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1

Comprehensive machine learning and deep learning approaches for Parkinson's disease classification and severity assessment

Majdoubi Oumaima, Benba Achraf, Hammouch Ahmed

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska

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2023

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T. 13, nr 4

15--20

EN

In this study, we aimed to adopt a comprehensive approach to categorize and assess the severity of Parkinson's disease by leveraging techniques from both machine learning and deep learning. We thoroughly evaluated the effectiveness of various models, including XGBoost, Random Forest, Multi-Layer Perceptron (MLP), and Recurrent Neural Network (RNN), utilizing classification metrics. We generated detailed reports to facilitate a comprehensive comparative analysis of these models. Notably, XGBoost demonstrated the highest precision at 97.4%. Additionally, we took a step further by developing a Gated Recurrent Unit (GRU) model with the purpose of combining predictions from alternative models. We assessed its ability to predict the severity of the ailment. To quantify the precision levels of the models in disease classification, we calculated severity percentages. Furthermore, we created a Receiver Operating Characteristic (ROC) curve for the GRU model, simplifying the evaluation of its capability to distinguish among various severity levels. This comprehensive approach contributes to a more accurate and detailed understanding of Parkinson's disease severity assessment.

PL

W tym badaniu naszym celem było przyjęcie kompleksowego podejścia do kategoryzacji i oceny ciężkości choroby Parkinsona poprzez wykorzystanie technik zarówno uczenia maszynowego, jak i głębokiego uczenia. Dokładnie oceniliśmy skuteczność różnych modeli, w tym XGBoost, Random Forest, Multi-Layer Perceptron (MLP) i Recurrent Neural Network (RNN), wykorzystując wskaźniki klasyfikacji. Wygenerowaliśmy szczegółowe raporty, aby ułatwić kompleksową analizę porównawczą tych modeli. Warto zauważyć, że XGBoost wykazał najwyższą precyzję na poziomie 97,4%. Ponadto poszliśmy o krok dalej, opracowując model Gated Recurrent Unit (GRU) w celu połączenia przewidywań z alternatywnych modeli. Oceniliśmy jego zdolność do przewidywania nasilenia dolegliwości. Aby określić ilościowo poziomy dokładności modeli w klasyfikacji chorób, obliczyliśmy wartości procentowe nasilenia. Ponadto stworzyliśmy krzywą charakterystyki operacyjnej odbiornika (ROC) dla modelu GRU, upraszczając ocenę jego zdolności do rozróżniania różnych poziomów nasilenia. To kompleksowe podejście przyczynia się do dokładniejszego i bardziej szczegółowego zrozumienia oceny ciężkości choroby Parkinsona.

2

CNN and LSTM for the classification of parkinson's disease based on the GTCC and MFCC

Boualoulou Nouhaila, Drissi Taoufiq Belhoussine, Nsiri Benayad

Applied Computer Science

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2023

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Vol. 19, no 2

1--24

EN

Parkinson's disease is a recognizable clinical syndrome with a variety of causes and clinical presentations; it represents a rapidly growing neurodegenerative disorder. Since about 90 percent of Parkinson's disease sufferers have some form of early speech impairment, recent studies on tele diagnosis of Parkinson's disease have focused on the recognition of voice impairments from vowel phonations or the subjects' discourse. This paper presents a new approach for Parkinson's disease detection from speech sounds that are based on CNN and LSTM and uses two categories of characteristics. These are Mel Frequency Cepstral Coefficients (MFCC) and Gammatone Cepstral Coefficients (GTCC) obtained from noise-removed speech signals with comparative EMD-DWT and DWT-EMD analysis. The proposed model is divided into three stages. In the first step, noise is removed from the signals using the EMD-DWT and DWT-EMD methods. In the second step, the GTCC and MFCC are extracted from the enhanced audio signals. The classification process is carried out in the third step by feeding these features into the LSTM and CNN models, which are designed to define sequential information from the extracted features. The experiments are performed using PC-GITA and Sakar datasets and 10-fold cross validation method, the highest classification accuracy for the Sakar dataset reached 100% for both EMD-DWT-GTCC-CNN and DWT-EMD-GTCC-CNN, and for the PC-GITA dataset, the accuracy is reached 100% for EMD-DWT-GTCC-CNN and 96.55% for DWT-EMD-GTCC-CNN. The results of this study indicate that the characteristics of GTCC are more appropriate and accurate for the assessment of PD than MFCC.

