Application of Human-Computer Interaction Technology in Ancillary Diagnosis of Nervous System Diseases: Current Situation and Prospect
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摘要: 随着人机交互技术的发展，如何利用智能、自然、高效的交互方式促进医学的发展，是近年来研究的热点问题。神经系统疾病极大影响了人们的日常生活质量，利用人机交互技术对神经系统疾病进行早期预警与辅助诊断，可在提高患者检查舒适感的同时，减轻医生工作强度，因此具有深远的临床意义。本文通过论述人机交互技术在神经系统疾病辅助诊断中的应用现状、存在问题及发展前景，思考如何利用计算机技术改进传统医学诊断方法。Abstract: With the development of human-computer interaction, how to use intelligent, natural and efficient methods to promote the development of the medical field has become a hot topic of research. Nervous system diseases have a great impact on the quality of people's daily life. Using the method of human-computer interaction for early warning and ancillary diagnosis of nervous system diseases can reduce discomfort of patients during exams and work intensity of doctors, which is of great significance to both doctors and patients. This paper discusses the application status, existing problems, future development of human-computer interaction in the ancillary diagnosis of nervous system diseases, and how to use computer technology to improve traditional medical diagnosis methods from the perspective of interaction.
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Key words:
- human-computer interaction /
- medical treatment /
- nervous system diseases
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表 1 基于人机交互的神经系统疾病辅助诊断技术及其临床应用
交互模式 设备 交互任务 病理体征 计算特征 诊断疾病 笔交互[7-12] 触控屏
电子笔笔迹任务：阿基米德螺旋线和重复的字母书写 手部震颤、僵硬、运动迟缓 位移相关
时间相关
压力相关帕金森病 绘图任务：TMT和CDT 手部震颤、僵硬、缓慢认知降低 位移相关
压力相关
时间相关
错误相关
图形比例相关
图形角度相关
目标识别帕金森病
轻度创伤性脑损伤
多发性硬化症
双相情感障碍
阿尔茨海默病
轻度认知障碍
血管性认知障碍语音交互[16-21] 麦克风 连续性语句 呼吸节奏、共振协调性、发音和韵律改变 语音识别相关
能量谱相关帕金森病
小脑共济失调
肌萎缩侧索硬化
阿尔茨海默病
认知能力衰退持续元音发音 震动不规律、噪音、嘶哑 声带震动相关
噪音相关
发音器官相关帕金森病
多系统萎缩
功能性神经障碍
颈部肌张力障碍
原发性震颤
全身性阵发性肌张力障碍步态交互[23-24, 26-28] 传感器
摄像头指令站立与行走测试 小碎步、冻结步态、平衡能力下降 位移相关
时间相关
角度相关小脑共济失调
帕金森病
脑卒中
脑瘫生理计算[30-32, 34-35] 脑电图
肌电图脑电图：无特定任务和视觉注意力任务 脑电波信号异常 相位振幅波形相关
频域相关非线性动力学特征理论
混沌理论缺血性脑卒中
癫痫
多发性硬化症肌电图：肘屈曲 肌肉电信号异常 相位相关
振幅相关帕金森病 
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[1] Card SK, Moran TP, Newell A. The Psychology of Human-Computer Interaction[J]. Am J Psychol, 1983. doi: 10.2307/1422176. [2] Grammatikopoulou A, Grammalidis N, Bostantjopoulou S, et al. Detecting hypomimia symptoms by selfie photo analysis: for early Parkinson disease detection[C]. Pervasive Technologies Related to Assistive Environments, 2019: 517-522. [3] Gordon ML, Gatys LA, Guestrin C, et al. App Usage Predicts Cognitive Ability in Older Adults[C]. Human Factors in Computing Systems, 2019, 168: 1-12. [4] Aoe J, Fukuma R, Yanagisawa T, et al. Automatic diagnosis of neurological diseases using MEG signals with a deep neural network[J]. Sci Rep, 2019, 9: 5057. doi: 10.1038/s41598-019-41500-x [5] Chiuchisan I, Chiuchisan I, Geman O, et al. Tremor Measurement System for Neurological Disorders Screening[J]. Springer Cham, 2016, 633: 339-348. http://www.eed.usv.ro/idei_48/articles/SOFA_2016_paper2.pdf [6] Impedovo D, Pirlo G, Vessio G, et al. A Handwriting-Based Protocol for Assessing Neurodegenerative Dementia[J]. Cogn Comput, 2019, 11: 576-586. doi: 10.1007/s12559-019-09642-2 [7] Smits EJ, Tolonen A, Cluitmans L, et al. Standardized handwriting to assess bradykinesia, micrographia