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Peach R, Friedrich M, Fronemann L, Muthuraman M, Schreglmann SR, Zeller D, Schrader C, Krauss JK, Schnitzler A, Wittstock M, Helmers AK, Paschen S, Kühn A, Skogseid IM, Eisner W, Mueller J, Matthies C, Reich M, Volkmann J, Ip CW. Head movement dynamics in dystonia: a multi-centre retrospective study using visual perceptive deep learning. NPJ Digit Med 2024; 7:160. [PMID: 38890413 PMCID: PMC11189529 DOI: 10.1038/s41746-024-01140-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 05/22/2024] [Indexed: 06/20/2024] Open
Abstract
Dystonia is a neurological movement disorder characterised by abnormal involuntary movements and postures, particularly affecting the head and neck. However, current clinical assessment methods for dystonia rely on simplified rating scales which lack the ability to capture the intricate spatiotemporal features of dystonic phenomena, hindering clinical management and limiting understanding of the underlying neurobiology. To address this, we developed a visual perceptive deep learning framework that utilizes standard clinical videos to comprehensively evaluate and quantify disease states and the impact of therapeutic interventions, specifically deep brain stimulation. This framework overcomes the limitations of traditional rating scales and offers an efficient and accurate method that is rater-independent for evaluating and monitoring dystonia patients. To evaluate the framework, we leveraged semi-standardized clinical video data collected in three retrospective, longitudinal cohort studies across seven academic centres. We extracted static head angle excursions for clinical validation and derived kinematic variables reflecting naturalistic head dynamics to predict dystonia severity, subtype, and neuromodulation effects. The framework was also applied to a fully independent cohort of generalised dystonia patients for comparison between dystonia sub-types. Computer vision-derived measurements of head angle excursions showed a strong correlation with clinically assigned scores. Across comparisons, we identified consistent kinematic features from full video assessments encoding information critical to disease severity, subtype, and effects of neural circuit interventions, independent of static head angle deviations used in scoring. Our visual perceptive machine learning framework reveals kinematic pathosignatures of dystonia, potentially augmenting clinical management, facilitating scientific translation, and informing personalized precision neurology approaches.
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Affiliation(s)
- Robert Peach
- Department of Neurology, University Hospital Würzburg, Würzburg, 97080, Germany.
- Department of Brain Sciences, Imperial College London, London, UK.
| | - Maximilian Friedrich
- Department of Neurology, University Hospital Würzburg, Würzburg, 97080, Germany
- Center for Brain Circuit Therapeutics, Brigham & Women's Hospital, Boston, USA
- Harvard Medical School, Boston, USA
| | - Lara Fronemann
- Department of Neurology, University Hospital Würzburg, Würzburg, 97080, Germany
| | | | | | - Daniel Zeller
- Department of Neurology, University Hospital Würzburg, Würzburg, 97080, Germany
| | - Christoph Schrader
- Department of Neurology and Clinical Neurophysiology, Hannover Medical School, Hannover, Germany
| | - Joachim K Krauss
- Department of Neurosurgery, Hannover Medical School, Hannover, Germany
| | - Alfons Schnitzler
- Institute of Clinical Neuroscience and Medical Psychology, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | | | - Ann-Kristin Helmers
- Department of Neurology, UKSH, Kiel Campus Christian-Albrechts-University, Kiel, Germany
| | - Steffen Paschen
- Department of Neurology, Christian-Albrechts-University, Kiel, Germany
| | - Andrea Kühn
- Department of Neurology, Movement Disorder and Neuromodulation Unit, Charité - Universitätsmedizin, Berlin, Germany
| | - Inger Marie Skogseid
- Movement Disorders Unit, Department of Neurology, Oslo University Hospital, Rikshospitalet, Oslo, Norway
| | - Wilhelm Eisner
- Department of Neurology, Innsbruck Medical University, 6020, Innsbruck, Austria
| | - Joerg Mueller
- Klinik für Neurologie mit Stroke Unit, Vivantes Klinikum Spandau, Berlin, Germany
| | - Cordula Matthies
- Department of Neurology, University Hospital Würzburg, Würzburg, 97080, Germany
| | - Martin Reich
- Department of Neurology, University Hospital Würzburg, Würzburg, 97080, Germany
| | - Jens Volkmann
- Department of Neurology, University Hospital Würzburg, Würzburg, 97080, Germany
| | - Chi Wang Ip
- Department of Neurology, University Hospital Würzburg, Würzburg, 97080, Germany.
