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. Author manuscript; available in PMC: 2023 Sep 26.
Published in final edited form as: Exp Neurol. 2022 Jan 29;351:113993. doi: 10.1016/j.expneurol.2022.113993

Table 3.

Machine learning studies for decoding of pathological states from intracranial recordings.

Investigated states Recording location Recording modality Features Models Performancea
Epilepsy

  • Three focal, three extrafocal electrodes

  • ECoG

 3 features each in 8 frequency bins (4–128 Hz):

  • Absolute BP

  • Relative BP

  • BP ratio

  • Cost-sensitive linear SVM

 Sensitivity: 1.0
FPR: 0.0324 per hour
Meisel and Bailey (2019)

  • Preictal/interictal state

  • Three focal, three extrafocal electrodes

  • ECoG

  • EEG

  • EKG

  • Average PSD across all channels

  • PSD of single channels

  • EKG PSD

  • Deep learning

  • k-NN

  • SGD

  • AdaBoost

 IoC-F1 score: ≈ 0.7
Essential Tremor
Tan et al. (2019)

  • Postural tremor

  • Upper limb movements

  • Thalamus, VIM

  • LFP

  • BP in 8 frequency bands (1–195 Hz)

  • Logistic regression

 AUC: Movement: 0.74–0.99
Tremor: 0.79–0.88
Parkinson’s Disease
Gilron et al. (2021)

  • Dyskinesia

    Mobile and immobile states labelled by Parkinson’s KinetiGraph watch

  • STN

  • Primary motor cortex

  • LFP

  • ECoG

  • STN: BP of peak frequency in beta band

  • Cortex: BP of peak frequency in gamma band

  • LFP-ECoG coherence

  • LDA

  • Supervised clustering

  • Unsupervised (density-based) clustering

 ROC-AUC: 0.81–1.0
Clustering concordance: 74%
Swann et al. (2016)

  • Dyskinesia

  • STN

  • Primary motor cortex

  • LFP

  • ECoG

  • BP in 2 Hz-bins (2–50 Hz)

  • (Phase-) coherence

  • Logistic regression

 ROC-AUC: 0.8–0.94
Hirschmann et al. (2017)

  • Resting tremor

  • STN

  • LFP

  • BP at tremor frequency

  • Beta-BP

  • Low gamma-BP

  • HFO BP ratio

  • Hidden Markov models

 Accuracy: 0.84
ROC-AUC: 0.82
Yao et al. (2020c)

  • Resting tremor

  • STN

  • LFP

  • BP

  • Hjorth parameters

  • Entropy

  • PAC

  • Tremor power

  • Peak tremor power

  • XGBoost

  • LDA

  • Logistic Regression

  • SVM (RBF and linear)

  • MLP-NN

  • Random forest

 F1 score: 0.88 (XGBoost)
Camara et al. (2015)

  • Resting tremor

  • Tremor subtypes

  • Across-patient classification

  • STN

  • LFP

  • 5 features each in 6 frequency bins (3–30 Hz): energy, average, variance, first derivative, entropy

  • K-means clustering

  • MLP-NN

 Accuracy: 0.64–1.00 (0.90 mean)
Tourette Syndrome
Shute et al. (2016)

  • Tics

  • Upper limb movements

  • Primary motor cortex

  • Thalamus, CM-PF

  • ECoG

  • LFP

  • BP of three 10 Hz-bins (1–100 Hz)

  • SVM

 Recall: 0.39–0.89
Precision: 0.37–0.96

AUC – Area under the curve; CM-PF - centromedian-parafascicular complex; ECoG – Electrocorticography; HFO – High-frequency oscillation; IoC – Improvement over chance; k-NN - k-nearest neighbors; LDA – Linear discriminant analysis; LFP – Local field potential; MLP-NN – Multi-layer perceptron neural network; PAC – Phase-amplitude coupling; PSD – Power spectral density; RBF – radial basis function; ROC – Receiver operating characteristic; SGD – Stochastic gradient descent; STN – subthalamic nucleus; SVM – Support vector machine; XGBoost – Extreme gradient boosted decision tree.

a

Classification performance is not strictly comparable between studies, as design of the performance metrics was highly variable. If more than one algorithm was compared, only the score of best-performing algorithm is reported in this table.