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Design of a Machine Learning-Assisted Wearable Accelerometer-Based Automated System for Studying the Effect of Dopaminergic Medicine on Gait Characteristics of Parkinson's Patients. JOURNAL OF HEALTHCARE ENGINEERING 2020; 2020:1823268. [PMID: 32148741 PMCID: PMC7049429 DOI: 10.1155/2020/1823268] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/24/2019] [Revised: 12/15/2019] [Accepted: 01/08/2020] [Indexed: 11/26/2022]
Abstract
In the last few years, the importance of measuring gait characteristics has increased tenfold due to their direct relationship with various neurological diseases. As patients suffering from Parkinson's disease (PD) are more prone to a movement disorder, the quantification of gait characteristics helps in personalizing the treatment. The wearable sensors make the measurement process more convenient as well as feasible in a practical environment. However, the question remains to be answered about the validation of the wearable sensor-based measurement system in a real-world scenario. This paper proposes a study that includes an algorithmic approach based on collected data from the wearable accelerometers for the estimation of the gait characteristics and its validation using the Tinetti mobility test and 3D motion capture system. It also proposes a machine learning-based approach to classify the PD patients from the healthy older group (HOG) based on the estimated gait characteristics. The results show a good correlation between the proposed approach, the Tinetti mobility test, and the 3D motion capture system. It was found that decision tree classifiers outperformed other classifiers with a classification accuracy of 88.46%. The obtained results showed enough evidence about the proposed approach that could be suitable for assessing PD in a home-based free-living real-time environment.
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Quantitative Assessment of Head Tremor in Patients with Essential Tremor and Cervical Dystonia by Using Inertial Sensors. SENSORS 2019; 19:s19194246. [PMID: 31574913 PMCID: PMC6806605 DOI: 10.3390/s19194246] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Revised: 09/11/2019] [Accepted: 09/24/2019] [Indexed: 11/25/2022]
Abstract
Tremor is most common among the movement disabilities that affect older people, having a prevalence rate of 4.6% in the population older than 65 years. Despite this, distinguishing different types of tremors is clinically challenging, often leading to misdiagnosis. However, due to advances in microelectronics and wireless communication, it is now possible to easily monitor tremor in hospitals and even in home environments. In this paper, we propose an architecture of a system for remote health-care and one possible implementation of such system focused on head tremor monitoring. In particular, the aim of the study presented here was to test new tools for differentiating essential tremor from dystonic tremor. To that aim, we propose a number of temporal and spectral features that are calculated from measured gyroscope signals, and identify those that provide optimal differentiation between two groups. The mean signal amplitude feature results in sensitivity = 0.8537 and specificity = 0.8039 in distinguishing patients having cervical dystonia with or without tremor. In addition, mean signal amplitude was shown to be significantly higher in patients with essential tremor than in patients with cervical dystonia, whereas the mean peak frequency is not different between two groups.
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Belić M, Bobić V, Badža M, Šolaja N, Đurić-Jovičić M, Kostić VS. Artificial intelligence for assisting diagnostics and assessment of Parkinson's disease-A review. Clin Neurol Neurosurg 2019; 184:105442. [PMID: 31351213 DOI: 10.1016/j.clineuro.2019.105442] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Revised: 05/31/2019] [Accepted: 07/11/2019] [Indexed: 01/30/2023]
Abstract
Artificial intelligence, specifically machine learning, has found numerous applications in computer-aided diagnostics, monitoring and management of neurodegenerative movement disorders of parkinsonian type. These tasks are not trivial due to high inter-subject variability and similarity of clinical presentations of different neurodegenerative disorders in the early stages. This paper aims to give a comprehensive, high-level overview of applications of artificial intelligence through machine learning algorithms in kinematic analysis of movement disorders, specifically Parkinson's disease (PD). We surveyed papers published between January 2007 and January 2019, within online databases, including PubMed and Science Direct, with a focus on the most recently published studies. The search encompassed papers dealing with the implementation of machine learning algorithms for diagnosis and assessment of PD using data describing motion of upper and lower extremities. This systematic review presents an overview of 48 relevant studies published in the abovementioned period, which investigate the use of artificial intelligence for diagnostics, therapy assessment and progress prediction in PD based on body kinematics. Different machine learning algorithms showed promising results, particularly for early PD diagnostics. The investigated publications demonstrated the potentials of collecting data from affordable and globally available devices. However, to fully exploit artificial intelligence technologies in the future, more widespread collaboration is advised among medical institutions, clinicians and researchers, to facilitate aligning of data collection protocols, sharing and merging of data sets.
