Summary Encephalitis is a severe inflammatory disorder of the brain with many possible causes and a complex differential diagnosis. Advances in autoimmune encephalitis research in the past 10 years have led to the identification of new syndromes and biomarkers that have transformed the diagnostic approach to these disorders. However, existing criteria for autoimmune encephalitis are too reliant on antibody testing and response to immunotherapy, which might delay the diagnosis. We reviewed the literature and gathered the experience of a team of experts with the aims of developing a practical, syndrome-based diagnostic approach to autoimmune encephalitis and providing guidelines to navigate through the differential diagnosis. Because autoantibody test results and response to therapy are not available at disease onset, we based the initial diagnostic approach on neurological assessment and conventional tests that are accessible to most clinicians. Through logical differential diagnosis, levels of evidence for autoimmune encephalitis (possible, probable, or definite) are achieved, which can lead to prompt immunotherapy.

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A clinical approach to diagnosis of autoimmune encephalitis

: This thesis applies neural network feature selection techniques to multivariate time series data to improve prediction of a target time series. Two approaches to feature selection are used. First, a subset enumeration method is used to determine which financial indicators are most useful for aiding in prediction of the S&P 500 futures daily price. The candidate indicators evaluated include RSI, Stochastics and several moving averages. Results indicate that the Stochastics and RSI indicators result in better prediction results than the moving averages. The second approach to feature selection is calculation of individual saliency metrics. A new decision boundary-based individual saliency metric, and a classifier independent saliency metric are developed and tested. Ruck's saliency metric, the decision boundary based saliency metric, and the classifier independent saliency metric are compared for a data set consisting of the RSI and Stochastics indicators as well as delayed closing price values. The decision based metric and the Ruck metric results are similar, but the classifier independent metric agrees with neither of the other metrics. The nine most salient features, determined by the decision boundary based metric, are used to train a neural network and the results are presented and compared to other published results. (AN)

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Nonlinear Time Series Analysis.

PREFACE The advantages which have been derived from the caution with which hypothetical statements are admitted, are in no instance more obvious than in those sciences which more particularly belong to the healing art. It therefore is necessary, that some conciliatory explanation should be offered for the present publication: in which, it is acknowledged, that mere conjecture takes the place of experiment; and, that analogy is the substitute for anatomical examination, the only sure foundation for pathological knowledge. When, however, the nature of the subject, and the circumstances under which it has been here taken up, are considered, it is hoped that the offering of the following pages to the attention of the medical public, will not be severely censured. The disease, respecting which the present inquiry is made, is of a nature highly afflictive. Notwithstanding which, it has not yet obtained a place in the classification of nosologists; some have regarded its characteristic symptoms as distinct and different diseases, and others have given its name to diseases differing essentially from it; whilst the unhappy sufferer has considered it as an evil, from the domination of which he had no prospect of escape. The disease is of long duration: to connect, therefore, the symptoms which occur in its later stages with those which mark its commencement, requires a continuance of observation of the same case, or at least a correct history of its symptoms, even for several years. Of both these advantages the writer has had the opportunities of availing himself, and has hence been led particularly to observe several other cases in which the disease existed in different stages of its progress. By these repeated observations, he hoped that he had been led to a probable conjecture as to the nature of the malady, and that analogy had suggested such means as might be productive of relief, and perhaps even of cure, if employed before the disease had been too long established. He therefore considered it to be a duty to submit his opinions to the examination of others, even in their present state of immaturity and imperfection. To delay their publication did not, indeed, appear to be warrantable. The disease had escaped particular notice; and the task of ascertaining its nature and cause by anatomical investigation, did not seem likely to be taken up by those who, from their abilities and opportunities, were most likely to accomplish it. That these friends to humanity and medical science, who have already unveiled to us many of the morbid processes by which health and life is abridged, might be excited to extend their researches to this malady, was much desired; and it was hoped, that this might be procured by the publication of these remarks. Should the necessary information be thus obtained, the writer will repine at no censurewhich the precipitate publication of mere conjectural suggestions may incur: but shall think himself fully rewarded by having excited the attention of those, who may point out the most appropriate means of relieving a tedious and most distressing malady.

