Jaw Rotation in Dysarthria Measured With a Single Electromagnetic Articulography Sensor

Purpose This study evaluated a novel method for characterizing jaw rotation using orientation data from a single electromagnetic articulography sensor. This method was optimized for clinical application, and a preliminary examination of clinical feasibility and value was undertaken. Method The computational adequacy of the single-sensor orientation method was evaluated through comparisons of jaw-rotation histories calculated from dual-sensor positional data for 16 typical talkers. The clinical feasibility and potential value of single-sensor jaw rotation were assessed through comparisons of 7 talkers with dysarthria and 19 typical talkers in connected speech. Results The single-sensor orientation method allowed faster and safer participant preparation, required lower data-acquisition costs, and generated less high-frequency artifact than the dual-sensor positional approach. All talkers with dysarthria, regardless of severity, demonstrated jaw-rotation histories with more numerous changes in movement direction and reduced smoothness compared with typical talkers. Conclusions Results suggest that the single-sensor orientation method for calculating jaw rotation during speech is clinically feasible. Given the preliminary nature of this study and the small participant pool, the clinical value of such measures remains an open question. Further work must address the potential confound of reduced speaking rate on movement smoothness

운율 정보를 이용한 마비말장애 음성 자동 검출 및 평가

학위논문 (석사) -- 서울대학교 대학원 : 인문대학 언어학과, 2020. 8. Minhwa Chung.말장애는 신경계 또는 퇴행성 질환에서 가장 빨리 나타나는 증 상 중 하나이다. 마비말장애는 파킨슨병, 뇌성 마비, 근위축성 측삭 경화증, 다발성 경화증 환자 등 다양한 환자군에서 나타난다. 마비말장애는 조음기관 신경의 손상으로 부정확한 조음을 주요 특징으로 가지고, 운율에도 영향을 미치는 것으로 보고된다. 선행 연구에서는 운율 기반 측정치를 비장애 발화와 마비말장애 발화를 구별하는 것에 사용했다. 임상 현장에서는 마비말장애에 대한 운율 기반 분석이 마비말장애를 진단하거나 장애 양상에 따른 알맞은 치료법을 준비하는 것에 도움이 될 것이다. 따라서 마비말장애가 운율에 영향을 미치는 양상뿐만 아니라 마비말장애의 운율 특징을 긴밀하게 살펴보는 것이 필요하다. 구체 적으로, 운율이 어떤 측면에서 마비말장애에 영향을 받는지, 그리고 운율 애가 장애 정도에 따라 어떻게 다르게 나타나는지에 대한 분석이 필요하다. 본 논문은 음높이, 음질, 말속도, 리듬 등 운율을 다양한 측면에 서 살펴보고, 마비말장애 검출 및 평가에 사용하였다. 추출된 운율 특징들은 몇 가지 특징 선택 알고리즘을 통해 최적화되어 머신러닝 기반 분류기의 입력값으로 사용되었다. 분류기의 성능은 정확도, 정밀도, 재현율, F1-점수로 평가되었다. 또한, 본 논문은 장애 중증도(경도, 중등도, 심도)에 따라 운율 정보 사용의 유용성을 분석하였다. 마지막으로, 장애 발화 수집이 어려운 만큼, 본 연구는 교차 언어 분류기를 사용하였다. 한국어와 영어 장애 발화가 훈련 셋으로 사용되었으며, 테스트셋으로는 각 목표 언어만이 사용되었다. 실험 결과는 다음과 같이 세 가지를 시사한다. 첫째, 운율 정보 를 사용하는 것은 마비말장애 검출 및 평가에 도움이 된다. MFCC 만을 사용했을 때와 비교했을 때, 운율 정보를 함께 사용하는 것이 한국어와 영어 데이터셋 모두에서 도움이 되었다. 둘째, 운율 정보는 평가에 특히 유용하다. 영어의 경우 검출과 평가에서 각각 1.82%와 20.6%의 상대적 정확도 향상을 보였다. 한국어의 경우 검출에서는 향상을 보이지 않았지만, 평가에서는 13.6%의 상대적 향상이 나타났다. 셋째, 교차 언어 분류기는 단일 언어 분류기보다 향상된 결과를 보인다. 실험 결과 교차언어 분류기는 단일 언어 분류기와 비교했을 때 상대적으로 4.12% 높은 정확도를 보였다. 이것은 특정 운율 장애는 범언어적 특징을 가지며, 다른 언어 데이터를 포함시켜 데이터가 부족한 훈련 셋을 보완할 수 있 음을 시사한다.One of the earliest cues for neurological or degenerative disorders are speech impairments. Individuals with Parkinsons Disease, Cerebral Palsy, Amyotrophic lateral Sclerosis, Multiple Sclerosis among others are often diagnosed with dysarthria. Dysarthria is a group of speech disorders mainly affecting the articulatory muscles which eventually leads to severe misarticulation. However, impairments in the suprasegmental domain are also