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Affiliation(s)
- Minja Belić
- Innovation Center, School of Electrical Engineering, University of Belgrade, Belgrade, Serbia.
| | - Vladislava Bobić
- Innovation Center, School of Electrical Engineering, University of Belgrade, Belgrade, Serbia; School of Electrical Engineering, University of Belgrade, Belgrade, Serbia.
| | - Milica Badža
- Innovation Center, School of Electrical Engineering, University of Belgrade, Belgrade, Serbia; School of Electrical Engineering, University of Belgrade, Belgrade, Serbia.
| | - Nikola Šolaja
- School of Electrical Engineering, University of Belgrade, Belgrade, Serbia.
| | - Milica Đurić-Jovičić
- Innovation Center, School of Electrical Engineering, University of Belgrade, Belgrade, Serbia.
| | - Vladimir S Kostić
- Clinic of Neurology, School of Medicine, University of Belgrade, Belgrade, Serbia.
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Wearable Accelerometer and sEMG-Based Upper Limb BSN for Tele-Rehabilitation. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9142795] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Assessment of human locomotion using wearable sensors is an efficient way of getting useful information about human health status, and determining human locomotion abnormalities. Wearable sensors do not only provide the opportunity to assess the behavior of patients as it happens in their daily life activities, but also provide quantitative, meaningful feedback data of patients to their therapists. This can pinpoint the cause of problems and help in maximizing their recovery rates. The popularity of using wearable sensors has received attention from a number of researchers from both the academic and industrial fields in the past few years. The different types of wearable sensors have given birth to the realization of a standard measurement model that can support different types of applications. Wireless body area networks (WBANs) are starting to replace traditional healthcare systems by enabling long-term monitoring of patients and tele-rehabilitation, especially those who suffer from chronic diseases. This paper investigates using wearable accelerometers and surface electromyography (EMG) in human locomotion monitoring for tele-rehabilitation. It proposes and investigates new positions for the proposed sensors, and compares the measured signals to similar techniques proposed in the literature. Realistic measurements show that the proposed positions of surface EMG sensors (on the forearm muscles) provide more reliable results in the classification of motion abnormality as compared to the sensor positions proposed in the literature (biceps muscles). Seven statistical features were extracted from accelerometer signals, and four time domain (TD) features are extracted from EMG signals. These features are used to construct six machine learning classifiers for automatic classification of Parkinson’s tremor. These models include; decision tree (DT), linear discriminant analysis analysis (LDA), k-nearest-neighbor (kNN), support vector machine (SVM), boosted tree and bagged tree classifiers. The performance of the applied classifiers is analyzed using accuracy, confusion matrix, and area under ROC (AUC) curve. The results are also compared to corresponding findings in the literature. The experimental results show that the highest classification accuracy is achieved when using the proposed measurement set and bagged tree classifier with a value of 99.6%.
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Chan PY, Mohd Ripin Z, Abdul Halim S, Kamarudin MI, Ng KS, Eow GB, Tan K, Cheah CF, Then L, Soong N, Hor JY, Yahya AS, Arifin WN, Tharakan J, Mustapha M. Biomechanical System Versus Observational Rating Scale for Parkinson's Disease Tremor Assessment. Sci Rep 2019; 9:8117. [PMID: 31148550 PMCID: PMC6544817 DOI: 10.1038/s41598-019-44142-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Accepted: 05/09/2019] [Indexed: 11/09/2022] Open
Abstract
There is a lack of evidence that either conventional observational rating scale or biomechanical system is a better tremor assessment tool. This work focuses on comparing a biomechanical system and the Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale in terms of test-retest reliability. The Parkinson's disease tremors were quantified by biomechanical system in joint angular displacement and predicted rating, as well as assessed by three raters using observational ratings. Qualitative comparisons of the validity and function are made also. The observational rating captures the overall severity of body parts, whereas the biomechanical system provides motion- and joint-specific tremor severity. The tremor readings of the biomechanical system were previously validated against encoders' readings and doctors' ratings; the observational ratings were validated with previous ratings on assessing the disease and combined motor symptoms rather than on tremor specifically. Analyses show that the predicted rating is significantly more reliable than the average clinical ratings by three raters. The comparison work removes some of the inconsistent impressions of the tools and serves as guideline for selecting a tool that can improve tremor assessment. Nevertheless, further work is required to consider more variabilities that influence the overall judgement.
