Improved detection of Parkinsonian resting tremor with feature engineering and Kalman filtering
Open accessAbstract
OBJECTIVE: Accurate and reliable detection of tremor onset in Parkinson's disease (PD) is critical to the success of adaptive deep brain stimulation (aDBS) therapy. Here, we investigated the potential use of feature engineering and machine learning methods for more accurate detection of rest tremor in PD. METHODS: We analyzed the local field potential (LFP) recordings from the subthalamic nucleus region in 12 patients with PD (16 recordings). To explore the optimal biomarkers and the best performing classifier, the performance of state-of-the-art machine learning (ML) algorithms and various features of the subthalamic LFPs were compared. We further used a Kalman filtering technique in feature domain to reduce the false positive rate. RESULTS: The Hjorth complexity showed a higher correlation with tremor, compared to other features in our study. In addition, by optimal selection of a maximum of five features with a sequential feature selection method and using the gradient boosted decision trees as the classifier, the system could achieve an average F1 score of up to 88.7% and a detection lead of 0.52 s. The use of Kalman filtering in feature space significantly improved the specificity by 17.0% (p = 0.002), thereby potentially reducing the unnecessary power dissipation of the conventional DBS system. CONCLUSION: The use of relevant features combined with Kalman filtering and machine learning improves the accuracy of tremor detection during rest. SIGNIFICANCE: The proposed method offers a potential solution for efficient on-demand stimulation for PD tremor.
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Research on using the KF and its derivative algorithms has shown improved accuracy in tremor estimation compared to gradient descent methods.
“Furthermore, research on using the KF and its derivative algorithms [21], [31], [32] has shown improved accuracy in tremor estimation compared to gradient descent methods.”
From Real-Time Performance Assessment of High-Order Tremor Estimators Used in a Wearable Tremor Suppression Device (2022) · cites this paper as [32]
The Discussion (Features, classifiers, and Kalman filter; page unavailable) says Kalman filtering improved tremor detection, and the Results report a 17.0% gain in specificity. The paper does not report a comparison with gradient descent methods or establish improved tremor-estimation accuracy relative to them. (LLM, conf=0.87)
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Cites
EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces
Lawhern, V. J., Solon, A. J., Waytowich, N. R., Gordon, S. M., Hung, C. P., & Lance, B. J. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aace8c · Cited by 5,115 · cited 1× · see citing works
“We used a compact CNN previously employed for EEG classification (Lawhern et al., 2018).”
On-Demand Control System for Deep Brain Stimulation for Treatment of Intention Tremor
Yamamoto, T., Katayama, Y., Ushiba, J., Yoshino, H., Obuchi, T., Kobayashi, K., Oshima, H., & Fukaya, C. (2013) · Neuromodulation: Technology at the Neural Interface · doi:10.1111/j.1525-1403.2012.00521.x · Cited by 90 · see citing works
Parkinsonian tremor identification with multiple local field potential feature classification
Bakstein, E., Burgess, J., Warwick, K., Ruiz, V., Aziz, T., & Stein, J. (2012) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2012.06.027 · Cited by 34 · see citing works
Flagged first, then by citations
Research works printed but not linked: 42
- Arlotti M Marceglia S Foffani G Volkmann J Lozano AM Moro E Eight-hours adaptive deep brain stimulation in patients with Parkinson disease Neurology 2018 90 e971 6 29444973 PMC5858949
- Arlotti M Rosa M Marceglia S Barbieri S Priori A The adaptive deep brain stimulation challenge Park Relat Disord 2016a 28 12 7 27079257
- Arlotti M Rossi L Rosa M Marceglia S Priori A An external portable device for adaptive deep brain stimulation (aDBS) clinical research in advanced Parkinson’s Disease Med Eng Phys 2016b 38 498 505 27029510
- Beudel M Brown P Adaptive deep brain stimulation in Parkinson’s disease Parkinsonism Relat Disord 2016 22 S123 6 26411502 PMC4671979
- Brown P Oscillatory nature of human basal ganglia activity: relationship to the pathophysiology of Parkinson’s disease Mov Disord 2003 18 357 63 12671940
- Burgess JG Warwick K Ruiz V Gasson MN Aziz TZ Brittain JS Identifying tremor-related characteristics of basal ganglia nuclei during movement in the Parkinsonian patient Park Relat Disord 2010 16 671 5 20884273