present and previous studies have shown that the prosodic patterns of speakers with dysarthria differ from the prosody of healthy speakers. In a clinical setting, a prosodic-based analysis of dysarthric speech can be helpful for diagnosing the presence of dysarthria. Therefore, there is a need to not only determine how the prosody of speech is affected by dysarthria, but also what aspects of prosody are more affected and how prosodic impairments change by the severity of dysarthria. In the current study, several prosodic features related to pitch, voice quality, rhythm and speech rate are used as features for detecting dysarthria in a given speech signal. A variety of feature selection methods are utilized to determine which set of features are optimal for accurate detection. After selecting an optimal set of prosodic features we use them as input to machine learning-based classifiers and assess the performance using the evaluation metrics: accuracy, precision, recall and F1-score. Furthermore, we examine the usefulness of prosodic measures for assessing different levels of severity (e.g. mild, moderate, severe). Finally, as collecting impaired speech data can be difficult, we also implement cross-language classifiers where both Korean and English data are used for training but only one language used for testing. Results suggest that in comparison to solely using Mel-frequency cepstral coefficients, including prosodic measurements can improve the accuracy of classifiers for both Korean and English datasets. In particular, large improvements were seen when assessing different severity levels. For English a relative accuracy improvement of 1.82% for detection and 20.6% for assessment was seen. The Korean dataset saw no improvements for detection but a relative improvement of 13.6% for assessment. The results from cross-language experiments showed a relative improvement of up to 4.12% in comparison to only using a single language during training. It was found that certain prosodic impairments such as pitch and duration may be language independent. Therefore, when training sets of individual languages are limited, they may be supplemented by including data from other languages.1. Introduction 1 1.1. Dysarthria 1 1.2. Impaired Speech Detection 3 1.3. Research Goals & Outline 6 2. Background Research 8 2.1. Prosodic Impairments 8 2.1.1. English 8 2.1.2. Korean 10 2.2. Machine Learning Approaches 12 3. Database 18 3.1. English-TORGO 20 3.2. Korean-QoLT 21 4. Methods 23 4.1. Prosodic Features 23 4.1.1. Pitch 23 4.1.2. Voice Quality 26 4.1.3. Speech Rate 29 4.1.3. Rhythm 30 4.2. Feature Selection 34 4.3. Classification Models 38 4.3.1. Random Forest 38 4.3.1. Support Vector Machine 40 4.3.1 Feed-Forward Neural Network 42 4.4. Mel-Frequency Cepstral Coefficients 43 5. Experiment 46 5.1. Model Parameters 47 5.2. Training Procedure 48 5.2.1. Dysarthria Detection 48 5.2.2. Severity Assessment 50 5.2.3. Cross-Language 51 6. Results 52 6.1. TORGO 52 6.1.1. Dysarthria Detection 52 6.1.2. Severity Assessment 56 6.2. QoLT 57 6.2.1. Dysarthria Detection 57 6.2.2. Severity Assessment 58 6.1. Cross-Language 59 7. Discussion 62 7.1. Linguistic Implications 62 7.2. Clinical Applications 65 8. Conclusion 67 References 69 Appendix 76 Abstract in Korean 79Maste

