Parkinson's disease
<p>Electrocardiogram (ECG) interpretation is critical for diagnosing a wide range of cardiovascular conditions. To streamline and accelerate the development of deep learning models in this domain, we present a novel, image-based version of the PTB Diagnostic ECG Database tailored for use with convolutional neural networks (CNNs), vision transformers (ViTs), and other image classification architectures. This enhanced dataset consists of 516 grayscale .png images, each representing a 12-lead ECG signal arranged as a 2D matrix (12 × T, where T is the number of time steps).
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A flexible pressure sensing system built with a flexible pressure sensor collected footprint image video of mice in a dark environment and open field, including footprint data of Parkinson's mice and normal mice at 6 months and 9 months old. Among them, 804, 811, 812 and 825 were normal mice; Mice 1, 2, 3, 4, 653, 682 were normal mice, and the rest were PD mice.
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The PD-BioStampRC21 dataset provides data from a wearable sensor
accelerometry study conducted for studying activity, gait, tremor, and
other motor symptoms in individuals with Parkinson's disease (PD). In
addition to individuals with PD, the dataset also includes data for
controls that also went through the same study protocol as the PD
participants. Data were acquired using lightweight MC 10 BioStamp RC
sensors (MC 10 Inc, Lexington, MA), five of which were attached to
each participant for gathering data over a roughly two day
interval.
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I would be grateful if you cite my two following papers:
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