3

Parametric analysis of pilot voice signals in Parkinson’s disease diagnostics

Majda-Zdancewicz Ewelina, Potulska-Chromik Anna, Nojszewska Monika, Kostera-Pruszczyk Anna

Journal of Automation, Electronics and Electrical Engineering

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2022

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Vol. 4, No. 1

21--28

EN

Parkinson's disease (PD) is a neurodegenerative disease of the central nervous system (CNS) characterized by the progressive loss of dopaminergic neurons in the substantia nigra. The article describes an analysis of pilot voice signal analysis in Parkinson's disease diagnostics. Frequency domain signal analysis was mainly used to assess the state of a patient's voice apparatus in order to support PD diagnostics. The recordings covered uttering the “a” sound at least twice with extended phonation. The research utilized real recordings acquired in the Department of Neurology at the Medical University of Warsaw, Poland. Spectral speech signal coefficients may be determined based on different defined frequency scales. The authors used four frequency scales: linear, Mel, Bark and ERB . Spectral descriptors have been defined for each scales which are widely used in machine and deep learning applications, and perceptual analysis. The usefulness of extracted features was assessed taking into account various methods. The discriminatory ability of individual coefficients was evaluated using the Fisher coefficient and LDA technique.. The results of numerical experiments have shown different efficiencies of the proposed descriptors using different frequencies scales.

PL

Choroba Parkinsona (PD) jest neurodegeneracyjną chorobą ośrodkowego układu nerwowego charakteryzującą się postępującą utratą neuronów dopaminergicznych w istocie czarnej. W artykule opisano analizę rejestracji pilotażowych sygnałów głosu w diagnostyce choroby Parkinsona. Rejestracji podlegało co najmniej dwukrotnie wypowiadanie głoski "a” o przedłużonej fonacji. Do badań wykorzystano nagrania zarejestrowane w Katedrze i Klinice Neurologii Warszawskiego Uniwersytetu Medycznego w Warszawie. Do oceny stanu aparatu głosu pacjenta celem wsparcia diagnostyki choroby Parkinsona wykorzystano w głównej mierze analizę sygnału w dziedzinie częstotliwości. Autorzy zastosowali cztery skale częstości: liniową, skalę typu Mel, skalę typu Bark oraz skalę typu ERB. Dla każdej z tych skali zdefiniowali deskryptory spektralne szeroko stosowane w aplikacjach uczenia maszynowego i głębokiego uczenia się oraz w analizie percepcyjnej. Ocena przydatności wyekstrahowanych cech została zrealizowana z uwzględnieniem różnych metod. Wykorzystano metodą oceny jakości cech przy użyciu współczynnika istotności Fischera oraz analizę LDA. Wyniki eksperymentów numerycznych wykazały różne wydajności proponowanych deskryptorów przy użyciu różnych skal częstości.

4

Parkinson disease detection using energy direction features based on EMD from voice signal

Zhang Tao, Zhang Yajuan, Sun Hao, Shan Haoran

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 1

127--141

EN

Voice disorders are one of the incipient symptoms of Parkinson's disease (PD). Recent studies have shown that approximately 90% of PD patients suffer from vocal disorders. Therefore, it is significant to extract pathological information on the voice signals to detect PD. In this paper, a feature, named energy direction features based on empirical mode decomposition (EDF-EMD), is proposed to show the different characteristics of voice signals between PD patients and healthy subjects. Firstly, the intrinsic mode functions (IMFs) were obtained through the decomposition of voice signals by EMD. Then, the EDF is obtained by calculating the directional derivatives of the energy spectrum of each IMFs. Finally, the performance of the proposed feature is verified on two different datasets: dataset-Sakar and dataset-CPPDD. The proposed approach shows the best average resulting accuracy of 96.54% on dataset-Sakar and 92.59% on dataset-CPPDD. The results demonstrate that the method proposed in this paper is promising in the field of PD detection.