Gene Therapy for Parkinson\u27s Disease

Background: Parkinson’s disease (PD) is a neurodegenerative disorder of unknown cause. The characteristic motor impairments of PD including resting tremor, rigidity, slowed movement, decreased dexterity, small handwriting, flexed posture, gait disorder, and imbalance predominantly arise from the loss of neurons in the substantia nigra region of the midbrain that produce the neurotransmitter dopamine. Dopamine replacement therapy provides temporary relief of motor symptoms, but chronic use leads to serious side effects and cannot prevent disease progression. This systematic review will focus upon gene therapy as a possible treatment for PD. Methods: An exhaustive literature search was conducted in Medline, CINAHL, Web of Science, Google Scholar, and EBMRmultifile, using the search terms gene therapy and Parkinson’s disease in combination and alone as well as terms known to be synonymous. The search was limited to the English language, clinical trials and double-blind, randomized, controlled trials. Results: Two studies were reviewed based on the inclusion and exclusion criteria delineated in the methods section. Both studies were double-blind, randomized, controlled trials and utilized sham surgery for comparison. Marks et al showed adeno-associated type-2 vector (AAV2)-neurturin delivery in the putamen was not superior to sham surgery. LeWitt et al showed AAV2-glutamic acid decarboxylase (GAD) delivery in the subthalamic nucleus was superior to sham surgery. Conclusion: This systematic review shows gene therapy may prove to be a treatment option for patients with advanced Parkinson’s disease in the future. More research and development of gene therapy are needed

Detecting number processing and mental calculation in patients with disorders of consciousness using a hybrid brain-computer interface system

Background: For patients with disorders of consciousness such as coma, a vegetative state or a minimally conscious state, one challenge is to detect and assess the residual cognitive functions in their brains. Number processing and mental calculation are important brain functions but are difficult to detect in patients with disorders of consciousness using motor response-based clinical assessment scales such as the Coma Recovery Scale-Revised due to the patients' motor impairments and inability to provide sufficient motor responses for number- and calculation-based communication. Methods: In this study, we presented a hybrid brain-computer interface that combines P300 and steady state visual evoked potentials to detect number processing and mental calculation in Han Chinese patients with disorders of consciousness. Eleven patients with disorders of consciousness who were in a vegetative state (n = 6) or in a minimally conscious state (n = 3) or who emerged from a minimally conscious state (n = 2) participated in the brain-computer interface-based experiment. During the experiment, the patients with disorders of consciousness were instructed to perform three tasks, i.e., number recognition, number comparison, and mental calculation, including addition and subtraction. In each experimental trial, an arithmetic problem was first presented. Next, two number buttons, only one of which was the correct answer to the problem, flickered at different frequencies to evoke steady state visual evoked potentials, while the frames of the two buttons flashed in a random order to evoke P300 potentials. The patients needed to focus on the target number button (the correct answer). Finally, the brain-computer interface system detected P300 and steady state visual evoked potentials to determine the button to which the patients attended, further presenting the results as feedback. Results: Two of the six patients who were in a vegetative state, one of the three patients who were in a minimally conscious state, and the two patients that emerged from a minimally conscious state achieved accuracies significantly greater than the chance level. Furthermore, P300 potentials and steady state visual evoked potentials were observed in the electroencephalography signals from the five patients. Conclusions: Number processing and arithmetic abilities as well as command following were demonstrated in the five patients. Furthermore, our results suggested that through brain-computer interface systems, many cognitive experiments may be conducted in patients with disorders of consciousness, although they cannot provide sufficient behavioral responses. © 2015 Li et al

The role of pre-supplementary motor area in spatial vector transformation: evidence from Parkinson's disease

