatial filtering is undermined due to the significant nonstationarity of EEG. Based on regularization, most of the conventional stationary spatial filter design methods addre...
Article
Full-text available
Electrooculogram (EOG) artifact contamination is a common critical issue in general electroencephalogram (EEG) studies as well as in brain computer interface (BCI) research. It is especially challenging when dedicated EOG channels are unavailable or when there are very few EEG channels available for ICA-based ocular artifact removal. It is even mor...
Conference Paper
Assessment and recognition of perceived well-being has wide applications in the development of assistive healthcare systems for people with physical and mental disorders. In practical data collection, these systems need to be less intrusive, and respect users' autonomy and willingness as much as possible. As a result, self-reported data are not nec...
Article
Full-text available
Objective: Session-to-session nonstationarity is inherent in brain-computer interfaces based on electroencephalography. The objective of this paper is to quantify the mismatch between the training model and test data caused by nonstationarity and to adapt the model towards minimizing the mismatch. Approach: We employ a tensor model to estimate t...
Conference Paper
Full-text available
To address the nonstationarity issue in EEG-based brain computer interface (BCI), the computational model trained using the training data needs to adapt to the data from the test sessions. In this paper, we propose a novel adaptation approach based on the divergence framework. Cross-session changes can be taken into consideration by searching the d...
Conference Paper
Full-text available
The non-stationarity inherent across sessions recorded on different days poses a major challenge for practical electroencephalography (EEG)-based Brain Computer Interface (BCI) systems. To address this issue, the computational model trained using the training data needs to adapt to the data from the test sessions. In this paper, we propose a novel...
Conference Paper
Full-text available
This paper addresses the key issue of discriminative feature extraction of electroencephalogram (EEG) signals in brain-computer interfaces. Recent advances in neuroscience indicate that multiple brain regions can be activated during motor imagery. The signal propagation among the regions can give rise to spurious effects in identifying event-relate...
Article
Full-text available
Effective learning and recovery of relevant source brain activity patterns is a major challenge to brain-computer interface using scalp EEG. Various spatial filtering solutions have been developed. Most current methods estimate an instantaneous demixing with the assumption of uncorrelatedness of the source signals. However, recent evidence in neuro...
Conference Paper
In this paper, motor imagery electroencephalograph classification problem is investigated and a method which modifies the projection matrix is proposed based on common spatial pattern analysis. Exceptional samples are detected through examining the features generated by the projection matrix in the first place, which are special in terms that the p...
Conference Paper
In this paper, the functional connectivity network of motor imagery based on EEG is investigated to understand brain function during motor imagery. In particular, partial directed coherence and directed transfer function measurements are applied to multi-channel EEG data to find out event related connectivity pattern with the direction and strength...
Article
In this paper, an approach of feature extraction by designing common spatial filters specifically for time domain parameters (TDP) is proposed. This approach is aiming at motor imagery detection in electroencephalogram (EEG). Particularly, this method calculates the derivatives of the original signals and then applies common spatial analysis (CSP)...
Conference Paper
Full-text available
In this paper, model-free impedance control is designed for the safe human-robot interaction. A passive impedance model is imposed on the robot and a control method is proposed to guarantee the robot dynamics governed by the target model. The proposed method does not require any model information except for upper bounds of system matrix. It is thus...
Article
In this paper, fusion of Common Spatial Pattern (CSP) analysis derived features and Time-Domain Parameters (TDP) is investigated for two-class motion imagery classification: left hand movement and right hand movement imagery. This fusion method is aiming at improving the performance of Brain- Computer Interface (BCI) systems in translating human th...

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