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Affiliation(s)
- Ping Yi Chan
- The Vibration Laboratory, School of Mechanical Engineering, Universiti Sains Malaysia, Engineering Campus, 14300, Nibong Tebal, Penang, Malaysia.
| | - Zaidi Mohd Ripin
- The Vibration Laboratory, School of Mechanical Engineering, Universiti Sains Malaysia, Engineering Campus, 14300, Nibong Tebal, Penang, Malaysia
| | - Sanihah Abdul Halim
- Department of Medicine, School of Medical Sciences, Universiti Sains Malaysia, Health Campus, 16150, Kubang Kerian, Kelantan, Malaysia
| | - Muhammad Imran Kamarudin
- Department of Medicine, School of Medical Sciences, Universiti Sains Malaysia, Health Campus, 16150, Kubang Kerian, Kelantan, Malaysia
| | - Kwang Sheng Ng
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Health Campus, 16150, Kubang Kerian, Kelantan, Malaysia
| | - Gaik Bee Eow
- Department of Neurology, Penang General Hospital, Residensi Road, 10990, Georgetown, Penang, Malaysia
| | - Kenny Tan
- Department of Neurology, Penang General Hospital, Residensi Road, 10990, Georgetown, Penang, Malaysia
| | - Chun Fai Cheah
- Department of Neurology, Penang General Hospital, Residensi Road, 10990, Georgetown, Penang, Malaysia
| | - Linda Then
- Department of Neurology, Penang General Hospital, Residensi Road, 10990, Georgetown, Penang, Malaysia
| | - Nelson Soong
- Department of Internal Medicine, Penang General Hospital, Residensi Road, 10990, Georgetown, Penang, Malaysia
| | - Jyh Yung Hor
- Department of Neurology, Penang General Hospital, Residensi Road, 10990, Georgetown, Penang, Malaysia
| | - Ahmad Shukri Yahya
- School of Civil Engineering, Universiti Sains Malaysia, Engineering Campus, 14300, Nibong Tebal, Penang, Malaysia
| | - Wan Nor Arifin
- Unit of Biostatistics and Research Methodology, School of Medical Sciences, Universiti Sains Malaysia, Health Campus, 16150, Kubang Kerian, Kelantan, Malaysia
| | - John Tharakan
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Health Campus, 16150, Kubang Kerian, Kelantan, Malaysia
| | - Muzaimi Mustapha
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Health Campus, 16150, Kubang Kerian, Kelantan, Malaysia
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Contrast and Homogeneity Feature Analysis for Classifying Tremor Levels in Parkinson's Disease Patients. SENSORS 2019; 19:s19092072. [PMID: 31060214 PMCID: PMC6539600 DOI: 10.3390/s19092072] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/21/2019] [Revised: 03/12/2019] [Accepted: 04/02/2019] [Indexed: 11/23/2022]
Abstract
Early detection of different levels of tremors helps to obtain a more accurate diagnosis of Parkinson’s disease and to increase the therapy options for a better quality of life for patients. This work proposes a non-invasive strategy to measure the severity of tremors with the aim of diagnosing one of the first three levels of Parkinson’s disease by the Unified Parkinson’s Disease Rating Scale (UPDRS). A tremor being an involuntary motion that mainly appears in the hands; the dataset is acquired using a leap motion controller that measures 3D coordinates of each finger and the palmar region. Texture features are computed using sum and difference of histograms (SDH) to characterize the dataset, varying the window size; however, only the most fundamental elements are used in the classification stage. A machine learning classifier provides the final classification results of the tremor level. The effectiveness of our approach is obtained by a set of performance metrics, which are also used to show a comparison between different proposed designs.