This thesis investigated the role of the supplementary motor area (SMA) in visuospatial processing using Parkinson’s disease (PD) patients as a model of pre-supplementary motor area (pre-SMA) dysfunction. The vector transformation hypothesis assumes that visuospatial transformation deficits in PD are a result of impairments in calculating vectors or co-ordinate remapping with a reference frame. These vector transformation processes were investigated in spatial normalisation during mental rotation and showed that PD patients demonstrate slower image normalisation rates indicative of a deficit compares with controls. It was then investigated how far these deficits extend to other vector transformation tasks such as abstract grid navigation. PD patients were less accurate than controls and these deficits were independent of spatial short term memory and serial processing suggesting that PD is associated with spatial transformation deficits. Comparisons of visual vector transformation and auditory vector transformation showed that PD patients were less accurate at visual vector transformation than auditory vector transformation suggesting that vector transformation processes may be more sensitive to the visual domain. The final study was a pilot study to investigate the feasibility of using a cognitive vector transformation task to remediate symptoms of bradykinesia in PD. Modest improvements in movement velocity following the vector transformation task but no significant change in movement velocity following a control task suggests that vector transformation can be used for therapeutic gain

The use of inertial measurement units for the determination of gait spatio-temporal parameters

The aim of this work was to develop a methodology whereby inertial measurement units (IMUs) could be used to obtain accurate and objective gait parameters within typical developed adults (TDA) and Parkinson’s disease (PD). The thesis comprised four studies, the first establishing the validity of the IMU method when measuring the vertical centre of mass (CoM) acceleration, velocity and position versus an optical motion capture system (OMCS) in TDA. The second study addressed the validity of the IMU and inverted pendulum model measurements within PD and also explored the inter-rater reliability of the measurement. In the third study the optimisation of the inverted pendulum model driven by IMU data was explored when comparing to standardised clinical tests within TDA and PD, and the fourth explored a novel phase plot analysis applied to CoM movement to explore gait in more detail. The validity study showed no significant difference for vertical acceleration and position between IMU and OMCS measurements within TDA. Vertical velocity however did show a significant difference, but the error was still less than 2.5%. ICCs for all three parameters ranged from 0.782 to 0.952, indicating an adequate test-retest reliability. Within PD there was no significant difference found for vertical CoM acceleration, velocity and position. ICCs for all three parameters ranged from 0.77 to 0.982. In addition, the reliability calculations found no difference for step time, stride length and walking speed for people with PD. Inter-rater reliability was found not to be different for the same parameters. The optimisation of the correction factor when using the inverted pendulum model showed no significant difference between TDA and PD. Furthermore the correction factor was found not to be related to walking speed. The fourth and final study found that phase plot analysis of variability could be performed on CoM vertical excursion. TDA and PD were shown to have, on average, different characteristics. This thesis demonstrated that CoM motion can be objectively measured within a clinical setting in people with PD by utilizing IMUs. Furthermore, in depth gait variability analysis can be performed by utilizing a phase plot method

Assessment of spontaneous cardiovascular oscillations in Parkinson's disease

Parkinson's disease (PD) has been reported to involve postganglionic sympathetic failure and a wide spectrum of autonomic dysfunctions including cardiovascular, sexual, bladder, gastrointestinal and sudo-motor abnormalities. While these symptoms may have a significant impact on daily activities, as well as quality of life, the evaluation of autonomic nervous system (ANS) dysfunctions relies on a large and expensive battery of autonomic tests only accessible in highly specialized laboratories. In this paper we aim to devise a comprehensive computational assessment of disease-related heartbeat dynamics based on instantaneous, time-varying estimates of spontaneous (resting state) cardiovascular oscillations in PD. To this end, we combine standard ANS-related heart rate variability (HRV) metrics with measures of instantaneous complexity (dominant Lyapunov exponent and entropy) and higher-order statistics (bispectra). Such measures are computed over 600-s recordings acquired at rest in 29 healthy subjects and 30 PD patients. The only significant group-wise differences were found in the variability of the dominant Lyapunov exponent. Also, the best PD vs. healthy controls classification performance (balanced accuracy: 73.47%) was achieved only when retaining the time-varying, non-stationary structure of the dynamical features, whereas classification performance dropped significantly (balanced accuracy: 61.91%) when excluding variability-related features. Additionally, both linear and nonlinear model features correlated with both clinical and neuropsychological assessments of the considered patient population. Our results demonstrate the added value and potential of instantaneous measures of heartbeat dynamics and its variability in characterizing PD-related disabilities in motor and cognitive domains

