MACHINE LEARNING USING SPEECH UTTERANCES FOR PARKINSON DISEASE DETECTION

Pathophysiological recordings of patients measured from various testing methods are frequently used in the medical field for determining symptoms as well as for probability prediction for selected diseases. There are numerous symptoms among the Parkinson’s disease (PD) population, however changes in speech and articulation – is potentially the most significant biomarker. This article is focused on PD diagnosis classification based on their speech signals using pattern recognition methods (AdaBoost, Bagged trees, Quadratic SVM and k-NN). The dataset investigated in the article consists of 30 PD and 30 HC individuals’ voice measurements, with each individual being represented with 2 recordings within the dataset. Training signals for PD and HC underwent an extraction of relatively well-discriminating features relating to energy and spectral speech properties. Model implementations included a 5-fold cross validation. The accuracy of the values obtained employing the models was calculated using the confusion matrix. The average value of the overall accuracy = 82.3 % and averaged AUC = 0.88 (min. AUC = 0.86) on the available data

ANALYSIS OF NEURAL ACTIVITY OF THE HUMAN BASAL GANGLIA IN DYSTONIA: A REVIEW

Deep brain stimulation of the globus pallidus internus is an efective symptomatic treatment for pharmacoresistant dystonic syndromes, where pathophysiological mechanisms of action are not yet fully understood. The aim of this review article is to provide an overview of the state-of-the-art approaches for processing of microelectrode recordings in dystonia; in order to define biomarkers to identify patients who will benefit from the clinical deep brain stimulation. For this purpose, the essential elements of microelectrode processing are examined. Next, we investigate a real example of spike sorting processing in this field. Herein, we describe baseline elements of microrecordings processing including data collection, preprocessing phase, features computation, spike detection and sorting and finally, advanced spike train data analysis. This study will help readers acquire the necessary information about these elements and their associated techniques. Thus, this study is supposed to assist during identification and proposal of interesting clinical hypotheses in the field of single unit neuronal recordings in dystonia

MEDIAN METHOD FOR DETERMINING CORTICAL BRAIN ACTIVITY IN A NEAR INFRARED SPECTROSCOPY IMAGE

Near-InfraRed-Spectroscopy (NIRS) is a neuroimaging method of brain cortical activity using low-energy optical radiation to detect local changes in (de)oxyhaemoglobin concentration. A methodology consisting of a raw signal pre-processing phase, followed by statistical analysis based on a general linear model, is currently being used to determine signal activity. The aim of this research is to define the median modification of the standard method usually used for the estimation of cortical activity from the NIRS signal and to verify its applicability in measuring motor tasks for patients with Parkinson's disease. Individual examinations were conducted in 10 cycles, during which finger tapping, and rest phases were alternating. Changes in oxyhaemoglobin concentration were calculated from the native NIRS signal using the modified Lambert-Beer equation. The signals were filtered in the 0.015–0.3 Hz band and fitted by the physiological response function of the brain tissue for each finger tapping cycle separately. The median value from the 10 cycles was then computed. Activity values obtained in individual subjects have been used in Brain Mapping visualizations. These describe motor task patterns during the ON and OFF deep brain stimulation of the subthalamic nucleus in Parkinson's disease, which demonstrates activation in accordance with the current state of knowledge in functional imaging

Evaluating the Performance of wav2vec Embedding for Parkinson's Disease Detection

Speech is one of the most serious manifestations of Parkinson's disease (PD). Sophisticated language/speech models have already demonstrated impressive performance on a variety of tasks, including classification. By analysing large amounts of data from a given setting, these models can identify patterns that would be difficult for clinicians to detect. We focus on evaluating the performance of a large self-supervised speech representation model, wav2vec, for PD classification. Based on the computed wav2vec embedding for each available speech signal, we calculated two sets of 512 derived features, wav2vec-sum and wav2vec-mean. Unlike traditional signal processing methods, this approach can learn a suitable representation of the signal directly from the data without requiring manual or hand-crafted feature extraction. Using an ensemble random forest classifier, we evaluated the embedding-based features on three different healthy vs. PD datasets (participants rhythmically repeat syllables /pa/, Italian dataset and English dataset). The obtained results showed that the wav2vec signal representation was accurate, with a minimum area under the receiver operating characteristic curve (AUROC) of 0.77 for the /pa/ task and the best AUROC of 0.98 for the Italian speech classification. The findings highlight the potential of the generalisability of the wav2vec features and the performance of these features in the cross-database scenarios