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Merola A, Sturchio A, Hacker S, Serna S, Vizcarra JA, Marsili L, Fasano A, Espay AJ. Technology-based assessment of motor and nonmotor phenomena in Parkinson disease. Expert Rev Neurother 2018; 18:825-845. [PMID: 30269610 DOI: 10.1080/14737175.2018.1530593] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Abstract
INTRODUCTION The increasing development and availability of portable and wearable technologies is rapidly expanding the field of technology-based objective measures (TOMs) in neurological disorders, including Parkinson disease (PD). Substantial challenges remain in the recognition of disease phenomena relevant to patients and clinicians, as well as in the identification of the most appropriate devices to carry out these measurements. Areas covered: The authors systematically reviewed PubMed for studies employing technology as outcome measures in the assessment of PD-associated motor and nonmotor abnormalities. Expert commentary: TOMs minimize intra- and inter-rater variability in clinical assessments of motor and nonmotor phenomena in PD, improving the accuracy of clinical endpoints. Critical unmet needs for the integration of TOMs into clinical and research practice are the identification and validation of relevant endpoints for individual patients, the capture of motor and nonmotor activities from an ecologically valid environment, the integration of various sensor data into an open-access, common-language platforms, and the definition of a regulatory pathway for approval of TOMs. The current lack of multidomain, multisensor, smart technologies to measure in real time a wide scope of relevant changes remain a significant limitation for the integration of technology into the assessment of PD motor and nonmotor functional disability.
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Affiliation(s)
- Aristide Merola
- a James J and Joan A Gardner Family Center for Parkinson's Disease and Movement Disorders, Department of Neurology , University of Cincinnati , Cincinnati , OH , USA
| | - Andrea Sturchio
- a James J and Joan A Gardner Family Center for Parkinson's Disease and Movement Disorders, Department of Neurology , University of Cincinnati , Cincinnati , OH , USA
| | - Stephanie Hacker
- a James J and Joan A Gardner Family Center for Parkinson's Disease and Movement Disorders, Department of Neurology , University of Cincinnati , Cincinnati , OH , USA
| | - Santiago Serna
- a James J and Joan A Gardner Family Center for Parkinson's Disease and Movement Disorders, Department of Neurology , University of Cincinnati , Cincinnati , OH , USA
| | - Joaquin A Vizcarra
- a James J and Joan A Gardner Family Center for Parkinson's Disease and Movement Disorders, Department of Neurology , University of Cincinnati , Cincinnati , OH , USA
| | - Luca Marsili
- a James J and Joan A Gardner Family Center for Parkinson's Disease and Movement Disorders, Department of Neurology , University of Cincinnati , Cincinnati , OH , USA
| | - Alfonso Fasano
- b Edmond J. Safra Program in Parkinson's disease and the Morton and Gloria Shulman Movement Disorders Clinic , Toronto Western Hospital, University of Toronto; Krembil Brain Institute , Toronto , ON , Canada
| | - Alberto J Espay
- a James J and Joan A Gardner Family Center for Parkinson's Disease and Movement Disorders, Department of Neurology , University of Cincinnati , Cincinnati , OH , USA
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58
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Aich S, Pradhan PM, Park J, Sethi N, Vathsa VSS, Kim HC. A Validation Study of Freezing of Gait (FoG) Detection and Machine-Learning-Based FoG Prediction Using Estimated Gait Characteristics with a Wearable Accelerometer. SENSORS 2018; 18:s18103287. [PMID: 30274340 PMCID: PMC6210779 DOI: 10.3390/s18103287] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2018] [Revised: 09/23/2018] [Accepted: 09/26/2018] [Indexed: 01/29/2023]
Abstract
One of the most common symptoms observed among most of the Parkinson's disease patients that affects movement pattern and is also related to the risk of fall, is usually termed as "freezing of gait (FoG)". To allow systematic assessment of FoG, objective quantification of gait parameters and automatic detection of FoG are needed. This will help in personalizing the treatment. In this paper, the objectives of the study are (1) quantification of gait parameters in an objective manner by using the data collected from wearable accelerometers; (2) comparison of five estimated gait parameters from the proposed algorithm with their counterparts obtained from the 3D motion capture system in terms of mean error rate and Pearson's correlation coefficient (PCC); (3) automatic discrimination of FoG patients from no FoG patients using machine learning techniques. It was found that the five gait parameters have a high level of agreement with PCC ranging from 0.961 to 0.984. The mean error rate between the estimated gait parameters from accelerometer-based approach and 3D motion capture system was found to be less than 10%. The performances of the classifiers are compared on the basis of accuracy. The best result was accomplished with the SVM classifier with an accuracy of approximately 88%. The proposed approach shows enough evidence that makes it applicable in a real-life scenario where the wearable accelerometer-based system would be recommended to assess and monitor the FoG.
