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Weschke S, Lüdtke S, Schaat S, Gube M, Weippert M, Bruhn S, Kirste T, Teipel SJ. P2‐288: MEASURING GAIT CHARACTERISTICS OF INDUCED DISORIENTATION IN A VR ENVIRONMENT. Alzheimers Dement 2018. [DOI: 10.1016/j.jalz.2018.06.977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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Grützmacher F, Beichler B, Hein A, Kirste T, Haubelt C. Time and Memory Efficient Online Piecewise Linear Approximation of Sensor Signals. SENSORS 2018; 18:s18061672. [PMID: 29882849 PMCID: PMC6022087 DOI: 10.3390/s18061672] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/21/2018] [Revised: 05/17/2018] [Accepted: 05/18/2018] [Indexed: 11/17/2022]
Abstract
Piecewise linear approximation of sensor signals is a well-known technique in the fields of Data Mining and Activity Recognition. In this context, several algorithms have been developed, some of them with the purpose to be performed on resource constrained microcontroller architectures of wireless sensor nodes. While microcontrollers are usually constrained in computational power and memory resources, all state-of-the-art piecewise linear approximation techniques either need to buffer sensor data or have an execution time depending on the segment’s length. In the paper at hand, we propose a novel piecewise linear approximation algorithm, with a constant computational complexity as well as a constant memory complexity. Our proposed algorithm’s worst-case execution time is one to three orders of magnitude smaller and its average execution time is three to seventy times smaller compared to the state-of-the-art Piecewise Linear Approximation (PLA) algorithms in our experiments. In our evaluations, we show that our algorithm is time and memory efficient without sacrificing the approximation quality compared to other state-of-the-art piecewise linear approximation techniques, while providing a maximum error guarantee per segment, a small parameter space of only one parameter, and a maximum latency of one sample period plus its worst-case execution time.
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Hein A, Grützmacher F, Haubelt C, Kirste T. Fast care – real-time sensor data analysis framework for intelligent assistance systems. CURRENT DIRECTIONS IN BIOMEDICAL ENGINEERING 2017. [DOI: 10.1515/cdbme-2017-0157] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
AbstractMain target of fast care is the development of a real-time capable sensor data analysis framework for intelligent assistive systems in the field of Ambient Assisted Living, eHealth, Tele Rehabilitation, and Tele Care. The aim is to provide a medically valid integrated situation model based on a distributed, ad-hoc connected, energy-efficient sensor infrastructure suitable for daily use. The integrated situation model combining physiological, cognitive, and kinematic information about the patient is grounded on the intelligent fusion of heterogeneous sensor data on different levels. The model can serve as a tool for quickly identifying risk and hazards as well as enable medical assistance systems to autonomously intervene in real-time and actively give telemedical feedback.
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Dyrba M, Grothe MJ, Binder H, Kirste T, Teipel SJ. [IC‐P‐029]: GAUSSIAN MARKOV RANDOM FIELDS FOR ASSESSING INTERMODAL REGIONAL ASSOCIATIONS IN PRODROMAL ALZHEIMER's DISEASE. Alzheimers Dement 2017. [DOI: 10.1016/j.jalz.2017.06.2301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Teipel S, Heine C, Hein A, Krüger F, Kutschke A, Kernebeck S, Halek M, Bader S, Kirste T. Multidimensional assessment of challenging behaviors in advanced stages of dementia in nursing homes-The insideDEM framework. ALZHEIMER'S & DEMENTIA: DIAGNOSIS, ASSESSMENT & DISEASE MONITORING 2017; 8:36-44. [PMID: 28462388 PMCID: PMC5403785 DOI: 10.1016/j.dadm.2017.03.006] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
INTRODUCTION Assessment of challenging behaviors in dementia is important for intervention selection. Here, we describe the technical and experimental setup and the feasibility of long-term multidimensional behavior assessment of people with dementia living in nursing homes. METHODS We conducted 4 weeks of multimodal sensor assessment together with real-time observation of 17 residents with moderate to very severe dementia in two nursing care units. Nursing staff received extensive training on device handling and measurement procedures. Behavior of a subsample of eight participants was further recorded by videotaping during 4 weeks during day hours. Sensors were mounted on the participants' wrist and ankle and measured motion, rotation, as well as surrounding loudness level, light level, and air pressure. RESULTS Participants were in moderate to severe stages of dementia. Almost 100% of participants exhibited relevant levels of challenging behaviors. Automated quality control detected 155 potential issues. But only 11% of the recordings have been influenced by noncompliance of the participants. Qualitative debriefing of staff members suggested that implementation of the technology and observation platform in the routine procedures of the nursing home units was feasible and identified a range of user- and hardware-related implementation and handling challenges. DISCUSSION Our results indicate that high-quality behavior data from real-world environments can be made available for the development of intelligent assistive systems and that the problem of noncompliance seems to be manageable. Currently, we train machine-learning algorithms to detect episodes of challenging behaviors in the recorded sensor data.