Identification of Microrecording Artifacts with Wavelet Analysis and Convolutional Neural Network: An Image Recognition Approach

Deep brain stimulation (DBS) is an internationally accepted form of treatment option for selected patients with Parkinson’s disease and dystonia. Intraoperative extracellular microelectrode recordings (MER) are considered as the standard electrophysiological method for the precise positioning of the DBS electrode into the target brain structure. Pre-processing of MERs is a key phase in clinical analysis, with intraoperative microelectrode recordings being prone to several artifact groups (up to 25 %). The aim of this methodological article is to provide a convolutional neural network (CNN) processing pipeline for the detection of artifacts in an MER. We applied continuous wavelet transform (CWT) to generate an over-complete time–frequency representation. We demonstrated that when attempting to find artifacts in an MER, the new CNN + CWT provides a high level of accuracy (ACC = 88.1 %), identifies individual classes of artifacts (ACC = 75.3 %) and also offers artifact time onset detail, which can lead to a reduction in false positives/negatives. In summary, the presented methodology is capable of identifying and removing various artifacts in a comprehensive database of MER and represents a substantial improvement over the existing methodology. We believe that this approach will assist in the proposal of interesting clinical hypotheses and will have neurologically relevant effects

Pokroky v analýze heterogenních neuroinformatických dat

V současné době roste počet dat získaných při měření pa-to/fyziologických procesů mozku. Aplikací analytických metod jsou z dat zís-kávány nové poznatky o mechanizmech neurologických onemocnění. Cílem výzkumu je dosažení pokroku v analýze různých typů neuroinformatických zá-znamů (jednotkové mikroelektrodové neuronové aktivity, transkraniální magne-tické stimulace a blízké infračervené spektroskopie) za účelem objektivní iden-tifikace významných biomarkerů metodami strojového učení. Do studie byla zahrnuta data z Neurologické kliniky 1. LF a VFN UK v Praze. V průběhu ře-šení výzkumu byly vytvořeny skripty pro ukládání, předzpracování a analýzu 3 typů výše zmíněných neuroinformatických datových zdrojů. Pokroky v analýze budou využity pro hodnocení naměřených dat a testování hypotéz s přínosem pro klinickou neurologickou praxi

Clinical and genetic characteristics of late-onset Huntington's disease

Background: The frequency of late-onset Huntington's disease (>59 years) is assumed to be low and the clinical course milder. However, previous literature on late-onset disease is scarce and inconclusive. Objective: Our aim is to study clinical characteristics of late-onset compared to common-onset HD patients in a large cohort of HD patients from the Registry database. Methods: Participants with late- and common-onset (30–50 years)were compared for first clinical symptoms, disease progression, CAG repeat size and family history. Participants with a missing CAG repeat size, a repeat size of ≤35 or a UHDRS motor score of ≤5 were excluded. Results: Of 6007 eligible participants, 687 had late-onset (11.4%) and 3216 (53.5%) common-onset HD. Late-onset (n = 577) had significantly more gait and balance problems as first symptom compared to common-onset (n = 2408) (P <.001). Overall motor and cognitive performance (P <.001) were worse, however only disease motor progression was slower (coefficient, −0.58; SE 0.16; P <.001) compared to the common-onset group. Repeat size was significantly lower in the late-onset (n = 40.8; SD 1.6) compared to common-onset (n = 44.4; SD 2.8) (P <.001). Fewer late-onset patients (n = 451) had a positive family history compared to common-onset (n = 2940) (P <.001). Conclusions: Late-onset patients present more frequently with gait and balance problems as first symptom, and disease progression is not milder compared to common-onset HD patients apart from motor progression. The family history is likely to be negative, which might make diagnosing HD more difficult in this population. However, the balance and gait problems might be helpful in diagnosing HD in elderly patients