5

Detection of Parkinson’s disease using automated tunable Q wavelet transform technique with EEG signals

Khare Smith K., Bajaj Varun, Acharya U. Rajendra

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 2

679--689

EN

Deep brain simulations play an important role to study physiological and neuronal behavior during Parkinson’s disease (PD). Electroencephalogram (EEG) signals may faithfully represent the changes that occur during PD in the brain. But manual analysis of EEG signals is tedious, and time consuming as these signals are complex, non-linear, and non-stationary nature. Therefore EEG signals are required to decompose into multiple subbands (SBs) to get detailed and representative information from it. Experimental selection of basis function for the decomposition may cause system degradation due to information loss and an increased number of misclassification. To address this, an automated tunable Q wavelet transform (A-TQWT) is proposed for automatic decomposition. A-TQWT extracts representative SBs for analysis and provides better reconstruction for the synthesis of EEG signals by automatically selecting the tuning parameters. Five features are extracted from the SBs and classified different machine learning techniques. EEG dataset of 16 healthy controls (HC) and 15 PD (ON and OFF medication) subjects obtained from ”openneuro” is used to develop the automated model. We have aimed to develop an automated model that effectively classify HC subjects from PD patients with and without medication. The proposed method yielded an accuracy of 96.13% and 97.65% while the area under the curve of 97% and 98.56% for the classification of HC vs PD OFF medication and HC vs PD ON medication using least square support vector machine, respectively.

6

An improved framework for Parkinson’s disease prediction using Variational Mode Decomposition-Hilbert spectrum of speech signal

Karan Biswajit, Sekhar Sahu Sitanshu

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 2

717--732

EN

Parkinson’s disease (PD) is a neuro-degenerative disease due to loss of brain cells, which produces dopamine. It is most common after Alzheimer’s disease specially seen in old age people. In the earlier stage of disease, it has been noticed that most of the people suffering from speech disorder. From last two decades many studies have been conducted for the analysis of vocal tremors in PD. This study explores the combined approach of Variational Mode Decomposition (VMD) and Hilbert spectrum analysis (HSA) to investigate the voice tremor of patients with PD. A new set of features Hilbert cepstral coefficients (HCCs) are proposed in this study. Proposed features are assessed using vowels and words of PC-GITA database. The effectiveness of HCC features is utilized to perform classification, and regression analysis for PD detection. The highest average classification accuracy up to 91% and 96% is obtained with vowel /a/ and word /apto/ respectively. Further the classification accuracy up to 82% is obtained with independent dataset, when tested with the optimized model developed using PC-GITA database. In dysarthria level prediction highest correlation up to 0.82 is obtained using vowel /a/ and 0.8 with word /petaka/. The outcomes of this study indicate that the proposed articulatory features are suitable and accurate for PD assessment.

7

The repeatability of the instrumented timed Up & Go test: The performance of older adults and parkinson's disease patients under different conditions

Viteckova Slavka, Krupicka Radim, Dusek Petr, Cejka Vaclav, Kutilek Patrik, Novak Jan, Szabo Zoltan, Růžicka Evžen

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 1

363--377

EN

The Timed Up & Go (TUG) test is a simple test for gait and balance that requires no special equipment and can be part of a routine clinical examination. Combined with the development of motion capture technologies, the possibilities of assessing individual TUG sub-components (i.e. sit-to-stand, gait, turn, turn-to-sit) are increasing. The clinical evaluation of an instrumented TUG requires reliable values. We analysed the intra-session repeatability of the iTUG sit-to-stand, gait and turn parameters in three conditions: (1) single, (2) cognitive dual-tasks, and (3) manual dual-tasks in older adults and Parkinson's disease (PD) patients. The repeatability coefficient (RC) was calculated for each of the 18 parameters. The repeatability varied across subject groups, the performed tasks, and the TUG subcomponent. The gait subcomponent had 6 non-repeatable spatio-temporal parameters and 2 non-repeatable parameters for the arm swing. The parameters of the turn subcomponent can be considered as non-repeatable in both groups under the manual dual-task condition and in HC under the single-task condition. When comparing PD to HC, the repeatability of the majority of the single-task parameters was higher in PD whereas lower under dual-tasks. In PD, the major part of gait parameters had a higher repeatability under single-tasks than dual-tasks. In contrast, HC exhibited better repeatability of dual-tasks than single-tasks. Repeatability can be used to assist researchers and clinicians to select adequate parameters with respect to the purpose of motion assessment.