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Yordanova K, Koldrack P, Heine C, Henkel R, Martin M, Teipel S, Kirste T. Situation Model for Situation-Aware Assistance of Dementia Patients in Outdoor Mobility. J Alzheimers Dis 2017; 60:1461-1476. [PMID: 29060937 PMCID: PMC5676980 DOI: 10.3233/jad-170105] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/17/2017] [Indexed: 12/11/2022]
Abstract
BACKGROUND Dementia impairs spatial orientation and route planning, thus often affecting the patient's ability to move outdoors and maintain social activities. Situation-aware deliberative assistive technology devices (ATD) can substitute impaired cognitive function in order to maintain one's level of social activity. To build such a system, one needs domain knowledge about the patient's situation and needs. We call this collection of knowledge situation model. OBJECTIVE To construct a situation model for the outdoor mobility of people with dementia (PwD). The model serves two purposes: 1) as a knowledge base from which to build an ATD describing the mobility of PwD; and 2) as a codebook for the annotation of the recorded behavior. METHODS We perform systematic knowledge elicitation to obtain the relevant knowledge. The OBO Edit tool is used for implementing and validating the situation model. The model is evaluated by using it as a codebook for annotating the behavior of PwD during a mobility study and interrater agreement is computed. In addition, clinical experts perform manual evaluation and curation of the model. RESULTS The situation model consists of 101 concepts with 11 relation types between them. The results from the annotation showed substantial overlapping between two annotators (Cohen's kappa of 0.61). CONCLUSION The situation model is a first attempt to systematically collect and organize information related to the outdoor mobility of PwD for the purposes of situation-aware assistance. The model is the base for building an ATD able to provide situation-aware assistance and to potentially improve the quality of life of PwD.
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Koldrack P, Teipel SJ, Kirste T. TD‐P‐019: Sensing Disorientation of Persons with Dementia in Outdoor Wayfinding Tasks Using Wearable Sensors to Enable Situation‐Aware Navigation Assistance. Alzheimers Dement 2016. [DOI: 10.1016/j.jalz.2016.06.265] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Heine C, Koldrack P, Yordanova K, Kasper E, Kirste T, Teipel SJ. P1‐215: Behavioural Manifestations of Disorientation of Persons with Alzheimer's Disease Dementia in Outdoor Wayfinding Tasks: Towards a Situation Aware Assistance. Alzheimers Dement 2016. [DOI: 10.1016/j.jalz.2016.06.964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Koldrack P, Kirste T, Teipel SJ. F3‐03‐02: Computational Description of Wayfinding Behavior in Outdoor Environments of People with Dementia Using Ontologies and Sensor Data. Alzheimers Dement 2016. [DOI: 10.1016/j.jalz.2016.06.497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Teipel S, Babiloni C, Hoey J, Kaye J, Kirste T, Burmeister OK. Information and communication technology solutions for outdoor navigation in dementia. Alzheimers Dement 2016; 12:695-707. [DOI: 10.1016/j.jalz.2015.11.003] [Citation(s) in RCA: 44] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2015] [Revised: 10/21/2015] [Accepted: 11/12/2015] [Indexed: 10/22/2022]
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Yordanova K, Kirste T. A Process for Systematic Development of Symbolic Models for Activity Recognition. ACM T INTERACT INTEL 2016. [DOI: 10.1145/2806893] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