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Bremm RP, Pavelka L, Garcia MM, Mombaerts L, Krüger R, Hertel F. Sensor-Based Quantification of MDS-UPDRS III Subitems in Parkinson's Disease Using Machine Learning. SENSORS (BASEL, SWITZERLAND) 2024; 24:2195. [PMID: 38610406 PMCID: PMC11014392 DOI: 10.3390/s24072195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 03/19/2024] [Accepted: 03/26/2024] [Indexed: 04/14/2024]
Abstract
Wearable sensors could be beneficial for the continuous quantification of upper limb motor symptoms in people with Parkinson's disease (PD). This work evaluates the use of two inertial measurement units combined with supervised machine learning models to classify and predict a subset of MDS-UPDRS III subitems in PD. We attached the two compact wearable sensors on the dorsal part of each hand of 33 people with PD and 12 controls. Each participant performed six clinical movement tasks in parallel with an assessment of the MDS-UPDRS III. Random forest (RF) models were trained on the sensor data and motor scores. An overall accuracy of 94% was achieved in classifying the movement tasks. When employed for classifying the motor scores, the averaged area under the receiver operating characteristic values ranged from 68% to 92%. Motor scores were additionally predicted using an RF regression model. In a comparative analysis, trained support vector machine models outperformed the RF models for specific tasks. Furthermore, our results surpass the literature in certain cases. The methods developed in this work serve as a base for future studies, where home-based assessments of pharmacological effects on motor function could complement regular clinical assessments.
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Affiliation(s)
- Rene Peter Bremm
- National Department of Neurosurgery, Centre Hospitalier de Luxembourg, 1210 Luxembourg, Luxembourg (F.H.)
| | - Lukas Pavelka
- Parkinson’s Research Clinic, Centre Hospitalier de Luxembourg, 1210 Luxembourg, Luxembourg; (L.P.); (R.K.)
- Translational Neuroscience, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 4365 Esch-sur-Alzette, Luxembourg
- Transversal Translational Medicine, Luxembourg Institute of Health, 1445 Strassen, Luxembourg
| | - Maria Moscardo Garcia
- Systems Control, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 4365 Esch-sur-Alzette, Luxembourg
| | - Laurent Mombaerts
- National Department of Neurosurgery, Centre Hospitalier de Luxembourg, 1210 Luxembourg, Luxembourg (F.H.)
| | - Rejko Krüger
- Parkinson’s Research Clinic, Centre Hospitalier de Luxembourg, 1210 Luxembourg, Luxembourg; (L.P.); (R.K.)
- Translational Neuroscience, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 4365 Esch-sur-Alzette, Luxembourg
- Transversal Translational Medicine, Luxembourg Institute of Health, 1445 Strassen, Luxembourg
| | - Frank Hertel
- National Department of Neurosurgery, Centre Hospitalier de Luxembourg, 1210 Luxembourg, Luxembourg (F.H.)
