生物医学语音测量数据集
包含42名早期帕金森病患者16种语音测量和UPDRS评分的数据集，用于预测运动与总UPDRS评分。
基本信息
资源简介
该数据集由牛津大学Athanasios Tsanas和Max Little创建，与美国10家医疗中心和Intel公司合作，用于远程症状进展监测的六个月试验。包含42名早期帕金森病患者的生物医学语音测量，包括语音记录、年龄、性别、时间间隔、运动UPDRS、总UPDRS和16种生物医学语音测量。主要目的是从16种语音测量中预测运动和总UPDRS评分。
下载信息
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使用方式
数据集获取
git clone https://github.com/Alireza-Rahimi-3000/Speech-Data-Analysis-Methodology-to-Diagnose-Parkinson-Using-Various-ML-Algorithms.git
curl -L -o repo.zip https://github.com/Alireza-Rahimi-3000/Speech-Data-Analysis-Methodology-to-Diagnose-Parkinson-Using-Various-ML-Algorithms/archive/refs/heads/main.zip
unzip repo.zip
源站 README 摘录（使用方式）
Speech Data Analysis Methodology to Diagnose Parkinson Using Various ML Algorithms
In this study, we will analyze the patients’ data who are diagnosed with the disease. Using speech data from subjects is expected to help the development of a noninvasive diagnostic. People with Parkinsonism (PWP) suffer from speech impairments like dysphonia (defective use of the voice), hypophonia (reduced volume), monotone (reduced pitch range), and dysarthria (difficulty with articulation of sounds or syllables). Therefore, our analysis in this project will be based on voice parameters of the affected.
Data
The dataset was created by Athanasios Tsanas and Max Little of the University of Oxford, in collaboration with 10 medical centers in the US and Intel Corporation who developed the tele-monitoring device to record the speech signals.
This dataset is composed of a range of biomedical voice measurements from 42 people with early-stage Parkinson’s disease recruited to a six-month trial of a tele-monitoring device for remote symptom progression monitoring. The recordings were automatically captured in the patient’s homes.
Columns in the dataset contain subject number, subject age, subject gender, time interval from baseline recruitment date, motor UPDRS, total UPDRS, and 16 biomedical voice measures. Each row corresponds to one of 5,875 voice recording from these individuals. The main aim of the data is to predict the motor and total UPDRS scores (‘motor_UPDRS’ and ‘total_UPDRS’) from the 16 voice measures. The data is in ASCII CSV format. The rows of the CSV file contain an instance corresponding to one voice recording. There are around 200 recordings per patient, the subject number of the patient is identified in the first column.
Data Cleaning and Outlier Removal
Our first step is going through the dataset and identify any missing value or outlier to take necessary measures. This step is essential to prepare the data for fruitful analysis. There are no missing values in our dataset.
Correlations between the variables
We can see that all the jitter variables highly correlate with Shimmer variables.
Outlier Detection
In this section we will look at some of the significant features and check if there are outliers available.
![43223622-5079b920-9019-11e8-8cba-3c46823e0b17](https://user-images.githubusercontent.com/88687718/1821420
数据加载示例（表格/文本类）
import pandas as pd, glob, os
files = (glob.glob(os.path.join(path, "**", "*.csv"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.tsv"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.xlsx"), recursive=True))
print("数据文件:", files)
df = pd.read_csv(files[0])
print(df.shape); print(df.columns.tolist()); print(df.head(3))
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