Conference paper Open Access

Multiple-Instance Learning for In-The-Wild Parkinsonian Tremor Detection

Alexandros Papadopoulos; Konstantinos Kyritsis; Sevasti Bostanjopoulou; Lisa Klingelhoefer; Ray K. Chaudhuri; Anastasios Delopoulos

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            <foaf:name>Department of Neurology, Technical University of Dresden, Dresden, Germany</foaf:name>
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            <foaf:name>International Parkinson Excellence Research Centre, King's College Hospital NHS Foundation Trust, London, UK</foaf:name>
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    <dct:title>Multiple-Instance Learning for In-The-Wild Parkinsonian Tremor Detection</dct:title>
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    <dct:description>&lt;p&gt;Parkinson&amp;rsquo;s Disease (PD) is a neurodegenerative&amp;nbsp;disorder that manifests through slowly progressing symptoms,&amp;nbsp;such as tremor, voice degradation and bradykinesia. Automated&amp;nbsp;detection of such symptoms has recently received much attention&amp;nbsp;by the research community, owing to the clinical benefits associated with the early diagnosis of the disease. Unfortunately,&amp;nbsp;most of the approaches proposed so far, operate under a strictly&amp;nbsp;laboratory setting, thus limiting their potential applicability in&amp;nbsp;real world conditions. In this work, we present a method for automatically detecting tremorous episodes related to PD, based on acceleration signals. We propose to address the problem&amp;nbsp;at hand, as a case of Multiple-Instance Learning, wherein a&amp;nbsp;subject is represented as an unordered bag of signal segments&amp;nbsp;and a single, expert-provided, ground-truth. We employ a&amp;nbsp;deep learning approach that combines feature learning and a&amp;nbsp;learnable pooling stage and is trainable end-to-end. Results on&amp;nbsp;a newly introduced dataset of accelerometer signals collected&amp;nbsp;in-the-wild confirm the validity of the proposed approach.&amp;nbsp;&lt;/p&gt;</dct:description>
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    <dct:title>Intelligent Parkinson eaRly detectiOn Guiding NOvel Supportive InterventionS</dct:title>
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Publication date:
July 31, 2019
DOI:
10.5281/zenodo.3676525

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