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Publications (10 of 119) Show all publications
Olsson, F. & Medvedev, A. (2020). Nonparametric time-domain tremor quantification with smart phone for therapy individualization. IEEE Transactions on Control Systems Technology, 28(1), 118-129
Open this publication in new window or tab >>Nonparametric time-domain tremor quantification with smart phone for therapy individualization
2020 (English)In: IEEE Transactions on Control Systems Technology, ISSN 1063-6536, E-ISSN 1558-0865, Vol. 28, no 1, p. 118-129Article in journal (Refereed) Published
Abstract [en]

This paper deals with a low-complexity time-domain tremor quantification method for individualization of therapies in medical conditions, where tremor is a cardinal symptom. This method produces a tremor severity estimate based on the measurements acquired from a standard sensor platform of a smart phone during a smooth voluntary movement. The estimate is calculated from a data set recorded over tens of seconds but can also be used for unobtrusive tremor monitoring over long time. Besides tremor amplitude, its frequency is also evaluated over time, thus providing the means to distinguish between, e.g., rest and action tremor. No analytical model is assumed for the tremor signal form. The characterization of tremor severity is performed by the steady-state analysis of a Markov chain, whose states correspond to different intervals of tremor amplitude. The utility of the proposed quantification approach is illustrated by clinical data obtained during Deep Brain Stimulation programming sessions. The results are compared with a conventional approach utilizing spectral analysis to demonstrate the benefits of the proposed method.

Keywords
Satellite broadcasting, Smart phones, Medical treatment, Accelerometers, Time-domain analysis, Spectral analysis, Gyroscopes, Biomedical systems, essential tremor (ET), inertial sensors, Markov models, nonparametric modeling, Parkinson's disease (PD), therapy individualization
National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-398281 (URN)10.1109/TCST.2018.2881420 (DOI)000505786600009 ()
Funder
Vinnova
Available from: 2018-12-07 Created: 2019-12-04 Last updated: 2020-05-04Bibliographically approved
Cubo, R. & Medvedev, A. (2020). Online tissue conductivity estimation in Deep Brain Stimulation. IEEE Transactions on Control Systems Technology, 28(1), 149-162
Open this publication in new window or tab >>Online tissue conductivity estimation in Deep Brain Stimulation
2020 (English)In: IEEE Transactions on Control Systems Technology, ISSN 1063-6536, E-ISSN 1558-0865, Vol. 28, no 1, p. 149-162Article in journal (Refereed) Published
National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-347346 (URN)10.1109/TCST.2018.2862397 (DOI)000505786600012 ()
Available from: 2018-08-16 Created: 2018-03-29 Last updated: 2020-01-29Bibliographically approved
Proskurnikov, A. V. & Medvedev, A. (2019). A simple positive state observer for multidimensional Goodwin's oscillator. In: Proc. 17th European Control Conference: . Paper presented at ECC 2019, June 25–28, Naples, Italy (pp. 1671-1676). IEEE
Open this publication in new window or tab >>A simple positive state observer for multidimensional Goodwin's oscillator
2019 (English)In: Proc. 17th European Control Conference, IEEE, 2019, p. 1671-1676Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE, 2019
National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-396475 (URN)10.23919/ECC.2019.8795718 (DOI)000490488301113 ()978-3-907144-00-8 (ISBN)
Conference
ECC 2019, June 25–28, Naples, Italy
Available from: 2019-08-15 Created: 2019-11-14 Last updated: 2019-11-14Bibliographically approved
Yamalova, D. & Medvedev, A. (2019). Attractivity of the synchronous mode in hybrid observers for the impulsive Goodwin's oscillator subject to harmonic exogenous excitation. In: Proc. American Control Conference: ACC 2019. Paper presented at ACC 2019, July 10–12, Philadelphia, PA (pp. 2334-2339). American Automatic Control Council
Open this publication in new window or tab >>Attractivity of the synchronous mode in hybrid observers for the impulsive Goodwin's oscillator subject to harmonic exogenous excitation
2019 (English)In: Proc. American Control Conference: ACC 2019, American Automatic Control Council , 2019, p. 2334-2339Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
American Automatic Control Council, 2019
National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-382413 (URN)978-1-5386-7926-5 (ISBN)
Conference
ACC 2019, July 10–12, Philadelphia, PA
Available from: 2019-08-29 Created: 2019-04-25 Last updated: 2019-09-30Bibliographically approved
Yamalova, D., Medvedev, A. & Zhusubaliyev, Z. (2019). Bifurcation analysis for non-local design of a hybrid observer for the impulsive Goodwin's oscillator.
Open this publication in new window or tab >>Bifurcation analysis for non-local design of a hybrid observer for the impulsive Goodwin's oscillator
2019 (English)In: Article in journal (Other academic) Submitted
National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-382411 (URN)
Available from: 2019-04-25 Created: 2019-04-25 Last updated: 2019-05-07Bibliographically approved
Cubo, R., Fahlström, M., Jiltsova, E., Andersson, H. & Medvedev, A. (2019). Calculating Deep Brain Stimulation Amplitudes and Power Consumption by Constrained Optimization. Journal of Neural Engineering, 16(1), Article ID 016020.
Open this publication in new window or tab >>Calculating Deep Brain Stimulation Amplitudes and Power Consumption by Constrained Optimization
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2019 (English)In: Journal of Neural Engineering, ISSN 1741-2560, E-ISSN 1741-2552, Vol. 16, no 1, article id 016020Article in journal (Refereed) Published
Abstract [en]