- Cagnan H Pedrosa D Little S Pogosyan A Cheeran B Zrinzo L Stimulating at the right time: phase-specific deep brain stimulation Stimulating at the right time: phase-specific deep brain stimulation Brain 2016 140 132 45 28007997 PMC5226063
- Chisci L Mavino A Perferi G Sciandrone M Anile C Colicchio G Real-time epileptic seizure prediction using AR models and support vector machines IEEE Trans Biomed Eng 2010 57 1124 32 20172805
- Foffani G Priori A Egidi M Rampini P Tamma F Caputo E 300-Hz subthalamic oscillations in Parkinson’s disease Brain 2003 126 2153 63 12937087
- Foltynie T Hariz MI Surgical management of Parkinson’s disease Expert Rev Neurother 2010 10 6 903 14 20518607
- Glaser JI Chowdhury RH Perich MG Miller LE Kording KP Machine learning for neural decoding arXiv Prepr arXiv1708.00909 2017 PMC7470933 32737181
- Hirschmann J Butz M Hartmann CJ Hoogenboom N Özkurt TE Vesper J Parkinsonian rest tremor is associated with modulations of subthalamic high-frequency oscillations Mov Disord 2016 31 1551 9 27214766
- Hirschmann J Hartmann CJ Butz M Hoogenboom N Özkurt TE Elben S A direct relationship between oscillatory subthalamic nucleus-cortex coupling and rest tremor in Parkinson’s disease Brain 2013 136 3659 70 24154618
- Hirschmann J Schoffelen JM Schnitzler A van Gerven MAJ Parkinsonian rest tremor can be detected accurately based on neuronal oscillations recorded from the subthalamic nucleus Clin Neurophysiol 2017 128 2029 36 28841506
- Hjorth B EEG analysis based on time domain properties Electroencephalogr Clin Neurophysiol 1970 29 306 10 4195653
- Houston B Blumenfeld Z Quinn E Bronte-Stewart H Chizeck H Long-term detection of Parkinsonian tremor activity from subthalamic nucleus local field potentials Engineering in Medicine and Biology Society (EMBC) 37th Annual International Conference of the IEEE 2015 3427 31 26737029
- Jain A Zongker D Feature selection: Evaluation, application, and small sample performance IEEE Trans Pattern Anal Mach Intell 1997 19 153 8
- Little S Beudel M Zrinzo L Foltynie T Limousin P Hariz M Bilateral adaptive deep brain stimulation is effective in Parkinson’s disease J Neurol Neurosurg Psychiatry 2016a 87 717 21 26424898 PMC4941128
- Little S Pogosyan A Neal S Zavala B Zrinzo L Hariz M Adaptive deep brain stimulation in advanced Parkinson disease Ann Neurol 2013 74 449 57 23852650 PMC3886292
- Little S Tripoliti E Beudel M Pogosyan A Cagnan H Herz D Adaptive deep brain stimulation for Parkinson’s disease demonstrates reduced speech side effects compared to conventional stimulation in the acute setting J Neurol Neurosurg Psychiatry 2016b 87 1388 9 27530809 PMC5136720
- López-Azcárate J Tainta M Rodriguez-Oroz MC Valencia M González R Guridi J Coupling between beta and high-frequency activity in the human subthalamic nucleus may be a pathophysiological mechanism in Parkinson’s disease J Neurosci 2010 30 6667 77 20463229 PMC6632566
- Malekmohammadi M Herron J Velisar A Blumenfeld Z Trager MH Chizeck HJ Kinematic A daptive D eep B rain S timulation for R esting T remor in P arkinson’s D isease Mov Disord 2016 31 426 8 26813875
- Meidahl AC Tinkhauser G Herz DM Cagnan H Debarros J Brown P Adaptive deep brain stimulation for movement disorders: the long road to clinical therapy Mov Disord 2017 32 810 9 28597557 PMC5482397
- Priori A Foffani G Pesenti A Tamma F Bianchi AM Pellegrini M Rhythm-specific pharmacological modulation of subthalamic activity in Parkinson’s disease Exp Neurol 2004 189 369 79 15380487
- Priori A Foffani G Rossi L Marceglia S Adaptive deep brain stimulation (aDBS) controlled by local field potential oscillations Exp Neurol 2013 245 77 86 23022916
- Qasim SE de Hemptinne C Swann NC Miocinovic S Ostrem JL Starr PA Electrocorticography reveals beta desynchronization in the basal ganglia-cortical loop during rest tremor in Parkinson’s disease Neurobiol Dis 2016 86 177 86 26639855 PMC4842026
- Ray S Crone NE Niebur E Franaszczuk PJ Hsiao SS Neural correlates of high-gamma oscillations (60-200 Hz) in macaque local field potentials and their potential implications in electrocorticography J Neurosci 2008 28 11526 36 18987189 PMC2715840
- Rosa M Arlotti M Ardolino G Cogiamanian F Marceglia S Di Fonzo A Adaptive deep brain stimulation in a freely moving parkinsonian patient Mov Disord 2015 30 1003 5 25999288 PMC5032989
- Rosso OA Blanco S Yordanova J Kolev V Figliola A Schürmann M Wavelet entropy: a new tool for analysis of short duration brain electrical signals J Neurosci Methods 2001 105 65 75 11166367