Several emerging approaches to activity recognition (AR) combine symbolic representation of user actions with probabilistic elements for reasoning under uncertainty. These approaches provide promising results in terms of recognition performance, coping with the uncertainty of observations, and model size explosion when complex problems are modelled. But experience has shown that it is not always intuitive to model even seemingly simple problems. To date, there are no guidelines for developing such models. To address this problem, in this work we present a development process for building symbolic models that is based on experience acquired so far as well as on existing engineering and data analysis workflows. The proposed process is a first attempt at providing structured guidelines and practices for designing, modelling, and evaluating human behaviour in the form of symbolic models for AR. As an illustration of the process, a simple example from the office domain was developed. The process was evaluated in a comparative study of an intuitive process and the proposed process. The results showed a significant improvement over the intuitive process. Furthermore, the study participants reported greater ease of use and perceived effectiveness when following the proposed process. To evaluate the applicability of the process to more complex AR problems, it was applied to a problem from the kitchen domain. The results showed that following the proposed process yielded an average accuracy of 78%. The developed model outperformed state-of-the-art methods applied to the same dataset in previous work, and it performed comparably to a symbolic model developed by a model expert without following the proposed development process.
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Dyrba M, Grothe MJ, Kirste T, Teipel SJ. P2‐131: Analysis of inter‐modal associations and dependencies of regional disease patterns based on multimodal imaging using markov random fields. Alzheimers Dement 2015. [DOI: 10.1016/j.jalz.2015.06.669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Dyrba M, Grothe MJ, Kirste T, Teipel SJ. IC‐P‐080: Analysis of intermodal associations and dependencies of regional disease patterns based on multimodal imaging using markov random fields. Alzheimers Dement 2015. [DOI: 10.1016/j.jalz.2015.06.101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Dyrba M, Grothe M, Kirste T, Teipel SJ. Multimodal analysis of functional and structural disconnection in Alzheimer's disease using multiple kernel SVM. Hum Brain Mapp 2015; 36:2118-31. [PMID: 25664619 DOI: 10.1002/hbm.22759] [Citation(s) in RCA: 128] [Impact Index Per Article: 14.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2015] [Accepted: 01/30/2015] [Indexed: 01/13/2023] Open
Abstract
Alzheimer's disease (AD) patients exhibit alterations in the functional connectivity between spatially segregated brain regions which may be related to both local gray matter (GM) atrophy as well as a decline in the fiber integrity of the underlying white matter tracts. Machine learning algorithms are able to automatically detect the patterns of the disease in image data, and therefore, constitute a suitable basis for automated image diagnostic systems. The question of which magnetic resonance imaging (MRI) modalities are most useful in a clinical context is as yet unresolved. We examined multimodal MRI data acquired from 28 subjects with clinically probable AD and 25 healthy controls. Specifically, we used fiber tract integrity as measured by diffusion tensor imaging (DTI), GM volume derived from structural MRI, and the graph-theoretical measures 'local clustering coefficient' and 'shortest path length' derived from resting-state functional MRI (rs-fMRI) to evaluate the utility of the three imaging methods in automated multimodal image diagnostics, to assess their individual performance, and the level of concordance between them. We ran the support vector machine (SVM) algorithm and validated the results using leave-one-out cross-validation. For the single imaging modalities, we obtained an area under the curve (AUC) of 80% for rs-fMRI, 87% for DTI, and 86% for GM volume. When it came to the multimodal SVM, we obtained an AUC of 82% using all three modalities, and 89% using only DTI measures and GM volume. Combined multimodal imaging data did not significantly improve classification accuracy compared to the best single measures alone.