and tremor in Parkinson's disease[J]. PLoS One, 2014, 9: e97614. doi: 10.1371/journal.pone.0097614 [8] Fellows RP, Dahmen J, Cook D, et al. Multicomponent analysis of a digital Trail Making Test[J]. Clin Neuropsychol, 2017, 31: 154-167. doi: 10.1080/13854046.2016.1238510 [9] Harbi, Zainab. Automatic interpretation of clock drawings for computerised assessment of dementia[D]. Wales: Cardiff University, 2017. [10] Drotár P, Mekyska J, Rektorová I, et al. Evaluation of handwriting kinematics and pressure for differential diagnosis of Parkinson's disease[J]. Artif Intell Med, 2016, 67: 39-46. doi: 10.1016/j.artmed.2016.01.004 [11] Dahmen J, Cook D, Fellows R, et al. An analysis of a digital variant of the Trail Making Test using machine learning techniques[J]. Technol Health Care, 2017, 25: 251-264. doi: 10.3233/thc-161274 [12] Souillard-Mandar W, Davis R, Rudin C, et al. Learning classification models of cognitive conditions from subtle behaviors in the digital Clock Drawing Test[J]. Mach Learn, 2016, 102: 393-441. doi: 10.1007/s10994-015-5529-5 [13] Hirano M. Psycho-Acoustic Evaluation of Voice[J]. Clin Exam Voice, 1981. http://ci.nii.ac.jp/naid/10011685687 [14] Baken RJ, Orlikoff RF. Clinical Measurement of Speech and Voice[M]. San Diego: Singular Thomson Learning, 2000. [15] Mekyska J, Janousova E, Gomez-Vilda P, et al. Robust and complex approach of pathological speech signal analysis[J]. Neurocomputing, 2015, 167: 94-111. doi: 10.1016/j.neucom.2015.02.085 [16] Laaridh I, Kheder WB, Fredouille C, et al. Automatic Prediction of Speech Evaluation Metrics for Dysarthric Speech[C]. Interspeech, 2017: 1363-1366. [17] Kodrasi I, Bourlard H. Super-gaussianity of Speech Spectral Coefficients as a Potential Biomarker for Dysarthric Speech Detection[C]. ICASSP, 2019: 6400-6404. [18] Weiner J, Herff C, Schultz T. Speech-Based Detection of Alzheimer's Disease in Conversational German[C]. Interspeech, 2016: 1938-1942. [19] Tsanas A, Little MA, Mcsharry PE, et al. Novel Speech Signal Processing Algorithms for High-Accuracy Classifica-tion of Parkinson's Disease[J]. IEEE Trans Biomed Eng, 2012, 59: 1264-1271. doi: 10.1109/TBME.2012.2183367 [20] Benba A, Jilbab A, Hammouch A. Voice assessments for detecting patients with neurological diseases using PCA and NPCA[J]. Inter J Speech Technol, 2017, 20: 673-683. doi: 10.1007/s10772-017-9438-9 [21] Berus L, Klancnik S, Brezocnik M, et al. Classifying Parkinson's Disease Based on Acoustic Measures Using Artificial Neural Networks[J]. Sensors (Basel), 2018, 19: 16. doi: 10.3390/s19010016 [22] Prakash C, Kumar R, Mittal N. Recent developments in human gait research: parameters, approaches, applications, machine learning techniques, datasets and challenges[J]. Art Int Rev, 2016, 49: 1-40. doi: 10.1007/s10462-016-9514-6 [23] Martelli D, Aprigliano F, Agrawal SK. Gait Adjustments Against Multidirectional Waist-Pulls in Cerebellar Ataxia and Parkinson's Disease[J]. Bio Sys Rob, 2019, 21: 283-286. [24] 吴壮, 仲敏, 蒋旭, 等. 不同临床分期帕金森病患者的步态分析[J]. 