期刊名：Bioengineering

期刊主页：https://www.mdpi.com/journal/bioengineering

人工智能与多模态传感技术的深度融合正推动帕金森病诊疗从临床评估向智能化、连续化、个性化迈进。本期文献清单汇集 Bioengineering 系列研究，系统展示了语音分析（深度学习、自监督模型、频谱特征）、可穿戴传感（惯性测量单元、肌电信号）、脑信号解码（刺激诱发响应、深部脑刺激记录）及新型影像（经颅超声）在帕金森病早期检测、症状量化与进展评估中的前沿应用。研究同时涵盖移动健康管理（PARKA AI应用）、算法优化（语法演化、特征选择）及无创干预（经颅脉冲刺激），为帕金森病的精准筛查、远程监测与个性化治疗提供了坚实的理论与技术支撑。

1. Voice-Based Detection of Parkinson’s Disease Using Machine and Deep Learning Approaches: A Systematic Review

基于机器学习和深度学习方法的帕金森病语音检测：系统性综述

https://www.mdpi.com/2306-5354/12/11/1279

2. Stimulus-Evoked Brain Signals for Parkinson’s Detection: A Comprehensive Benchmark Performance Analysis on Cross-Stimulation and Channel-Wise Experiments

刺激诱发脑信号在帕金森病检测中的应用：基于交叉刺激和通道实验的综合性能基准分析

https://www.mdpi.com/2306-5354/12/11/1185

3. Voice-Based Early Diagnosis of Parkinson’s Disease Using Spectrogram Features and AI Models

基于语音的帕金森病早期诊断：频谱图特征与人工智能模型

https://www.mdpi.com/2306-5354/12/10/1052

4. Study Protocol: Investigating the Effects of Transcranial Pulse Stimulation in Parkinson’s Disease

研究方案：经颅脉冲刺激对帕金森病的影响研究

https://www.mdpi.com/2306-5354/12/7/773

5. Speech-Based Parkinson’s Detection Using Pre-Trained Self-Supervised Automatic Speech Recognition (ASR) Models and Supervised Contrastive Learning

基于预训练自监督自动语音识别（ASR）模型和监督对比学习的语音帕金森病检测

https://www.mdpi.com/2306-5354/12/7/728

6. PARKA AI: A Sensor-Integrated Mobile Application for Parkinson’s Disease Monitoring and Self-Management

PARKA AI：一款用于帕金森病监测和自我管理的集成传感器的移动应用程序

https://www.mdpi.com/2306-5354/12/10/1059

7. Evaluation of Parkinson’s Disease Motor Symptoms via Wearable Inertial Measurements Units and Surface Electromyography Sensors

利用可穿戴惯性测量单元和表面肌电图传感器评估帕金森病运动症状

https://www.mdpi.com/2306-5354/12/10/1116

8. Constructing Artificial Features with Grammatical Evolution for the Motor Symptoms of Parkinson’s Disease

利用语法演化构建帕金森病运动症状的人工特征

https://www.mdpi.com/2306-5354/12/12/1318

9. Constructing Artificial Features with Grammatical Evolution for the Motor Symptoms of Parkinson’s Disease

利用语法演化构建帕金森病运动症状的人工特征

https://www.mdpi.com/2306-5354/12/12/1318

10. The Effect of Data Leakage and Feature Selection on Machine Learning Performance for Early Parkinson’s Disease Detection

数据泄露和特征选择对早期帕金森病检测机器学习性能的影响

https://www.mdpi.com/2306-5354/12/8/845

11. Unveiling the Unpredictable in Parkinson’s Disease: Sensor-Based Monitoring of Dyskinesias and Freezing of Gait in Daily Life

揭示帕金森病中不可预测的因素：基于传感器的日常生活中运动障碍和步态冻结监测

https://www.mdpi.com/2306-5354/11/5/440

12. Adaptive vs. Conventional Deep Brain Stimulation: One-Year Subthalamic Recordings and Clinical Monitoring in a Patient with Parkinson’s Disease

自适应与传统深部脑刺激：帕金森病患者一年的丘脑底核记录和临床监测

https://www.mdpi.com/2306-5354/11/10/990

13. Automatic Transcranial Sonography-Based Classification of Parkinson’s Disease Using a Novel Dual-Channel CNXV2-DANet

基于新型双通道 CNXV2-DANet 的经颅超声自动帕金森病分类

https://www.mdpi.com/2306-5354/11/9/889

14. Leveraging Deep Learning for Fine-Grained Categorization of Parkinson’s Disease Progression Levels through Analysis of Vocal Acoustic Patterns

利用深度学习通过分析声学模式对帕金森病进展程度进行精细分类

https://www.mdpi.com/2306-5354/11/3/295

15. Hidden Markov Model for Parkinson’s Disease Patients Using Balance Control Data

基于平衡控制数据的帕金森病患者隐马尔可夫模型

https://www.mdpi.com/2306-5354/11/1/88

Bioengineering 期刊介绍

主编：Anthony Guiseppi-Elie, Texas A&M University, USA

期刊专注于发表生物医学工程及应用，生物分子、细胞和组织工程及其应用，生物工艺和生物系统工程及应用，生物化学工程与应用，生物信号处理与分析，仿生学与生物控制论，生物电子学和转化生物工程等相关的最新科学技术及应用等相关的研究成果。刊载研究论文、综述及短讯，鼓励学者发表详细的实验和理论结果。期刊已被PubMed、Scopus、SCIE (Web of Sciences) 等数据库收录。

2024 Impact Factor：3.7

2024 CiteScore：5.3

Time to First Decision：17 Days

Acceptance to Publication：2.8 Days

期刊主页：https://www.mdpi.com/journal/bioengineering