Objective: Deep brain stimulation (DBS) consists of delivering electrical stimuli to a brain target via an implanted lead to treat neurological and psychiatric conditions. Individualized stimulation is vital to ensure therapeutic results, since DBS may otherwise become ineffective or cause undesirable side effects. Since the DBS pulse generator is battery-driven, power consumption incurred by the stimulation is important. In this study, target coverage and power consumption are compared over a patient population for clinical and model-based patient-specific settings calculated by constrained optimization.

Approach: Brain models for five patients undergoing bilateral DBS were built. Mathematical optimization of activated tissue volume was utilized to calculate stimuli amplitudes, with and without specifying the volumes, where stimulation was not allowed to avoid side effects. Power consumption was estimated using measured impedance values and battery life under both clinical and optimized settings.

Results: It was observed that clinical settings were generally less aggressive than the ones suggested by unconstrained model-based optimization, especially under asymmetrical stimulation. The DBS settings satisfying the constraints were close to the clinical values.

Significance: The use of mathematical models to suggest optimal patient-specific DBS settings that observe technological and safety constraints can save time in clinical practice. It appears though that the considered safety constraints based on brain anatomy depend on the patient and further research into it is needed. This work highlights the need of specifying the brain volumes to be avoided by stimulation while optimizing the DBS amplitude, in contrast to minimizing general stimuli overspill, and applies the technique to a cohort of patients. It also stresses the importance of considering power consumption in DBS optimization, since it increases with the square of the stimuli amplitude and also critically affects battery life through pulse frequency and duty cycle.

Keywords
neuromodulation, deep brain stimulation, inverse problems
National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-368330 (URN)10.1088/1741-2552/aaeeb7 (DOI)000455843600002 ()30524006 (PubMedID)
Available from: 2018-12-04 Created: 2018-12-04 Last updated: 2019-02-05Bibliographically approved
Medvedev, A., Cubo, R., Olsson, F., Bro, V. & Andersson, H. (2019). Control-Engineering Perspective on Deep Brain Stimulation: Revisited. In: : . Paper presented at 2019 American Control Conference (ACC,10-12 July, 2019, Philedelphia, PA, USA.
Open this publication in new window or tab >>Control-Engineering Perspective on Deep Brain Stimulation: Revisited
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2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Deep brain stimulation (DBS) is a an established therapy in neurological and mental disorders making use of electrical pulses chronically delivered to a certain disease-specific neural target through surgically implanted electrodes. The therapeutical effect of DBS is highly individual and depends on the target coverage by the stimuli and the amount of spill beyond it. This can be suitably formulated as an optimization problem. Since the biological mechanism underlying the DBS therapy is mainly unknown, and due to high inter-patient and intra-patient variability of the DBS effect, a pragmatic approach to the DBS programming is to consider the process as tuning of a control system for the symptoms. Such a technology assumes that the symptoms are accurately quantified. The paper summarizes the progress in the individualized DBS and presents the results of a limited clinical study making use of the proposed DBS programming approach.

