Parkinson's Disease Tremor Dataset - ALAMEDA

The ALAMEDA_PD_tremor_dataset.csv contains 92 features extracted from raw accelerometer data after pre-processing, 4 tremor-related labels and some other metadata. In total, it includes 99 columns: The first two columns correspond to the start_timestamp and the end_timestamp of the time window from which the respective features have been extracted. The third column corresponds to the subject_id, which is used to uniquely identify PD patients enrolled in the current study. The next 92 columns correspond to features extracted from raw triaxial accelerometer data collected with the GENEActiv smart bracelets throughout 30-min MDS-UPDRS assessment during in-clinic visits, after applying some preprocessing steps. First, the accelerometer signals were band-pass filtered [2.5 Hz, 12.5 Hz] to enable tremor detection. Then, the magnitude and the first principal component of the filtered signals were computed to attenuate the dependency on sensor placement and orientation. Finally, the transformed signals were segmented into time windows of 2048 samples (or 20.48 sec) with 50% overlap. Then, 92 features were extracted in both time and frequency domains. Spectral features were extracted after applying Fast Fourier Transform. The full list of the extracted features is demonstrated in the table below. These features can feed Machine Learning models to predict the presence/absence of PD tremor. The final 4 columns correspond to tremor-related labels (Constancy_of_rest, Kinetic_tremor, Postural_tremor and Rest_tremor). They derive from the respective MDS-UPDRS III annotations, after transforming them to make them suitable for binary classification. More specifically, zero scores remained 0 to indicate the absence of tremor while positive scores were transformed to 1 to indicate the presence of tremor. Each of these columns can be used as a target to be predicted with the help of Machine Learning models.

搜集汇总

背景与挑战

背景概述

该数据集包含92个从帕金森病患者加速度计数据中提取的特征和4个震颤相关标签，用于机器学习模型预测震颤存在/缺失。数据来源于临床评估期间使用智能手环收集的原始信号，并经过预处理和特征工程处理。