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Dyrba M, Barkhof F, Fellgiebel A, Filippi M, Hausner L, Hauenstein K, Kirste T, Teipel SJ. Predicting Prodromal Alzheimer's Disease in Subjects with Mild Cognitive Impairment Using Machine Learning Classification of Multimodal Multicenter Diffusion-Tensor and Magnetic Resonance Imaging Data. J Neuroimaging 2015; 25:738-47. [PMID: 25644739 DOI: 10.1111/jon.12214] [Citation(s) in RCA: 62] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2014] [Revised: 12/01/2014] [Accepted: 12/10/2014] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND Alzheimer's disease (AD) patients show early changes in white matter (WM) structural integrity. We studied the use of diffusion tensor imaging (DTI) in assessing WM alterations in the predementia stage of mild cognitive impairment (MCI). METHODS We applied a Support Vector Machine (SVM) classifier to DTI and volumetric magnetic resonance imaging data from 35 amyloid-β42 negative MCI subjects (MCI-Aβ42-), 35 positive MCI subjects (MCI-Aβ42+), and 25 healthy controls (HC) retrieved from the European DTI Study on Dementia. The SVM was applied to DTI-derived fractional anisotropy, mean diffusivity (MD), and mode of anisotropy (MO) maps. For comparison, we studied classification based on gray matter (GM) and WM volume. RESULTS We obtained accuracies of up to 68% for MO and 63% for GM volume when it came to distinguishing between MCI-Aβ42- and MCI-Aβ42+. When it came to separating MCI-Aβ42+ from HC we achieved an accuracy of up to 77% for MD and a significantly lower accuracy of 68% for GM volume. The accuracy of multimodal classification was not higher than the accuracy of the best single modality. CONCLUSIONS Our results suggest that DTI data provide better prediction accuracy than GM volume in predementia AD.
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Krüger F, Nyolt M, Yordanova K, Hein A, Kirste T. Computational state space models for activity and intention recognition. A feasibility study. PLoS One 2014; 9:e109381. [PMID: 25372138 PMCID: PMC4220990 DOI: 10.1371/journal.pone.0109381] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2014] [Accepted: 09/04/2014] [Indexed: 12/05/2022] Open
Abstract
Background Computational state space models (CSSMs) enable the knowledge-based construction of Bayesian filters for recognizing intentions and reconstructing activities of human protagonists in application domains such as smart environments, assisted living, or security. Computational, i. e., algorithmic, representations allow the construction of increasingly complex human behaviour models. However, the symbolic models used in CSSMs potentially suffer from combinatorial explosion, rendering inference intractable outside of the limited experimental settings investigated in present research. The objective of this study was to obtain data on the feasibility of CSSM-based inference in domains of realistic complexity. Methods A typical instrumental activity of daily living was used as a trial scenario. As primary sensor modality, wearable inertial measurement units were employed. The results achievable by CSSM methods were evaluated by comparison with those obtained from established training-based methods (hidden Markov models, HMMs) using Wilcoxon signed rank tests. The influence of modeling factors on CSSM performance was analyzed via repeated measures analysis of variance. Results The symbolic domain model was found to have more than states, exceeding the complexity of models considered in previous research by at least three orders of magnitude. Nevertheless, if factors and procedures governing the inference process were suitably chosen, CSSMs outperformed HMMs. Specifically, inference methods used in previous studies (particle filters) were found to perform substantially inferior in comparison to a marginal filtering procedure. Conclusions Our results suggest that the combinatorial explosion caused by rich CSSM models does not inevitably lead to intractable inference or inferior performance. This means that the potential benefits of CSSM models (knowledge-based model construction, model reusability, reduced need for training data) are available without performance penalty. However, our results also show that research on CSSMs needs to consider sufficiently complex domains in order to understand the effects of design decisions such as choice of heuristics or inference procedure on performance.