An Essay on the Shaking Palsy

Tracking Parkinson's disease (PD) symptom progression often uses the unified Parkinson's disease rating scale (UPDRS) that requires the patient's presence in clinic, and time-consuming physical examinations by trained medical staff. Thus, symptom monitoring is costly and logistically inconvenient for patient and clinical staff alike, also hindering recruitment for future large-scale clinical trials. Here, for the first time, we demonstrate rapid, remote replication of UPDRS assessment with clinically useful accuracy (about 7.5 UPDRS points difference from the clinicians' estimates), using only simple, self-administered, and noninvasive speech tests. We characterize speech with signal processing algorithms, extracting clinically useful features of average PD progression. Subsequently, we select the most parsimonious model with a robust feature selection algorithm, and statistically map the selected subset of features to UPDRS using linear and nonlinear regression techniques that include classical least squares and nonparametric classification and regression trees. We verify our findings on the largest database of PD speech in existence (~6000 recordings from 42 PD patients, recruited to a six-month, multicenter trial). These findings support the feasibility of frequent, remote, and accurate UPDRS tracking. This technology could play a key part in telemonitoring frameworks that enable large-scale clinical trials into novel PD treatments.

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Accurate Telemonitoring of Parkinson's Disease Progression by Noninvasive Speech Tests

An assessment of vocal impairment is presented for separating healthy people from persons with early untreated Parkinson’s disease (PD). This study’s main purpose was to (a) determine whether voice and speech disorder are present from early stages of PD before starting dopaminergic pharmacotherapy, (b) ascertain the specific characteristics of the PD-related vocal impairment, (c) identify PD-related acoustic signatures for the major part of traditional clinically used measurement methods with respect to their automatic assessment, and (d) design new automatic measurement methods of articulation. The varied speech data were collected from 46 Czech native speakers, 23 with PD. Subsequently, 19 representative measurements were pre-selected, and Wald sequential analysis was then applied to assess the efficiency of each measure and the extent of vocal impairment of each subject. It was found that measurement of the fundamental frequency variations applied to two selected tasks was the best method for separating healthy from PD subjects. On the basis of objective acoustic measures, statistical decision-making theory, and validation from practicing speech therapists, it has been demonstrated that 78% of early untreated PD subjects indicate some form of vocal impairment. The speech defects thus uncovered differ individually in various characteristics including phonation, articulation, and prosody.

https://www.utdallas.edu/~hynek/citing_papers/Rusz_Quantitative%20acoustic%20measurements%20for%20characterization%20of%20speech%20and%20voice%20disorders%20in%20early%20untreated%20Parkinson's%20disease.pdf

Quantitative acoustic measurements for characterization of speech and voice disorders in early untreated Parkinson’s disease

Vowel Articulation in Parkinson's Disease

Articulatory rate and pause time in a standardized reading task in Parkinson's disease (PD) patients in correlation to disease duration and severity as compared to healthy controls were analyzed. In 121 PD patients and 70 healthy controls, an acoustical analysis was performed on the first and last sentence of a standardized 170-syllabic text, using a commercial audio software. Articulatory rate and speech to pause ratios were calculated by measuring the length of each syllable and each pause both at the end of words and within polysyllabic words. No significant difference in overall articulatory rate was found between PD patients and controls. Both groups showed an accelerated speech rate in the last sentence compared to the first; however, PD patients had a higher speech acceleration than did controls. PD patients exhibited a significantly reduced percental pause duration in relation to total speech time in the first sentence and a reduced percental pause time within polysyllabic words. PD patients made significantly less but longer pauses at the end of words and less pauses within polysyllabic words. UPDRS III showed an inverse relation to number and rate of intraword pauses, and disease duration was negatively correlated with articulatory rate. The characteristics of parkinsonian speech feature was not only a stronger acceleration of articulation rate in the course of speaking but also a significant reduction of the total numbers of pauses, indicating an impaired speech rhythm and timing organization.

Speech rate and rhythm in Parkinson's disease.