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Affiliation(s)
- Satyabrata Aich
- Department of Computer Engineering/Institute of Digital Anti-Aging Healthcare, Inje University, Gimhae 50834, Korea.
| | - Pyari Mohan Pradhan
- Department of Electronics and Communication Engineering, IIT Roorkee, Uttarakhand 247667, India.
| | - Jinse Park
- Department of Neurology, Haeundae Paik Hospital, Inje University, Busan 47392, Korea.
| | - Nitin Sethi
- Department of Electronics and Communication Engineering, IIT Roorkee, Uttarakhand 247667, India.
| | - Vemula Sai Sri Vathsa
- Department of Electronics and Communication Engineering, IIT Roorkee, Uttarakhand 247667, India.
| | - Hee-Cheol Kim
- Department of Computer Engineering/Institute of Digital Anti-Aging Healthcare, Inje University, Gimhae 50834, Korea.
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Ramdhani RA, Khojandi A, Shylo O, Kopell BH. Optimizing Clinical Assessments in Parkinson's Disease Through the Use of Wearable Sensors and Data Driven Modeling. Front Comput Neurosci 2018; 12:72. [PMID: 30254580 PMCID: PMC6141919 DOI: 10.3389/fncom.2018.00072] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2017] [Accepted: 08/13/2018] [Indexed: 11/17/2022] Open
Abstract
The emergence of motion sensors as a tool that provides objective motor performance data on individuals afflicted with Parkinson's disease offers an opportunity to expand the horizon of clinical care for this neurodegenerative condition. Subjective clinical scales and patient based motor diaries have limited clinometric properties and produce a glimpse rather than continuous real time perspective into motor disability. Furthermore, the expansion of machine learn algorithms is yielding novel classification and probabilistic clinical models that stand to change existing treatment paradigms, refine the application of advance therapeutics, and may facilitate the development and testing of disease modifying agents for this disease. We review the use of inertial sensors and machine learning algorithms in Parkinson's disease.
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Affiliation(s)
- Ritesh A Ramdhani
- Department of Neurology, School of Medicine, New York University, New York City, NY, United States
| | - Anahita Khojandi
- Department of Industrial and Systems Engineering, University of Tennessee, Knoxville, TN, United States
| | - Oleg Shylo
- Department of Industrial and Systems Engineering, University of Tennessee, Knoxville, TN, United States
| | - Brian H Kopell
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York City, NY, United States.,Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York City, NY, United States.,Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, NY, United States.,Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York City, NY, United States
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60
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Hou Z, Xiang J, Dong Y, Xue X, Xiong H, Yang B. Capturing Electrocardiogram Signals from Chairs by Multiple Capacitively Coupled Unipolar Electrodes. SENSORS (BASEL, SWITZERLAND) 2018; 18:E2835. [PMID: 30154303 PMCID: PMC6163948 DOI: 10.3390/s18092835] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Revised: 08/18/2018] [Accepted: 08/21/2018] [Indexed: 11/21/2022]
Abstract
A prototype of an electrocardiogram (ECG) signal acquisition system with multiple unipolar capacitively coupled electrodes is designed and experimentally tested. Capacitively coupled electrodes made of a standard printed circuit board (PCB) are used as the sensing electrodes. Different from the conventional measurement schematics, where one single lead ECG signal is acquired from a pair of sensing electrodes, the sensing electrodes in our approaches operate in a unipolar mode, i.e., the biopotential signals picked up by each sensing electrodes are amplified and sampled separately. Four unipolar electrodes are mounted on the backrest of a regular chair and therefore four channel of signals containing ECG information are sampled and processed. It is found that the qualities of ECG signal contained in the four channel are different from each other. In order to pick up the ECG signal, an index for quality evaluation, as well as for aggregation of multiple signals, is proposed based on phase space reconstruction. Experimental tests are carried out while subjects sitting on the chair and clothed. The results indicate that the ECG signals can be reliably obtained in such a unipolar way.
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Affiliation(s)
- Zhongjie Hou
- State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing 100084, China.
| | - Jinxi Xiang
- State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing 100084, China.
| | - Yonggui Dong
- State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing 100084, China.
| | - Xiaohui Xue
- State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing 100084, China.
| | - Hao Xiong
- State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing 100084, China.
| | - Bin Yang
- State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing 100084, China.