临床神经病学杂志, 2021, 34: 169-172. doi: 10.3969/j.issn.1004-1648.2021.03.003Wu Z, Zhong M, Jiang X, et al. Gait analysis of patients with different stages Parkinson's disease[J]. Linchuang Shenjingbingxue Zazhi, 2021, 34: 169-172 doi: 10.3969/j.issn.1004-1648.2021.03.003 [25] Pavone L, Pasqua G, Ricciuti P, et al. A Kinect-Based Portable Automatic Gait Analysis System-An Experimental Validation[J]. Biom J Sci Tech Res, 2019, 17: 12552-12555. http://www.researchgate.net/publication/335541156_A_Kinect-Based_Portable_Automatic_Gait_Analysis_System_An_Experimental_Validation [26] Yoneyama M, Kurihara Y, Watanabe K, et al. Accelerometry-based gait analysis and its application to Parkinson's disease assessment- part 2: a new measure for quantifying walking behavior[J]. IEEE Trans Neural Syst Rehabil Eng, 2013, 21: 999-1005. doi: 10.1109/TNSRE.2013.2268251 [27] Latorre J, Llorens R, Colomer C, et al. Reliability and comparison of Kinect-based methods for estimating spatiotem-poral gait parameters of healthy and post-stroke individuals[J]. J Biomech, 2018, 72: 268-273. doi: 10.1016/j.jbiomech.2018.03.008 [28] Ma Y, Mithraratne K, Wilson N, et al. The Validity and Reliability of a Kinect v2-Based Gait Analysis System for Children with Cerebral Palsy[J]. Sensors (Basel), 2019, 19: 1660-1676. doi: 10.3390/s19071660 [29] Chakraborty S, Nandy A. Automatic Diagnosis of Cerebral Palsy Gait Using Computational Intelligence Techniques: A Low-Cost Multi-Sensor Approach[J]. IEEE Trans Neural Syst Rehabil Eng, 2020, 28: 2488-2496. . doi: 10.1109/TNSRE.2020.3028203 [30] Jordan KG. Emergency EEG and continuous EEG monitoring in acute ischemic stroke[J]. J Clin Neurophysiol, 2004, 21: 341-352. http://europepmc.org/abstract/MED/15592008 [31] Faust O, Acharya UR, Adeli H, et al. Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis[J]. Inter J Eng Res Appl, 2015, 26: 56-64. [32] Ahmadi A, Davoudi S, Daliri MR. Computer Aided Diagnosis System for multiple sclerosis disease based on phase to amplitude coupling in covert visual attention[J]. Comput Methods Programs Biomed, 2019, 169: 9-18. doi: 10.1016/j.cmpb.2018.11.006 [33] Zwarts MJ, Drost G, Stegeman DF. Recent progress in the diagnostic use of surface EMG for neurological diseases[J]. J Electromyogr Kinesiol, 2000, 10: 287-291. doi: 10.1016/S1050-6411(00)00020-1 [34] Robichaud JA, Pfann KD, Leurgans S, et al. Variability of EMG patterns: a potential neurophysiological marker of Parkinson's disease?[J]. Clin Neurophysiol, 2009, 120: 390-397. doi: 10.1016/j.clinph.2008.10.015 [35] Das T, Ghosh A, Guha S, et al. Classification of EEG Signals for Prediction of Seizure using Multi-Feature Extraction[C]. International Conference on Electronics, 2017: 1-4. [36] 李洋, 黄进, 田丰, 等. 云端融合的神经系统疾病多通道辅助诊断研究[J]. 中国科学(信息科学), 2017, 47: 1164-1182. https://www.cnki.com.cn/Article/CJFDTOTAL-PZKX201709004.htmLi Y, Huang J, Tian F, et al. Research on the multi-modal aided neurological disease diagnosis with synergy of cloud and client[J]. Zhongguo Kexue(Xinxi Kexue), 2017, 47: 1164-1182. https://www.cnki.com.cn/Article/CJFDTOTAL-PZKX201709004.htm [37] Hsu C, Liu Y, Zachary K, et al. Extracting Gait Velocity and Stride Length from Surrounding Radio Signals[C]. CHI Conference on Human Factors in Computing Systems, 2017: 2116-2126. [38] Cirillo D, Catuara-Solarz S, Morey C, et al. Sex and gender differences and biases in artificial intelligence for biomedi-cine and healthcare[J]. NPJ Digit Med, 2020, 3: 81. doi: 10.1038/s41746-020-0288-5 -
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