8

Pose and Optical Flow Fusion (POFF) for accurate tremor detection and quantification

Alper Mehmet Akif, Goudreau John, Daniel Morris

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 1

468--481

EN

Limb tremor measurements are one factor used to characterize and quantify the severity of neurodegenerative disorders. These tremor measurements can also provide dosage-response feedback to guide medication treatments. Here, we propose a system to automatically measure limb tremors in home or clinic settings. The key feature of proposed method is that it is contactless; not requiring a user to wear or hold a device or marker. Our sensor is a Kinect 2, which measures color and depth and estimates rough limb motion. We show that its pose accuracy is poor for small limb tremors below 10 mm amplitude, and so we propose an additional level of tremor tracking that recovers limb motion at a higher precision. Our method upgrades the sensitivity to achieve detection and analysis for tremors down to 2 mm amplitude. We include empirical experiments and measurements showing improved tremor amplitude and frequency estimation using our proposed Pose and Optical Flow Fusion (POFF) algorithm.

9

A novel deep LSTM network for artifacts detection in microelectrode recordings

Hosny Mohamed, Zhu Minwei, Gao Wenpeng, Fu Yili

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 3

1052--1063

EN

Microelectrode recording (MER) signals are world-widely used for validating the planned trajectories in the procedure of deep brain stimulation (DBS) surgery to obtain accurate position of electrodes inside the brain structure. Besides, MER signals are important source for studying extracellular neuronal activity and DBS biomarkers, such as, spike clustering and sorting. However, MER signals are prone to several artifacts derived from electrical equipment in the operating room, electrode movement and patient activities, etc., which reduce the signal-to-noise ratio of the MER signals. Therefore, in this paper, we propose a novel deep learning architecture based on long short-term memory (LSTM) network for automatic artifact detection in MER signals. Frequency and time-domain features were extracted from the raw MER signals and fed to the deep LSTM network. A manually annotated MER database obtained from 17 Parkinson's disease (PD) patients were used to validate the proposed architecture. The proposed architecture achieved promising results of 97.49% accuracy, 98.21% sensitivity and 96.87% specificity on an unseen test set. To our best knowledge, this is the first study to use LSTM network for artifacts detection in MER signals. The MER data will be available at http://homepage.hit.edu.cn/wpgao.

10

Early effect of thalamotomy on cognitive function in patients with Parkinson’s disease

Gorzelańczyk E. J., Ackermann-Szulgit D., Kunc M., Harat M., Walecki P.

Bio-Algorithms and Med-Systems

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2018

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Vol. 14, no. 1

art. no. 20180005

EN

Thalamotomy is a neurosurgical procedure used in the treatment of advanced Parkinson’s disease (PD). The aim of our research is to evaluate the early impact of a lesion in the ventrointermedial nucleus (VIM) of the thalamus on cognitive and motor function in people with PD. Sixty patients who qualified for right- or leftsided VIM thalamotomy were involved in the study. The cognitive and motor functions of each patient were assessed both prior to and following the surgical procedure. Twenty-nine PD patients without ablative treatment were qualified for the comparison group, and 57 neurologically healthy individuals were assigned to the control group. The following tests were carried out: Mini Mental State Examination, Benton Visual Retention Test, Stroop Color and Word Test, Trail Making Test A&B, and Rey Auditory Verbal Learning Test. Statistically significant differences were found in reaction time, visual-spatial working memory, auditory-verbal memory, and overall level of cognitive function when comparing the results of tests carried out before and after thalamotomy and when comparing patients who had undergone surgery with untreated or healthy individuals. In patients with right-sided and left-sided thalamotomy differences were also found in the mean number of perseverative errors and recalled words.

11

Influence of dopaminergic treatment on resting elbow joint angle control mechanisms in patients with Parkinson’s disease – a preliminary report

Marusiak J., Jaskólska A., Budrewicz S., Koszewicz M., Andrzejewska R., Kisiel-Sajewicz K., Jaskólski A.

