Adaptation of Convolutional Neural Networks for Multi-Channel Artifact Detection in Chronically Recorded Local Field Potentials

Fabietti, Marcos; Mahmud, Mufti; Lotfi, Ahmad; Averna, Alberto; Guggenmos, David J.; Nudo, Randolph J.; Chiappalone, Michela

doi:10.1109/ssci47803.2020.9308165

Cited by 26 publications

(8 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several EEG-based classification algorithms have been employed to aid the diagnosis among different neurological conditions by using state-of-the-art ML algorithms (i.e., LDA, SVM, ANNs) [ 9 , 10 , 11 , 12 , 13 , 14 ]. In particular, ML approaches for EEG analysis to early diagnose ES have attracted a lot of interest from the scientific community in recent years [ 12 , 15 , 16 ].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Permutation Entropy-Based Interpretability of Convolutional Neural Network Models for Interictal EEG Discrimination of Subjects with Epileptic Seizures vs. Psychogenic Non-Epileptic Seizures

Giudice

Varone

Ieracitano

et al. 2022

Entropy

View full text Add to dashboard Cite

The differential diagnosis of epileptic seizures (ES) and psychogenic non-epileptic seizures (PNES) may be difficult, due to the lack of distinctive clinical features. The interictal electroencephalographic (EEG) signal may also be normal in patients with ES. Innovative diagnostic tools that exploit non-linear EEG analysis and deep learning (DL) could provide important support to physicians for clinical diagnosis. In this work, 18 patients with new-onset ES (12 males, 6 females) and 18 patients with video-recorded PNES (2 males, 16 females) with normal interictal EEG at visual inspection were enrolled. None of them was taking psychotropic drugs. A convolutional neural network (CNN) scheme using DL classification was designed to classify the two categories of subjects (ES vs. PNES). The proposed architecture performs an EEG time-frequency transformation and a classification step with a CNN. The CNN was able to classify the EEG recordings of subjects with ES vs. subjects with PNES with 94.4% accuracy. CNN provided high performance in the assigned binary classification when compared to standard learning algorithms (multi-layer perceptron, support vector machine, linear discriminant analysis and quadratic discriminant analysis). In order to interpret how the CNN achieved this performance, information theoretical analysis was carried out. Specifically, the permutation entropy (PE) of the feature maps was evaluated and compared in the two classes. The achieved results, although preliminary, encourage the use of these innovative techniques to support neurologists in early diagnoses.

show abstract

Section: Related Workmentioning

confidence: 99%

“…On the contrary, the association of some features with a specific class of subjects could be a source of clear class determination for an artificial intelligence (AI) system. In fact, the literature [ 9 , 10 , 11 ] yields examples of EEG data processing with machine/deep learning (DL) with excellent results.…”

Section: Introductionmentioning

confidence: 99%

Permutation Entropy-Based Interpretability of Convolutional Neural Network Models for Interictal EEG Discrimination of Subjects with Epileptic Seizures vs. Psychogenic Non-Epileptic Seizures

Giudice

Varone

Ieracitano

et al. 2022

Entropy

View full text Add to dashboard Cite

show abstract

“…The third approach [73] consisted of using CNNs, where three popular architectures were adapted for the one-dimensional signal. The best performance was achieved by the Alexnet [74] inspired model, with an accuracy of 95.1%. In addition, grad cam maps were extracted to understand which portions of the signal the model used for assigning each class.…”

Section: Local Field Potentialsmentioning

confidence: 99%

ABOT: An Open-Source Online Benchmarking Tool for Machine Learning-based Artefact Detection and Removal Methods from Neuronal Signals

Fabietti

Mahmud

Lotfi

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Brain signals are recorded using different techniques to aid an accurate understanding of brain function and to treat its disorders. Untargeted internal and external sources contaminate the acquired signals during the recording process. Often termed as artefacts, these contaminations cause serious hindrances in decoding the recorded signals; hence, they must be removed to facilitate unbiased decision making for a given investigation. Due to the complex and elusive manifestation of artefacts in neuronal signals, computational techniques serve as powerful tools for their detection and removal. Machine learning (ML) based methods have been successfully applied in this task. Due to ML's popularity, many articles are published every year, making it challenging to find, compare and select the most appropriate method for a given experiment. To this end, this paper presents ABOT (Artefact removal Benchmarking Online Tool) as an online benchmarking tool which allows users to compare existing ML-driven artefact detection and removal methods from the literature. The characteristics and related information about the existing methods have been compiled as a knowledgebase (KB) and presented through a user-friendly interface with interactive plots and tables for users to search it using several criteria. Key characteristics extracted from over 120 articles from the literature have been used in the KB to help compare the specific ML models. To comply with the FAIR (Findable, Accessible, Interoperable and Reusable) principle, the source code and documentation of the toolbox have been made available via an open-access repository.

show abstract

“…Artificial intelligence has been used for analysis of patterns and classification in diverse fields such as, anomaly detection [29,[36][37][38][39][40][41][42][43][44], biological data mining [45,46], disease detection [47][48][49][50][51][52][53][54][55][56][57][58], monitoring of human [59][60][61][62], financial forecasting [63], image analysis [64,65], and natural language processing [66][67][68]. Most of the time, these algorithms are composed of multiple layers of neurons for processing of non-linear information and were inspired by how the human brain works.…”

Section: Artifact Detectionmentioning

confidence: 99%

“…In our earlier publications [37], an MLP is employed to identify artifacts in LFP along with two other architectures: long short-term memory (LSTM) networks and one dimensional convolutional neural network (1D-CNN) [71,72]. The diagrams of the main components of these architectures are depicted in Fig.…”

Section: Artifact Detectionmentioning

confidence: 99%

SANTIA: a Matlab-based open-source toolbox for artifact detection and removal from extracellular neuronal signals

et al. 2021

Self Cite

View full text Add to dashboard Cite

Neuronal signals generally represent activation of the neuronal networks and give insights into brain functionalities. They are considered as fingerprints of actions and their processing across different structures of the brain. These recordings generate a large volume of data that are susceptible to noise and artifacts. Therefore, the review of these data to ensure high quality by automatically detecting and removing the artifacts is imperative. Toward this aim, this work proposes a custom-developed automatic artifact removal toolbox named, SANTIA (SigMate Advanced: a Novel Tool for Identification of Artifacts in Neuronal Signals). Developed in Matlab, SANTIA is an open-source toolbox that applies neural network-based machine learning techniques to label and train models to detect artifacts from the invasive neuronal signals known as local field potentials.

show abstract

Adaptation of Convolutional Neural Networks for Multi-Channel Artifact Detection in Chronically Recorded Local Field Potentials

Cited by 26 publications

References 30 publications

Permutation Entropy-Based Interpretability of Convolutional Neural Network Models for Interictal EEG Discrimination of Subjects with Epileptic Seizures vs. Psychogenic Non-Epileptic Seizures

Permutation Entropy-Based Interpretability of Convolutional Neural Network Models for Interictal EEG Discrimination of Subjects with Epileptic Seizures vs. Psychogenic Non-Epileptic Seizures

ABOT: An Open-Source Online Benchmarking Tool for Machine Learning-based Artefact Detection and Removal Methods from Neuronal Signals

SANTIA: a Matlab-based open-source toolbox for artifact detection and removal from extracellular neuronal signals

Contact Info

Product

Resources

About