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Upadhya SS, Cheeran AN. PERFORMANCE COMPARISON OF REGRESSION TECHNIQUES IN PREDICTING PARKINSON DISEASE SEVERITY SCORE USING SPEECH FEATURES. BIOMEDICAL ENGINEERING: APPLICATIONS, BASIS AND COMMUNICATIONS 2018. [DOI: 10.4015/s1016237218500254] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
This paper gives a performance comparison in terms of Root mean square error (RMSE) of the six regression techniques used to predict the Parkinson disease severity score. People affected by Parkinson disease suffer various muscular impairments like gait, speech etc. The severity of the disease is generally assessed by the clinicians by observing the different muscular functions of the affected people or by performing scans of the brain. This paper focusses on predicting the disease severity using features of speech signal and performing regression on these features. The features used in the prediction are the phonation features extracted from voice samples of both Parkinson disease affected people and healthy people. The 14 phonation features extracted include the frequency variability features jitter and its other variants, the energy variability features shimmer and its other variants, the mean auto correlation of the pitch frequencies, harmonicity features harmonic to noise ratio and noise to harmonic ratio. The six regression techniques used to predict the severity score are the Linear, Stepwise, Lasso, Ridge regression, prediction using Neural network model and Classification and Regression trees (CART). The trained regression model is validated using the [Formula: see text]-fold cross-validation method with [Formula: see text] values three, five, seven and ten and also using the hold out validation model in which the hold out value is taken to be 0.3. The results obtained from the six regression techniques is then compared and it shows that the severity score prediction using Neural network model provides the least RMSE of 1.5 followed by 1.8 using the CART regression technique.
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Affiliation(s)
- Savitha S. Upadhya
- Electrical Engineering Department, Veermata Jijabai Technological Institute, Matunga, Mumbai, Maharashtra 400019, India
| | - A. N. Cheeran
- Electrical Engineering Department, Veermata Jijabai Technological Institute, Matunga, Mumbai, Maharashtra 400019, India
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Ramírez J, Rodriquez D, Qiao F, Warchall J, Rye J, Aklile E, Chiang ASC, Marin BC, Mercier PP, Cheng CK, Hutcheson KA, Shinn EH, Lipomi DJ. Metallic Nanoislands on Graphene for Monitoring Swallowing Activity in Head and Neck Cancer Patients. ACS NANO 2018; 12:5913-5922. [PMID: 29874030 PMCID: PMC6286678 DOI: 10.1021/acsnano.8b02133] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
There is a need to monitor patients with cancer of the head and neck postradiation therapy, as diminished swallowing activity can result in disuse atrophy and fibrosis of the swallowing muscles. This paper describes a flexible strain sensor comprising palladium nanoislands on single-layer graphene. These piezoresistive sensors were tested on 14 disease-free head and neck cancer patients with various levels of swallowing function: from nondysphagic to severely dysphagic. The patch-like devices detected differences in (1) the consistencies of food boluses when swallowed and (2) dysphagic and nondysphagic swallows. When surface electromyography (sEMG) is obtained simultaneously with strain data, it is also possible to differentiate swallowing vs nonswallowing events. The plots of resistance vs time are correlated to specific events recorded by video X-ray fluoroscopy. Finally, we developed a machine-learning algorithm to automate the identification of bolus type being swallowed by a healthy subject (86.4%. accuracy). The algorithm was also able to discriminate between swallows of the same bolus from either the healthy subject or a dysphagic patient (94.7% accuracy). Taken together, these results may lead to noninvasive and home-based systems for monitoring of swallowing function and improved quality of life.