National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-397991 (URN)10.23919/ACC.2019.8814839 (DOI)
Conference
2019 American Control Conference (ACC,10-12 July, 2019, Philedelphia, PA, USA
Available from: 2019-11-29 Created: 2019-11-29 Last updated: 2020-04-27Bibliographically approved
Johansson, D., Thomas, I., Ericsson, A., Johansson, A., Medvedev, A., Memedi, M., . . . Bergquist, F. (2019). Evaluation of a sensor algorithm for motor state rating in Parkinson's disease. Parkinsonism & Related Disorders, 64, 112-117
Open this publication in new window or tab >>Evaluation of a sensor algorithm for motor state rating in Parkinson's disease
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2019 (English)In: Parkinsonism & Related Disorders, ISSN 1353-8020, E-ISSN 1873-5126, Vol. 64, p. 112-117Article in journal (Refereed) Published
Abstract [en]

Introduction: A treatment response objective index (TRIS) was previously developed based on sensor data from pronation-supination tests. This study aimed to examine the performance of TRIS for medication effects in a new population sample with Parkinson's disease (PD) and its usefulness for constructing individual dose-response models.

Methods: Twenty-five patients with PD performed a series of tasks throughout a levodopa challenge while wearing sensors. TRIS was used to determine motor changes in pronation-supination tests following a single levodopa dose, and was compared to clinical ratings including the Treatment Response Scale (TRS) and six sub-items of the UPDRS part III.

Results: As expected, correlations between TRIS and clinical ratings were lower in the new population than in the initial study. TRIS was still significantly correlated to TRS (r(s) = 0.23, P < 0.001) with a root mean square error (RMSE) of 1.33. For the patients (n = 17) with a good levodopa response and clear motor fluctuations, a stronger correlation was found (r(s) = 0.38, RMSE = 1.29, P < 0.001). The mean TRIS increased significantly when patients went from the practically defined off to their best on state (P = 0.024). Individual dose-response models could be fitted for more participants when TRIS was used for modelling than when TRS ratings were used.

Conclusion: The objective sensor index shows promise for constructing individual dose-response models, but further evaluations and retraining of the TRIS algorithm are desirable to improve its performance and to ensure its clinical effectiveness.

Place, publisher, year, edition, pages
ELSEVIER SCI LTD, 2019
Keywords
Levodopa challenge test, Independent evaluation, Wearable sensors, Parkinson's disease, Machine learning algorithms
National Category
Neurology Other Medical Engineering
Identifiers
urn:nbn:se:uu:diva-395925 (URN)10.1016/j.parkreldis.2019.03.022 (DOI)000487567800016 ()30935826 (PubMedID)
Funder
Swedish Foundation for Strategic Research , SBE 13-0086Vinnova, 2014-03727
Available from: 2019-10-30 Created: 2019-10-30 Last updated: 2019-10-30Bibliographically approved
Yamalova, D. & Medvedev, A. (2019). Hybrid observer with finite-memory output error correction for linear systems under intrinsic impulsive feedback. Automatica
Open this publication in new window or tab >>Hybrid observer with finite-memory output error correction for linear systems under intrinsic impulsive feedback
2019 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836Article in journal (Other academic) Submitted
National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-382412 (URN)
Available from: 2019-04-25 Created: 2019-04-25 Last updated: 2019-05-07Bibliographically approved
Bro, V. & Medvedev, A. (2019). Identification of continuous Volterra models with explicit time delay through series of Laguerre functions. In: Proc. 58th Conference on Decision and Control: . Paper presented at CDC 2019, December 11–13, Nice, France (pp. 5641-5646). IEEE
Open this publication in new window or tab >>Identification of continuous Volterra models with explicit time delay through series of Laguerre functions
2019 (English)In: Proc. 58th Conference on Decision and Control, IEEE, 2019, p. 5641-5646Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE, 2019
National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-385943 (URN)10.1109/CDC40024.2019.9029789 (DOI)978-1-7281-1398-2 (ISBN)
Conference
CDC 2019, December 11–13, Nice, France
Available from: 2020-03-12 Created: 2019-06-18 Last updated: 2020-03-24Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-6608-250x

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