Valentin Goverdovsky

Valentin Goverdovsky

Valentin Goverdovsky (M’13) received the M.Eng. degree in electronic engineering and the Ph.D. degree in communications from Imperial College London, U.K.

He is currently a Rosetrees Fellow with the Department of Electrical and Electronic Engineering, Imperial College London. His research interests are primarily in the areas of biomedical instrumentation, analog integrated circuits, and radio frequency communications. He is currently involved in the development of wearable biosensing platforms, such as the novel in-the-ear sensing concept for 24/7 monitoring of brain and body functions in the context of traumatic brain injury. He received the Eric Laithwaithe Award for excellence in research.


  • Pain Prediction from ECG in Vascular Surgery
    Pain Prediction from ECG in Vascular Surgery

    Part of the Special Issue NIH-IEEE POCT 2016
    Varicose vein surgeries are routine outpatient procedures, which are often performed under local anaesthesia. The use of local anaesthesia both minimises the risk to patients and is cost effective, however, a number of patients still experience pain during surgery. Surgical teams must therefore decide to administer either a general or local anaesthetic based on their subjective qualitative assessment of patient anxiety and sensitivity to pain, without any means to objectively validate their decision…

  • Automatic Sleep Monitoring Using Ear-EEG

    The monitoring of sleep patterns without patient’s inconvenience or involvement of a medical specialist is a clinical question of significant importance. To this end, we propose an automatic sleep stage monitoring system based on an affordable, unobtrusive, discreet, and long-term wearable in-ear sensor for recording the electroencephalogram (ear-EEG). The selected features for sleep pattern classification from a single ear-EEG channel include the spectral edge frequency and multi-scale fuzzy entropy, a structural complexity feature. In this preliminary study, the manually scored hypnograms from simultaneous scalp-EEG and ear-EEG recordings of four subjects are used as labels for two analysis scenarios.

  • Smart Helmet: Wearable Multichannel ECG & EEG
    Smart Helmet: Wearable Multichannel ECG & EEG

    Modern wearable technologies have enabled continuous recording of vital signs, however, for activities such as cycling, motor-racing, or military engagement, a helmet with embedded sensors would provide maximum convenience and the opportunity to monitor simultaneously both the vital signs and the electroencephalogram (EEG). To this end, we investigate the feasibility of recording the electrocardiogram (ECG), respiration, and EEG from face-lead locations, by embedding multiple electrodes within a standard helmet.


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