- Santaniello S Fiengo G Glielmo L Grill WM Closed-loop control of deep brain stimulation: A simulation study IEEE Trans Neural Syst Rehabil Eng 2011 19 15 24 20889437
- Shah SA Tinkhauser G Chen CC Little S Brown P Parkinsonian tremor detection from subthalamic nucleus local field potentials for closed-loop deep brain stimulation 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 2018 2320 4 PMC6277049 30440871
- Shoaran M Haghi BA Taghavi M Farivar M Emami A Energy-efficient classification for resource-constrained biomedical applications IEEE J Emerg Sel Top Circ Syst 2018 693 707
- Shoaran M Shahshahani M Farivar M Almajano J Shahshahani A Schmid A A 16-channel 1.1mm2 implantable seizure control SoC with sub-lW/channel consumption and closed-loop stimulation in 0.18lm CMOS IEEE Symposium on VLSI Circuits, Digest of Technical Papers 2016
- Swann NCC de Hemptinne C Thompson MCC Miocinovic S Miller AMM Ostrem JLL Adaptive deep brain stimulation for Parkinson’s disease using motor cortex sensing J Neural Eng 2018 15 46006 PMC6021210 29741160
- Trottenberg T Fogelson N Kühn AA Kivi A Kupsch A Schneider G-H Subthalamic gamma activity in patients with Parkinson’s disease Exp Neurol 2006 200 56 65 16499911
- van Wijk BCM Beudel M Jha A Oswal A Foltynie T Hariz MI Subthalamic nucleus phase-amplitude coupling correlates with motor impairment in Parkinson’s disease Clin Neurophysiol 2016 127 2010 9 26971483 PMC4803022
- Velisar A Syrkin-Nikolau J Blumenfeld Z Trager MH Afzal MF Prabhakar V Dual threshold neural closed loop deep brain stimulation in Parkinson disease patients Brain Stimul 2019 10.1016/j.brs.2019.02.020 30833216 · DOI
- Weinberger M Hutchison WD Lozano AM Hodaie M Dostrovsky JO Increased gamma oscillatory activity in the subthalamic nucleus during tremor in Parkinson’s disease patients J Neurophysiol 2009 101 789 802 19004998
- Yao L Brown P Shoaran M Resting tremor detection in parkinson’s disease with machine learning and kalman filtering IEEE Biomedical Circuits and Systems Conference (BioCAS) 1 4 10.1109/BIOCAS.2018.8584721 PMC6645988 31334499 · DOI
- Zhang Z Parhi KK Low-complexity seizure prediction from iEEG/sEEG using spectral power and ratios of spectral power IEEE Trans Biomed Circuits Syst 2016 10 693 706 26529783
- Zheng W Zhu J Lu B Zhu J Lu B Identifying stable patterns over time for emotion recognition from EEG IEEE Trans Affect Comput 2017 10.1109/TAFFC.2017.2712143 · DOI
- Zhu B Coppola G Shoaran M Migraine classification using somatosensory evoked potentials Cephalalgia 2019 10.1177/0333102419839975 30913908 · DOI
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Qiu, S., Zhao, H., Jiang, N., Wang, Z., Liu, L., An, Y., Zhao, H., Miao, X., Liu, R., & Fortino, G. (2022) · Information Fusion · doi:10.1016/j.inffus.2021.11.006 · Cited by 671 · see citing works
Zhu, B., Shin, U., & Shoaran, M. (2021) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2021.3112756 · Cited by 67 · see citing works
Self-citationZhou, Y., Habibollahi, Z., Ibrahim, A., Jenkins, M. E., Naish, M. D., & Trejos, A. L. (2022) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2022.3211450 · Cited by 8 · see citing works
- Authors
- Yao, L., Brown, P., & Shoaran, M.
- Affiliations
- Cornell University (US)
- John Radcliffe Hospital (GB)
- Corresponding author
- Yao, Lin
- Venue
- Clinical Neurophysiology
- Publisher
- Elsevier BV
- Published
- 2020-01
- Volume
- 131
- Issue
- 1
- Pages
- 274-284
- ISSN
- 1388-2457 (ISSN-L)
- PMC
- PMC6927801
- OpenAlex
- W2985410335
- Cited by
- 68
- References
- 3
- Citations by year
- 2026: 10 · 2025: 6 · 2024: 5 · 2023: 15 · 2022: 9 · 2021: 13 · 2020: 7 · 2019: 3
- FWCI
- 5.41
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Clinical Neurophysiology (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion, cc-by-nc-nd)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Funders
- Rosetrees Trust (M526-B-CD1)
- Medical Research Council (MC_UU_00003/2, MC_UU_12024/1)
- Topics
- Health Sciences › Medicine › Neurology › Neurological disorders and treatments
- Social Sciences › Psychology › Clinical Psychology › Obsessive-Compulsive Spectrum Disorders
- Life Sciences › Neuroscience › Neurology › Transcranial Magnetic Stimulation Studies
- Keywords
- Parkinson's disease, adaptive deep brain stimulation, subthalamic nucleus, local field potentials, feature selection, gradient boosting decision tree, Kalman filter
- MeSH terms
- Machine Learning, Aged, Algorithms, Analysis of Variance, Female, Humans, Male, Middle Aged, Parkinson Disease, Reaction Time, Rest, Tremor, Subthalamic Nucleus, Deep Brain Stimulation, Wavelet Analysis
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