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Abstract
BackgroundIt is widely accepted by the scientific community that bioelectrical signals, which can be used for the identification of neurophysiological biomarkers indicative of a diseased or pathological state, could direct patient treatment towards more effective therapeutic strategies. However, the design and realisation of an instrument that can precisely record weak bioelectrical signals in the presence of strong interference stemming from a noisy clinical environment is one of the most difficult challenges associated with the strategy of monitoring bioelectrical signals for diagnostic purposes. Moreover, since patients often have to cope with the problem of limited mobility being connected to bulky and mains-powered instruments, there is a growing demand for small-sized, high-performance and ambulatory biopotential acquisition systems in the Intensive Care Unit (ICU) and in High-dependency wards. Finally, to the best of our knowledge, there are no commercial, small, battery-powered, wearable and wireless recording-only instruments that claim the capability of recording electrocorticographic (ECoG) signals.MethodsTo address this problem, we designed and developed a low-noise (8 nV/√Hz), eight-channel, battery-powered, wearable and wireless instrument (55 × 80 mm2). The performance of the realised instrument was assessed by conducting both ex vivo and in vivo experiments.ResultsTo provide ex vivo proof-of-function, a wide variety of high-quality bioelectrical signal recordings are reported, including electroencephalographic (EEG), electromyographic (EMG), electrocardiographic (ECG), acceleration signals, and muscle fasciculations. Low-noise in vivo recordings of weak local field potentials (LFPs), which were wirelessly acquired in real time using segmented deep brain stimulation (DBS) electrodes implanted in the thalamus of a non-human primate, are also presented.ConclusionsThe combination of desirable features and capabilities of this instrument, namely its small size (~one business card), its enhanced recording capabilities, its increased processing capabilities, its manufacturability (since it was designed using discrete off-the-shelf components), the wide bandwidth it offers (0.5–500 Hz) and the plurality of bioelectrical signals it can precisely record, render it a versatile and reliable tool to be utilized in a wide range of applications and environments.
Highlights
It is widely accepted by the scientific community that bioelectrical signals, which can be used for the identification of neurophysiological biomarkers indicative of a diseased or pathological state, could direct patient treatment towards more effective therapeutic strategies
In vivo recordings of weak local field potentials (LFPs), which were wirelessly acquired in real time using deep brain stimulation (DBS) electrodes implanted in the thalamus of a non-human primate, are presented
The results shown were derived from signal recordings that took place in a university laboratory, which is vulnerable to noise/Electromagnetic interference (EMI) originating from a wide variety of devices that are in operation
Summary
It is widely accepted by the scientific community that bioelectrical signals, which can be used for the identification of neurophysiological biomarkers indicative of a diseased or pathological state, could direct patient treatment towards more effective therapeutic strategies. Neuroelectrical activity in the brain generates oscillatory bioelectrical signals, occurring in multiple frequency bands, such as alpha (8–12 Hz), beta (13–30 Hz) and gamma (40– 80 Hz) [1] These oscillations result from the coordination or synchronization of neural activity and have been linked to a wide range of cognitive and perceptual processes [2]. They may reflect abnormal function and present as key biomarkers of many serious neurological disorders, such as Parkinson’s disease (PD), epilepsy, traumatic brain injury, schizophrenia and autism [3]. Significant biomarkers that reveal the onset of severe diseases can be extracted from bioelectrical signals (Table 1)
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