Parkinsonian speech or hypokinetic dysarthria results from a multidimensional impairment of phonation, articulation, and prosody. Although the dysprosody in Parkinson's disease (PD) is well described (alterations in speech rate and pause time, speech intensity and pitch variation), little is known about alterations of these single prosodic parameters over a longer time course. The objective of this study is to analyze changes of speech rate and pitch variation in patients with PD over time and to compare these findings with healthy controls. Patients with PD (N = 50; 27 male and 23 female) and n = 50 age-matched healthy controls (25 male, 25 female) were tested and retested after at least 7 months (mean: 25.02; median: 21; SD: 17.44; range: 7–79 months). In the PD group, motor impairment according to UPDRS motor score was similar at first and second visit. The participants had to accomplish a standardized four sentence reading task. The acoustical analysis was performed using a standard head-worn microphone for voice recordings and commercial audio software (WaveLab®). For the determination of intonation based upon fundamental frequency (F0) variation, we used a computer analysis program (Praat®). Articulatory velocity was determined by measurement of syllable rate and pause ratios. In the PD group, total speech rate (syllables per second related to total speech time/TSR) and net speech rate declined from first to second examination, especially in the male patients, but showed no significant differences to the control group. The course of pitch variation revealed some gender particularities. Whereas female patients' pitch variability declined over time, male patients' intonation variability remained relatively stable. F0 variation in male and female patients with PD were significantly reduced compared with the control group in the first examination and the follow up as well. Progression of prosodic impairment over time showed no correlation to disease duration or UPDRS motor score. Some aspects of dysprosody in PD show characteristic changes over time, but show no clear correlation with general motor impairment as assessed by UPDRS motor score. Therefore, we suspect that the underlying mechanism could be independent from dopaminergic deficits. © 2008 Movement Disorder Society

Progression of dysprosody in Parkinson's disease over time--a longitudinal study.

The aim of this study is the analysis of continuous speech signals of people with Parkinson's disease (PD) considering recordings in different languages (Spanish, German, and Czech). A method for the characterization of the speech signals, based on the automatic segmentation of utterances into voiced and unvoiced frames, is addressed here. The energy content of the unvoiced sounds is modeled using 12 Mel-frequency cepstral coefficients and 25 bands scaled according to the Bark scale. Four speech tasks comprising isolated words, rapid repetition of the syllables /pa/-/ta/-/ka/, sentences, and read texts are evaluated. The method proves to be more accurate than classical approaches in the automatic classification of speech of people with PD and healthy controls. The accuracies range from 85% to 99% depending on the language and the speech task. Cross-language experiments are also performed confirming the robustness and generalization capability of the method, with accuracies ranging from 60% to 99%. This work comprises a step forward for the development of computer aided tools for the automatic assessment of dysarthric speech signals in multiple languages.

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Automatic detection of Parkinson's disease in running speech spoken in three different languages.

Purpose 
The aim of the current study was to survey if vowel articulation in speakers with Parkinson's disease (PD) shows specific changes in the course of the disease.


Method 
67 patients with PD (42 male) and 40 healthy speakers (20 male) were tested and retested after an average time interval of 34 months. Participants had to read a given text as source for subsequent calculation of the triangular vowel space area (tVSA) and vowel articulation index (VAI). Measurement of tVSA and VAI were based upon analysis of the first and second formant of the vowels /α/, /i/and /u/ extracted from defined words within the text.


Results 
At first visit, VAI values were reduced in male and female PD patients as compared to the control group, and showed a further decrease at the second visit. Only in female Parkinsonian speakers, VAI was correlated to overall speech impairment based upon perceptual impression. VAI and tVSA were correlated to gait impairment, but no correlations were seen between VAI and global motor impairment or overall disease duration. tVSA showed a similar reduction in the PD as compared to the control group and was also found to further decline between first and second examination in female, but not in male speakers with PD.


Conclusions 
Measurement of VAI seems to be superior to tVSA in the description of impaired vowel articulation and its further decline in the course of the disease in PD. Since impairment of vowel articulation was found to be independent from global motor function but correlated to gait dysfunction, measurement of vowel articulation might have a potential to serve as a marker of axial disease progression.

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Sabine Skodda

Papers

Vowel Articulation in Parkinson's Disease

Speech rate and rhythm in Parkinson's disease.

Progression of dysprosody in Parkinson's disease over time--a longitudinal study.

Automatic detection of Parkinson's disease in running speech spoken in three different languages.

Impairment of Vowel Articulation as a Possible Marker of Disease Progression in Parkinson's Disease