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Affiliation(s)
- Julian Ramírez
- Department of NanoEngineering, University of California, San Diego, 9500 Gilman Drive, Mail Code 0448, La Jolla, CA 92093-0448
| | - Daniel Rodriquez
- Department of NanoEngineering, University of California, San Diego, 9500 Gilman Drive, Mail Code 0448, La Jolla, CA 92093-0448
| | - Fang Qiao
- Department of Computer Science and Engineering, University of California, San Diego, 9500 Gilman Drive, Mail Code 0404, La Jolla, CA 92093-0404
| | - Julian Warchall
- Department of Electrical and Computer Engineering, University of California, San Diego, 9500 Gilman Drive, Mail Code 0407, La Jolla, CA 92093-0407
| | - Jasmine Rye
- Department of NanoEngineering, University of California, San Diego, 9500 Gilman Drive, Mail Code 0448, La Jolla, CA 92093-0448
| | - Eden Aklile
- Department of NanoEngineering, University of California, San Diego, 9500 Gilman Drive, Mail Code 0448, La Jolla, CA 92093-0448
| | - Andrew S-C. Chiang
- Department of NanoEngineering, University of California, San Diego, 9500 Gilman Drive, Mail Code 0448, La Jolla, CA 92093-0448
| | - Brandon C. Marin
- Department of NanoEngineering, University of California, San Diego, 9500 Gilman Drive, Mail Code 0448, La Jolla, CA 92093-0448
| | - Patrick P. Mercier
- Department of Electrical and Computer Engineering, University of California, San Diego, 9500 Gilman Drive, Mail Code 0407, La Jolla, CA 92093-0407
| | - CK Cheng
- Department of Computer Science and Engineering, University of California, San Diego, 9500 Gilman Drive, Mail Code 0404, La Jolla, CA 92093-0404
| | - Katherine A. Hutcheson
- Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Unit 1445, 1515 Holcombe St, Houston, TX 77030-4009
| | - Eileen H. Shinn
- Department of Behavioral Sciences, The University of Texas MD Anderson Cancer Center, Unit 1330, 1155 Pressler St, Houston, TX 77230-1439
| | - Darren J. Lipomi
- Department of NanoEngineering, University of California, San Diego, 9500 Gilman Drive, Mail Code 0448, La Jolla, CA 92093-0448
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Abstract
Neurological disorders are the leading cause of global disability. However, for most people around the world, current neurological care is poor. In low-income countries, most individuals lack access to proper neurological care, and in high-income countries, distance and disability limit access. With the global proliferation of smartphones, teleneurology - the use of technology to provide neurological care and education remotely - has the potential to improve and increase access to care for billions of people. Telestroke has already fulfilled this promise, but teleneurology applications for chronic conditions are still in their infancy. Similarly, few studies have explored the capabilities of mobile technologies such as smartphones and wearable sensors, which can guide care by providing objective, frequent, real-world assessments of patients. In low-income settings, teleneurology can increase the capacity of local care systems through professional development, diagnostic support and consultative services. In high-income settings, teleneurology is likely to promote the expansion and migration of neurological care away from institutions, incorporate systems of asynchronous communication (such as e-mail), integrate clinicians with diverse skill sets and reach new populations. Inertia, outdated policies and social barriers - especially the digital divide - will slow this progress at considerable cost. However, a future increasingly will be possible in which neurological care can be accessed by anyone, anywhere. Here, we examine the emerging evidence regarding the benefits of teleneurology for chronic conditions, its role and risks in low-income countries and the promise of mobile technologies to measure disease status and deliver care. We conclude by discussing the future trends, barriers and timing for the adoption of teleneurology.
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Jeon H, Lee W, Park H, Lee HJ, Kim SK, Kim HB, Jeon B, Park KS. Correction: Automatic Classification of Tremor Severity in Parkinson's Disease Using a Wearable Device. Sensors 2017, 17, 2067. SENSORS 2017; 18:s18010033. [PMID: 29295543 PMCID: PMC5795920 DOI: 10.3390/s18010033] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 12/18/2017] [Revised: 12/18/2017] [Accepted: 12/22/2017] [Indexed: 11/16/2022]
Affiliation(s)
- Hyoseon Jeon
- The Interdisciplinary Program for Bioengineering, Seoul National University, Seoul 03080, Korea.
| | - Woongwoo Lee
- Department of Neurology and Movement Disorder Center, Seoul National University Hospital, Seoul 03080, Korea.
| | - Hyeyoung Park
- Department of Neurology and Movement Disorder Center, Seoul National University Hospital, Seoul 03080, Korea.
| | - Hong Ji Lee
- The Interdisciplinary Program for Bioengineering, Seoul National University, Seoul 03080, Korea.
| | - Sang Kyong Kim
- The Interdisciplinary Program for Bioengineering, Seoul National University, Seoul 03080, Korea.
| | - Han Byul Kim
- The Interdisciplinary Program for Bioengineering, Seoul National University, Seoul 03080, Korea.
| | - Beomseok Jeon
- Department of Neurology and Movement Disorder Center, Seoul National University Hospital, Seoul 03080, Korea.
| | - Kwang Suk Park
- Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul 03080, Korea.
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