Browsing by Author "He, Xiongxiong"

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    A dementia classification framework using frequency and time-frequency features based on EEG signals
    (IEEE, 2019-04-04) Durongbhan, Pholpat; Zhao, Yifan; Chen, Liangyu; Zis, Panagiotis; De Marco, Matteo; Unwin, Zoe C.; Venneri, Annalena; He, Xiongxiong; Li, Sheng; Zhao, Yitian; Blackburn, Daniel J.; Sarrigiannis, Ptolemaios G.
    Alzheimer’s disease (AD) accounts for 60%–70% of all dementia cases, and clinical diagnosis at its early stage is extremely difficult. As several new drugs aiming to modify disease progression or alleviate symptoms are being developed, to assess their efficacy, novel robust biomarkers of brain function are urgently required. This paper aims to explore a routine to gain such biomarkers using the quantitative analysis of electroencephalography (QEEG). This paper proposes a supervised classification framework that uses EEG signals to classify healthy controls (HC) and AD participants. The framework consists of data augmentation, feature extraction, K-nearest neighbor (KNN) classification, quantitative evaluation, and topographic visualization. Considering the human brain either as a stationary or a dynamical system, both the frequency-based and time–frequency-based features were tested in 40 participants. The results show that: 1) the proposed method can achieve up to a 99% classification accuracy on short (4s) eyes open EEG epochs, with the KNN algorithm that has best performance when compared with alternative machine learning approaches; 2) the features extracted using the wavelet transform produced better classification performance in comparison to the features based on FFT; and 3) in the spatial domain, the temporal and parietal areas offer the best distinction between healthy controls and AD. The proposed framework can effectively classify HC and AD participants with high accuracy, meanwhile offering identification and the localization of significant QEEG features. These important findings and the proposed classification framework could be used for the development of a biomarker for the diagnosis and monitoring of disease progression in AD.
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    A wavelet-based correlation analysis framework to study cerebromuscular activity in essential tremor
    (Hindawi, 2018-07-03) Zhao, Yifan; Laguna, Ramon C.; Zhao, Yitian; Liu, Jimmy Jiang; He, Xiongxiong; Yianni, John; Sarrigiannis, Ptolemaios G.
    Deep brain stimulation (DBS) provides dramatic tremor relief in patients with severe essential tremor (ET). Typically, the VIM nucleus is the most effective brain area to target for high-frequency electrical stimulation in these patients. Correlation analysis between electrical local field potential (LFP) recordings from the thalamic DBS leads and electrical muscle activity from the contralateral tremulous limb has become an attractive practical tool to interpret the LFPs and their association with the tremulous clinical manifestations. Although functional connectivity analysis between brain electrical recordings and electromyographic (EMG) signals from the tremor has been of interest to an increasing number of engineering researchers, there is no well-accepted tailored framework to consistently characterise the association between thalamic electrical recordings and the tremorogenic EMG activity. Methods. This paper proposes a novel framework to address this challenge, including an estimation of the interaction strength using wavelet cross-spectrum and phase lag index while demonstrating the statistical significance of the findings. Results. Consistent results were estimated for single and multiple trials of consecutive or partially overlapping epochs of data. The latter approach reveals a substantial increase on the range of statistically significant dynamic low-frequency interrelationships while decreasing the dynamic range of high-frequency interactions. Conclusion. Results from both simulation and real data demonstrate the feasibility and robustness of the proposed framework. Significance. This study offers the proof of principle required to implement this methodology to uncover VIM thalamic LFP-EMG interactions for (i) better understanding of the pathophysiology of tremor; (ii) objective selection of the DBS electrode contacts with the highest strength of association with the tremorogenic EMG, a particularly useful feature for the implementation of novel multicontact directional leads in clinical practice; and (iii) future research on DBS closed-loop devices.