Acta of Bioengineering and Biomechanics

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2018

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Vol. 20, no. 4

75--82

EN

Heightened tonic stretch reflex contributes to increased muscle tone and a more-flexed resting elbow joint angle (EJA) in patients with Parkinson’s disease (PD). Dopaminergic medication restores central nervous system (CNS) functioning and decreases resting muscle electrical and mechanical activities. This study aimed to evaluate the effects of dopaminergic medication on parkinsonian rigidity, resting EJA, resting electrical activity (electromyography, EMG) and mechanical properties (myotonometry, MYO) of elbow flexor muscles and the associations of EJA with these muscles resting electrical activity and mechanical properties in PD patients. We also evaluated a relationship between dopaminergic treatment dose and these outcome measures values. Methods: Ten PD patients (age 68 ± 10.1 years; body mass 70 ± 16.8 kg; height 162 ± 6.6 cm; illness duration 9 ± 4.5 years) were tested during medication on- and off-phases. Resting EJA, myotonometric muscle stiffness (S-MYO) and root mean square electromyogram amplitude (RMS-EMG) were recorded from relaxed biceps brachii and brachioradialis muscles. Based on the above parameters, we also calculated the EJA/S-MYO ratio and EJA/RMS-EMG ratio. Parkinsonian rigidity was assessed using the motor section of the Unified Parkinson’s Disease Rating Scale. Results: EJA, EJA/S-MYO ratio, and EJA/RMS-EMG ratio were increased and S-MYO, RMS-EMG, and parkinsonian rigidity were decreased during the medication on-phase compared with the off-phase. In addition, the dopaminergic treatment dose was negatively correlated with S-MYO and RMS-EMG, and positively correlated with EJA/SMYO and EJA/RMS-EMG ratios. Conclusions: We conclude that dopaminergic medication-induced improvements in resting elbow joint angle in tested patients with PD are related to changes in their muscle electrical and mechanical properties.

12

Zastosowanie rezonansu magnetycznego w leczeniu choroby Parkinsona

Garbaciok S., Stąpór-Fudzińska M., Brzezińska D., Ślosarek K.

Inżynier i Fizyk Medyczny

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2017

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Vol. 6, nr 1

17--25

PL

Choroby neurodegeneracyjne, do których należy choroba Parkinsona, charakteryzuje patologiczny proces prowadzący do utraty komórek nerwowych na drodze apoptozy bądź nekrozy. Dokładniejsze poznanie ich patomechanizmów, lepsza diagnostyka, a przede wszystkim leczenie są niezwykle istotne dla współczesnej medycyny. Liczba osób zmagających się z chorobą Parkinsona będzie stale rosnąć, czego powodem jest występowanie zjawiska starzenia się społeczeństwa krajów zachodnich. W badaniach obrazowych tomografii komputerowej i rezonansu magnetycznego obraz tej choroby nie jest jednoznaczny. Zastosowanie badania rezonansu magnetycznego w leczeniu choroby Parkinsona ma niezwykle istotne znaczenie z punktu widzenia terapeutycznego i w mniejszym stopniu z punktu widzenia diagnostycznego ze względu na występowanie pewnych cech choroby w badaniu obrazowym w późnym stadium choroby. Wykonanie rezonansu magnetycznego umożliwia dokładne zlokalizowanie dowolnego celu w mózgu i przeprowadzenie operacji metodą stereotaktyczną. Rezonans magnetyczny ma także niezwykle istotne znaczenie w radiochirurgii z użyciem CyberKnife.

EN

Neurodegenerative diseases which include Parkinson’s disease are characterized by a pathological process leading to a loss of nerve cells by apoptosis or necrosis. More detailed knowledge of the pathomechanisms, improved diagnostics, and especially treatment are extremely important for contemporary medicine. The diseases continue to spread, despite significant advancements in medicine. The imaging computed tomography and magnetic resonance image of this disease is not clear apart from lowering the signal substantia nigra and the lenticular nucleus in come cases. The application of magnetic resonance imaging in the treatment of Parkinson’s disease is of paramount importance, not only from the point of view of diagnosis, but from the viewpoint of therapeutic effect. Performing a magnetic resonance imaging allows the precise location of any object in the brain and performing the surgery using stereotactic. Magnetic resonance imaging is also extremely important in radiosurgery using CyberKnife.

13

Postural stability in Parkinson’s disease patients

Wilczyński J., Pedrycz A., Zieliński E., Ambroży T., Mucha D.