Automatic Assessment Of Parkinson’s Disease Using Spontaneous Speech

Parkinson's disease is a neurodegenerative disease with a range of symptoms, including speech impairments. These can be detected with digital signal processing, since speech signals carry paralinguistic information, which means information beyond linguistic information. In this work, Parkinson's disease is being recognized from speech signals using machine learning methods while following the steps of a typical research of paralinguistic speech processing. The main goal of this work is to evaluate how different feature extractions and machine learning models are capable of recognizing Parkinson's disease from spontaneous speech. The literature research part of this work presents the stages of a typical paralinguistic speech processing pipeline and evaluates related studies and research. Based on the related studies, people with Parkinson's disease have recognizable features in their speech signals which can be used to assess the disease. Additionally, multitude of feature sets and classification models have been applied in the studies. In the research of this work, for feature extraction MFCCs and eGeMAPS features are used to extract useful information from audio signals. The features work as an input to three different machine learning models used in this study: support vector machine, random forest, and convolutional neural network. These machine learning models are used to identify Parkinson's disease from the monologues of PC-GITA corpus. The data from PC-GITA used in this study consists of around a minute long spontaneous speeches from a hundred people of healthy speaker and people with diagnosed Parkinson’s disease. The results of this work were evaluated with a speaker-independent cross-validation method, in which each speaker acts as test data for the machine learning model and the remaining speakers as the training data. The final accuracy of the model was obtained by calculating the average accuracy of all folds of one hundred speakers. The results of this work indicate that Parkinson's disease can be recognized from speech using machine learning methods. Convolutional neural network produced the best accuracy for MFCCs features with 67.40% classification accuracy (Parkinson’s patient versus healthy talker), while random forest produced 75.00% accuracy for eGeMAPS features. The low accuracies are explained by the complexity of spontaneous speech and the chosen machine learning methods

PERIORAL BIOMECHANICS, KINEMATICS, AND ELECTROPHYSIOLOGY IN PARKINSON'S DISEASE

This investigation quantitatively characterized the orofacial biomechanics, labial kinematics, and associated electromyography (EMG) patterns in individuals with Parkinson's disease (PD) as a function of anti-PD medication state. Passive perioral stiffness, a clinical correlate of rigidity, was sampled using a face-referenced OroSTIFF system in 10 mildly diagnosed PD and 10 age/sex-matched control elderly. Labial movement amplitudes and velocities were evaluated using a 4-dimensional computerized motion capture system. Associated perioral EMG patterns were sampled to examine the characteristics of perioral muscles and compensatory muscular activation patterns during repetitive syllable productions. This study identified several trends that reflect various characteristics of perioral system differences between PD and control subjects: 1. The presence of high tonic EMG patterns after administration of dopaminergic treatment indicated an up-regulation of the central mechanism, which may serve to regulate orofacial postural control. 2. Multilevel regression modeling showed greater perioral stiffness in PD subjects, confirming the clinical correlate of rigidity in these patients. 3. Similar to the clinical symptoms in the upper and lower limb, a reduction of range of motion (hypokinesia) and velocity (bradykinesia) was evident in the PD orofacial system. Administration of dopaminergic treatment improved hypokinesia and bradykinesia. 4. A significant correlation was found between perioral stiffness and the range of labial movement, indicating these two symptoms may result in part from a common neural substrate. 5. As speech rate increased, PD speakers down-scaled movement amplitude and velocity compared to the control subjects, reflecting a compensatory mechanism to maintain target speech rates. 6. EMG from orbicularis oris inferior (OOIm) and depressor labii inferioris (DLIm) muscles revealed a limited range of muscle activation level in PD speakers, reflecting the underlying changes in motor unit firing behavior due to basal ganglia dysfunction. The results of this investigation provided a quantitative description of the perioral stiffness, labial kinematics, and EMG patterns in PD speakers. These findings indicate that perioral stiffness may provide clinicians a quantitative biomechanical correlate to medication response, movement aberrations, and EMG compensatory patterns in PD. The utilization of these objective assessments will be helpful in diagnosing, assessing, and monitoring the progression of PD to examine the efficacy of pharmacological, neurosurgical, and behavioral interventions