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Dyrba M, Ewers M, Plant C, Barkhof F, Fellgiebel A, Hausner L, Filippi M, Kirste T, Teipel SJ. IC‐P‐072: PREDICTION OF PRODROMAL AD IN MCI SUBJECTS USING MULTICENTER DTI AND MRI DATA AND MULTIPLE KERNELS SVM: AN EDSD STUDY. Alzheimers Dement 2014. [DOI: 10.1016/j.jalz.2014.05.077] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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Dyrba M, Ewers M, Plant C, Barkhof F, Fellgiebel A, Hausner L, Filippi M, Kirste T, Teipel SJ. P1‐255: PREDICTION OF PRODROMAL AD IN MCI SUBJECTS USING MULTICENTER DTI AND MRI DATA AND MULTIPLE KERNELS SVM: AN EDSD STUDY. Alzheimers Dement 2014. [DOI: 10.1016/j.jalz.2014.05.495] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Koldrack P, Teipel SJ, Kirste T. P1‐354: TAILORING NAVIGATION SUPPORT TO THE NEEDS AND CAPABILITIES OF PERSONS WITH MCI AND EARLY AD. Alzheimers Dement 2014. [DOI: 10.1016/j.jalz.2014.05.596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Dyrba M, Grothe M, Kirste T, Teipel SJ. P3‐222: MULTIMODAL ANALYSIS OF FUNCTIONAL AND STRUCTURAL DISCONNECTION IN AD USING MULTIPLE KERNELS SVM. Alzheimers Dement 2014. [DOI: 10.1016/j.jalz.2014.05.1313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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Kirste T, Hoffmeyer A, Koldrack P, Bauer A, Schubert S, Schröder S, Teipel S. Detecting the effect of Alzheimer's disease on everyday motion behavior. J Alzheimers Dis 2014; 38:121-32. [PMID: 24077435 DOI: 10.3233/jad-130272] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Early detection of behavioral changes in Alzheimer's disease (AD) would help the design and implementation of specific interventions. OBJECTIVE The target of our investigation was to establish a correlation between diagnosis and unconstrained motion behavior in subjects without major clinical behavior impairments. METHOD We studied everyday motion behavior in 23 dyads with one partner suffering from AD dementia and one cognitively healthy partner in the subjects' home, employing ankle-mounted three-axes accelerometric sensors. We determined frequency features obtained from the signal envelopes computed by an envelope detector for the carrier band 0.5 Hz to 5 Hz. Based on these features, we employed quadratic discriminant analysis for building models discriminating between AD patients and healthy controls. RESULTS After leave-one-out cross-validation, the classification accuracy of motion features reached 91% and was superior to the classification accuracy based on the Cohen-Mansfield Agitation Inventory (CMAI). Motion features were significantly correlated with MMSE and CMAI scores. CONCLUSION Our findings suggest that changes of everyday behavior are detectable in accelerometric behavior protocols even in the absence of major clinical behavioral impairments in AD.
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Dyrba M, Grothe M, Kirste T, Teipel S. Kombinierte Erkennung funktioneller und struktureller Diskonnektionsmuster bei der Alzheimer-Krankheit mittels multimodaler MRT und maschineller Lernverfahren. KLIN NEUROPHYSIOL 2014. [DOI: 10.1055/s-0034-1371316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Dyrba M, Ewers M, Wegrzyn M, Plant C, Oswald A, Pievani M, Bokde A, Fellgiebel A, Filippi M, Hausner L, Barkhof F, Hampel H, Klöppel S, Hauenstein K, Kirste T, Teipel S. P2–196: Predicting prodromal Alzheimer's disease in people with mild cognitive impairment using multicenter diffusion‐tensor imaging data and machine learning algorithms. Alzheimers Dement 2013. [DOI: 10.1016/j.jalz.2013.05.841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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Dyrba M, Koldrack P, Schubert S, Bauer A, Kirste T, Teipel S. P4–221: Characterizing the effect of Alzheimer's disease on everyday motion behavior. Alzheimers Dement 2013. [DOI: 10.1016/j.jalz.2013.05.1613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Dyrba M, Grothe M, Kirste T, Teipel S. IC‐P‐178: Multimodal support vector machine for automated detection of functional and structural disconnection in Alzheimer's disease. Alzheimers Dement 2013. [DOI: 10.1016/j.jalz.2013.05.175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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