Acta of Bioengineering and Biomechanics

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2017

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Vol. 19, no. 4

135--141

EN

Purpose: The aim of the study was to analyze postural stability in Parkinson's disease patients. A total of 32 subjects were tested, including 26 (81.25%) women and 6 (18.75%) men. These were patients with advanced, idiopathic Parkinson’s disease. The disease duration was over 5 years. Methods: The study was conducted in the Posturology Laboratory at the Department of Medicine and Health Sciences, Jan Kochanowski University in Kielce (Poland). The Biodex Balance System was used for evaluation of postural stability. Postural Stability Testing was performed with both feet positioned on a stable surface with the eyes open. Results: The Overall Stability Index in the whole group was 0.5°. The higher Overall Stability Index in women is indicative of slightly worse postural stability compared to men, although in both groups, it was within norms (Z = 2.0545, p = 0.0399). Anterior-Posterior Overall Stability Index (A/P) was an average of 0.35°. The Medial-Lateral Overall Stability Index (M/L) was an average of 0.27°. Both women and men were observed to have higher postural sway in the sagittal plane than the frontal plane. The vast majority of the subjects maintained in Zone A during testing (99.94%), and was slightly bent backwards to the right and in Quadrant IV (61.53%). Conclusions: Regular control of postural stability in Parkinson's disease patients is significant due to the risk of falls.

14

Data mining and neural network simulations can help to improve deep brain stimulation effects in parkinson’s disease

Szymański A., Kubis A., Przybyszewski A. W.

Computer Science

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2015

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Vol. 16 (2)

199--215

EN

Parkinson’s Disease (PD) is primary related to substantia nigra degeneration and, thus, dopamine insufficiency. L-DOPA as a precursor of dopamine is the standard medication in PD. However, disease progression causes L-DOPA therapy efficiency decay (on-off symptom fluctuation), and neurologists often decide to classify patients for DBS (Deep Brain Stimulation) surgery. DBS treatment is based on stimulating the specific subthalamic structure: subthalamic nucleus (STN) in our case. As STN consists of parts with different physiological functions, finding the appropriate placement of the DBS electrode contacts is challenging. In order to predict the neurological effects related to different electrodecontact stimulations, we have tracked connections between the stimulated part of STN and the cortex with the help of diffusion tensor imaging (DTI). By changing a contacts number and amplitude of stimulus (proportional in size to stimulated area), we have determined connections to cortical areas and related neurological effects. We have applied data mining methods to predict which contact (and at what amplitude) should be stimulated in order to improve a particular symptom. We have compared different data mining methods: Wekas Random Forest classifier and Rough Set Exploration System (RSES). We have demonstrated that the Weka classifier was more accurate when predicting the effects of stimulations on general neurological improvements, while RSES was more accurate when using specific neurological symptoms. We have simulated other effects of stimulation related to the interruption of pathological oscillation in the basal ganglia found in PD. Our model represents possible STN neural population with inhibitory and excitatory connections that have pathologically synchronized oscillations. High-frequency electrical stimulation has interrupted synchronization. something that is also observed in PD patients.

15

Verification of the functionality of device for monitoring human tremor

Szumilas M., Lewenstein K., Ślubowska E.

Biocybernetics and Biomedical Engineering

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2015

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Vol. 35, no. 4

240--246

EN

Tremor accompanying the Parkinson's disease is perceived as one of its most disturbing symptoms. Among available treatments there is a deep brain stimulation, which effectively reduces unwanted oscillations of patient's muscles. Nevertheless, setting parameters of the stimulation is a highly empirical process and the final outcome depends primarily on the experience of involved medical personnel. We present a device which is meant to provide a clinician with feedback based on the measurable parameters of tremor, monitored in many points of the body simultaneously. Functionality of the device was verified at a basic level. During the verification, the vibrations were recorded: (1) in a relaxed arm, (2) during voluntary contraction of muscles and (3) after being damped by tissues (in this case the vibrations were introduced from an external generator). Moreover, a method of selecting optimal place for mounting vibration probes is presented.

16

System pomiarowy do rejestracji drżeń parkinsonowskich

Piwowar P., Ćwik T., Cianciara C.

Przegląd Elektrotechniczny

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2014

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R. 90, nr 5

130--133

PL

W pracy zaprezentowano system pomiarowy umożliwiający dokładny i powtarzalny pomiar drżeń parkinsonowskich kończyn górnych. Prawidłowe rozpoznanie choroby Parkinsona jest kluczowe dla wyboru właściwego sposobu leczenia pacjenta. Niestety większość klinicznych kryteriów diagnostycznych wymaga szczegółowej historii choroby oraz długiego czasu obserwacji pacjenta. Dostępne obecnie specjalistyczne metody badań, są dość drogie i nie nadające się do powszechnego zastosowania. Zaproponowane rozwiązanie, oparte na trójosiowym czujniku przyśpieszenia może zapewnić stosunkowo szybką i dokładną diagnozę.

