Abstract
Acquisition of neuronal signals involves a wide range of devices with specific electrical properties. Combined with other physiological sources within the body, the signals sensed by the devices are often distorted. Sometimes these distortions are visually identifiable, other times, they overlay with the signal characteristics making them very difficult to detect. To remove these distortions, the recordings are visually inspected and manually processed. However, this manual annotation process is time-consuming and automatic computational methods are needed to identify and remove these artefacts. Most of the existing artefact removal approaches rely on additional information from other recorded channels and fail when global artefacts are present or the affected channels constitute the majority of the recording system. Addressing this issue, this paper reports a novel channel-independent machine learning model to accurately identify and replace the artefactual segments present in the signals. Discarding these artifactual segments by the existing approaches causes discontinuities in the reproduced signals which may introduce errors in subsequent analyses. To avoid this, the proposed method predicts multiple values of the artefactual region using long–short term memory network to recreate the temporal and spectral properties of the recorded signal. The method has been tested on two open-access data sets and incorporated into the open-access SANTIA (SigMate Advanced: a Novel Tool for Identification of Artefacts in Neuronal Signals) toolbox for community use.
Highlights
When recording neural signals, other electrical sources either instrumental or physiological may distort the process
Out of the 54 convolutional neural network (CNN)-Long–Short-Term Memory (LSTM) models, the best performance is achieved with an output of 20 data points across all inputs, while the worst performances are achieved with 50 or 100 output points
The performance of the CNN-LSTM is better than the LSTM models, with the best score being 0.1463 of the 200 ms input and 20 points prediction model
Summary
Other electrical sources either instrumental or physiological may distort the process. They are commonly known as artefacts, and their identification and removal are of importance to further analyse and infer insights from them. They produce longer review times [5], misdiagnosis of diseases or brain conditions (as in the diagnosis of Schizophrenia, sleep disorders and Alzheimer’s disease [32]) or produce false alarms (as in generating false alarms for brain seizures [49]).
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