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Feldmann LK, Roudini J, Kühn AA, Habets JGV. Improving naturalistic neuroscience with patient engagement strategies. Front Hum Neurosci 2024; 17:1325154. [PMID: 38259336 PMCID: PMC10800538 DOI: 10.3389/fnhum.2023.1325154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 12/13/2023] [Indexed: 01/24/2024] Open
Abstract
Introduction The clinical implementation of chronic electrophysiology-driven adaptive deep brain stimulation (DBS) algorithms in movement disorders requires reliable representation of motor and non-motor symptoms in electrophysiological biomarkers, throughout normal life (naturalistic). To achieve this, there is the need for high-resolution and -quality chronic objective and subjective symptom monitoring in parallel to biomarker recordings. To realize these recordings, an active participation and engagement of the investigated patients is necessary. To date, there has been little research into patient engagement strategies for DBS patients or chronic electrophysiological recordings. Concepts and results We here present our concept and the first results of a patient engagement strategy for a chronic DBS study. After discussing the current state of literature, we present objectives, methodology and consequences of the patient engagement regarding study design, data acquisition, and study infrastructure. Nine patients with Parkinson's disease and their caregivers participated in the meeting, and their input led to changes to our study design. Especially, the patient input helped us designing study-set-up meetings and support structures. Conclusion We believe that patient engagement increases compliance and study motivation through scientific empowerment of patients. While considering patient opinion on sensors or questionnaire questions may lead to more precise and reliable data acquisition, there was also a high demand for study support and engagement structures. Hence, we recommend the implementation of patient engagement in planning of chronic studies with complex designs, long recording durations or high demand for individual active study participation.
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Affiliation(s)
- Lucia K. Feldmann
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Juliet Roudini
- QUEST Center for Responsible Research, Berlin Institute of Health at Charité, Berlin, Germany
- Patient and Stakeholder Engagement, Cluster of Excellence, NeuroCure, Berlin, Germany
| | - Andrea A. Kühn
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité – Universitätsmedizin Berlin, Berlin, Germany
- Berlin School of Mind and Brain, Charité University Medicine, Berlin, Germany
- NeuroCure Clinical Research Center, Charité University Medicine, Berlin, Germany
- DZNE, German Center for Neurodegenerative Diseases, Berlin, Germany
| | - Jeroen G. V. Habets
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité – Universitätsmedizin Berlin, Berlin, Germany
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Cakmak OO, Akar K, Youssef H, Samanci MY, Ertan S, Vural A. Comparative Assessment of Gait and Balance in Patients with Parkinson's Disease and Normal Pressure Hydrocephalus. SISLI ETFAL HASTANESI TIP BULTENI 2023; 57:232-237. [PMID: 37899810 PMCID: PMC10600622 DOI: 10.14744/semb.2023.79990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 05/17/2023] [Accepted: 05/17/2023] [Indexed: 10/31/2023]
Abstract
Objectives We aim to compare balance and gait parameters in patients diagnosed with Parkinson's disease (PD) and normal pressure hydrocephalus (NPH). Methods A total of 13 patients with NPH, 20 with PD, and 13 healthy controls (HC) recruited in the study. Three IMU sensors (Ambulatory PD Monitoring Inc., OR, USA) were placed on the lumbar area and the feet of the participants. The balance evaluations comprised eight successive standing tasks; the modified clinical test of sensory interaction on balance test. These tasks involved standing with feet apart and eyes open as well as eyes closed on a firm and foam surface, standing with feet together and eyes open as well as eyes closed, and tandem stance with the right foot front and the left foot front. Functional evaluations of gait were conducted using the 10-M Walk Test (10 MWT), the 2 min-Walk Test (2 MWT), and the timed-up and go (TUG). Parameters of the gait and balance were analyzed and then compared. Results NPH patients displayed a notable decrease in both stride length and gait speed as compared with both PD patients and healthy participants. The balance tests revealed that the NPH group demonstrated significantly poorer performance, specifically in the feet-apart eyes-closed foam-surface test, and the tandem stance test. During the tasks while eyes were open on firm and foam surfaces, PD and NPH groups showed an increase in root mean square sway, range, and mean velocity (p<0.05) of sway in the anteroposterior plane. In addition, during the TUG test, the NPH group exhibited a significant prolongation in the time needed to complete the task and a decline in turning velocity as compared to PD, but no notable difference was seen in comparison to the HC group. Conclusion Our study indicated that the patients with NPH exhibited notably worse gait and balance measurements in comparison to both the PD patients and HC groups. These findings emphasize the significance of monitoring and managing gait and balance impairments in NPH patients. Sensor-based technologies may offer objective parameters for a more precise and efficient follow-up of these patients in terms of gait and balance.
