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Wing Lok Au

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3 papers
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3

YNICL Journal 2017 Journal Article

Associations of hippocampal subfields in the progression of cognitive decline related to Parkinson's disease

  • Heidi Foo
  • Elijah Mak
  • Russell Jude Chander
  • Aloysius Ng
  • Wing Lok Au
  • Yih Yian Sitoh
  • Louis C.S. Tan
  • Nagaendran Kandiah

OBJECTIVE: Hippocampal atrophy has been associated with mild cognitive impairment (MCI) in Parkinson's disease (PD). However, literature on how hippocampal atrophy affects the pathophysiology of cognitive impairment in PD has been limited. Previous studies assessed the hippocampus as an entire entity instead of their individual subregions. We studied the progression of cognitive status in PD subjects over 18 in relation to hippocampal subfields atrophy. METHODS: 65 PD subjects were included. Using the MDS task force criteria, PD subjects were classified as either having no cognitive impairment (PD-NCI) or PD-MCI. We extended the study by investigating the hippocampal subfields atrophy patterns in those who converted from PD-NCI to PD-MCI (PD-converters) compared to those who remained cognitively stable (PD-stable) over 18 months. Freesurfer 6.0 was used to perform the automated segmentation of the hippocampus into thirteen subregions. RESULTS: PD-MCI showed lower baseline volumes in the left fimbria, right CA1, and right HATA; and lower global cognition scores compared to PD-NCI. Baseline right CA1 was also correlated with baseline attention. Over 18 months, decline in volumes of CA2-3 and episodic memory were also seen in PD-converters compared to PD-stable. Baseline volumes of GC-DG, right CA4, left parasubiculum, and left HATA were predictive of the conversion from PD-NCI to PD-MCI. CONCLUSION: The findings from this study add to the anatomical knowledge of hippocampal subregions in PD, allowing us to understand the unique functional contribution of each subfield. Structural changes in the hippocampus subfields could be early biomarkers to detect cognitive impairment in PD.

ICRA Conference 2011 Conference Paper

Using electromechanical delay for real-time anti-phase tremor attenuation system using Functional Electrical Stimulation

  • Ferdinan Widjaja
  • Cheng Yap Shee
  • Wing Lok Au
  • Philippe Poignet
  • Wei Tech Ang

In this paper, we propose a novel anti-phase tremor compensation method using surface electromyography (SEMG) and accelerometer (ACC). The usefulness of the SEMG signal is that it precedes the generated joint movement by 20 100 ms (electromechanical delay, EMD). Hence by detecting the tremor in advance, there is enough time window to do the necessary computation and to actuate the antagonist muscle by Functional Electrical Stimulation (FES). This is also possible because the time taken for FES to actuate the muscle is significantly less than that of the neural signal, as detected by SEMG. Specifically, what is proposed in this paper is algorithm to an estimate the EMD and to determine when to start/stop the FES such that anti-phase tremor cancellation. Experimental result from one Essential Tremor patient show 57% reduction in tremor power as measured by the ACC.

ICRA Conference 2008 Conference Paper

Kalman filtering of accelerometer and electromyography (EMG) data in pathological tremor sensing system

  • Ferdinan Widjaja
  • Cheng Yap Shee
  • Win Tun Latt
  • Wing Lok Au
  • Philippe Poignet
  • Wei Tech Ang

Currently there is a lack of objective clinical diagnosis and classification of tremor is difficult when it is subtle. Thus in previous work, a sensing system has been developed to quantify pathological tremor in human upper limb. In this paper, a Kalman filter algorithm to fuse information from accelerometers and surface electromyography is proposed. As the ground truth, an optical motion tracking system will be utilized. Then two sensor fusion algorithms based on Kalman filter are formulated to estimate the joint angle of the limb from the reading of accelerometers and surface EMG. Initial results using tremor data from two Parkinson's disease patients show promising future in this sensor fusion. The sensing system and the algorithms proposed are useful for actively compensating the tremor and helping the clinicians in tremor diagnostics.

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