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Affiliation(s)
- Ozgur Oztop Cakmak
- Department of Neurology, Koc University Faculty of Medicine, Istanbul, Türkiye
| | - Kardelen Akar
- Motion Analysis Laboratory, Koc University, KUTTAM, Istanbul, Türkiye
| | - Hussein Youssef
- Motion Analysis Laboratory, Koc University, KUTTAM, Istanbul, Türkiye
| | | | - Sibel Ertan
- Department of Neurology, Koc University Faculty of Medicine, Istanbul, Türkiye
| | - Atay Vural
- Department of Neurology, Koc University Faculty of Medicine, Istanbul, Türkiye
- Motion Analysis Laboratory, Koc University, KUTTAM, Istanbul, Türkiye
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Habets JGV, Spooner RK, Mathiopoulou V, Feldmann LK, Busch JL, Roediger J, Bahners BH, Schnitzler A, Florin E, Kühn AA. A First Methodological Development and Validation of ReTap: An Open-Source UPDRS Finger Tapping Assessment Tool Based on Accelerometer-Data. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23115238. [PMID: 37299968 DOI: 10.3390/s23115238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 05/24/2023] [Accepted: 05/29/2023] [Indexed: 06/12/2023]
Abstract
Bradykinesia is a cardinal hallmark of Parkinson's disease (PD). Improvement in bradykinesia is an important signature of effective treatment. Finger tapping is commonly used to index bradykinesia, albeit these approaches largely rely on subjective clinical evaluations. Moreover, recently developed automated bradykinesia scoring tools are proprietary and are not suitable for capturing intraday symptom fluctuation. We assessed finger tapping (i.e., Unified Parkinson's Disease Rating Scale (UPDRS) item 3.4) in 37 people with Parkinson's disease (PwP) during routine treatment follow ups and analyzed their 350 sessions of 10-s tapping using index finger accelerometry. Herein, we developed and validated ReTap, an open-source tool for the automated prediction of finger tapping scores. ReTap successfully detected tapping blocks in over 94% of cases and extracted clinically relevant kinematic features per tap. Importantly, based on the kinematic features, ReTap predicted expert-rated UPDRS scores significantly better than chance in a hold out validation sample (n = 102). Moreover, ReTap-predicted UPDRS scores correlated positively with expert ratings in over 70% of the individual subjects in the holdout dataset. ReTap has the potential to provide accessible and reliable finger tapping scores, either in the clinic or at home, and may contribute to open-source and detailed analyses of bradykinesia.
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Affiliation(s)
- Jeroen G V Habets
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité Universitaetsmedizin Berlin, 10117 Berlin, Germany
| | - Rachel K Spooner
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Varvara Mathiopoulou
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité Universitaetsmedizin Berlin, 10117 Berlin, Germany
| | - Lucia K Feldmann
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité Universitaetsmedizin Berlin, 10117 Berlin, Germany
| | - Johannes L Busch
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité Universitaetsmedizin Berlin, 10117 Berlin, Germany
| | - Jan Roediger
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité Universitaetsmedizin Berlin, 10117 Berlin, Germany
| | - Bahne H Bahners
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
- Department of Neurology, Center for Movement Disorders and Neuromodulation, Medical Faculty, Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Alfons Schnitzler
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
- Department of Neurology, Center for Movement Disorders and Neuromodulation, Medical Faculty, Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Esther Florin
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Andrea A Kühn
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité Universitaetsmedizin Berlin, 10117 Berlin, Germany
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