EN

In this paper, the authors present the hand tremor measurement system. The correct diagnosis of Parkinson's disease is crucial for the selection of proper treatment and future prognosis. Unfortunately most of clinical diagnostic criteria require a detailed patient history and long observation time. There are also available specialist survey methods, but they are quite expensive and not suitable for general diagnosis. The proposed solution is to use acceleration sensor can provide possibly fast and accurate diagnosis.

17

Diagnosing Parkinson’s disease using the classification of speech signals

Froelich W., Wróbel K., Porwik P.

Journal of Medical Informatics & Technologies

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2014

|

Vol. 23

187--193

EN

This paper addressees the problem of an early diagnosis of Parkinson’s disease by the classification of characteristic features of person’s voice. A new, two-step classification approach is proposed. In the first step, the voice samples are classified using standard state-of-the-art classifiers. In the second step, the classified samples are assigned to patients and the final classification process based on majority criterion is performed. The advantage of using our new approach is the resulting, reliable patientoriented medical diagnose. The proposed two-step method of classification allows also to deal with the variable number of voice samples gathered for every patient. Preliminary experiments revealed quite satisfactory classification accuracy obtained during the performed leave-one-out cross validation.

18

Classification of speech intelligibility in Parkinson's disease

Khan T., Westin J., Dougherty M.

Biocybernetics and Biomedical Engineering

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2014

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Vol. 34, no. 1

35--45

EN

A problem in the clinical assessment of running speech in Parkinson's disease (PD) is to track underlying deficits in a number of speech components including respiration, phonation, articulation and prosody, each of which disturbs the speech intelligibility. A set of 13 features, including the cepstral separation difference and Mel-frequency cepstral coefficients were computed to represent deficits in each individual speech component. These features were then used in training a support vector machine (SVM) using n-fold cross validation. The dataset used for method development and evaluation consisted of 240 running speech samples recorded from 60 PD patients and 20 healthy controls. These speech samples were clinically rated using the Unified Parkinson's Disease Rating Scale Motor Examination of Speech (UPDRS-S). The classification accuracy of SVM was 85% in 3 levels of UPDRS-S scale and 92% in 2 levels with the average area under the ROC (receiver operating characteristic) curves of around 91%. The strong classification ability of selected features and the SVM model supports suitability of this scheme to monitor speech symptoms in PD.

19

Cepstral separation difference: A novel approach for speech impairment quantification in Parkinson's disease

Khan T., Westin J., Dougherty M.

Biocybernetics and Biomedical Engineering

|

2014

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Vol. 34, no. 1

25--34

EN

This paper introduces a novel approach, Cepstral Separation Difference (CSD), for quantification of speech impairment in Parkinson's disease (PD). CSD represents a ratio between the magnitudes of glottal (source) and supra-glottal (filter) log-spectrums acquired using the source-filter speech model. The CSD-based features were tested on a database consisting of 240 clinically rated running speech samples acquired from 60 PD patients and 20 healthy controls. The Guttmann (μ2) monotonic correlations between the CSD features and the speech symptom severity ratings were strong (up to 0.78). This correlation increased with the increasing textual difficulty in different speech tests. CSD was compared with some non-CSD speech features (harmonic ratio, harmonic-to-noise ratio and Mel-frequency cepstral coefficients) for speech symptom characterization in terms of consistency and reproducibility. The high intra-class correlation coefficient (>0.9) and analysis of variance indicates that CSD features can be used reliably to distinguish between severity levels of speech impairment. Results motivate the use of CSD in monitoring speech symptoms in PD.

20

Aparat rejestrujący drżenie w chorobie Parkinsona z zastosowaniem akcelerometru MEMS

Karaś K., Tutaj J.

Aktualne Problemy Biomechaniki

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2013

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z. 7

87--92

PL

Głównym celem artykułu jest opis prototypu aparatu do rejestracji drżenia, którego najważniejszym elementem jest akcelerometr. W artykule zawarto kryteria wyboru czujnika do pomiaru tremoru oraz wyjaśniono zasadę działania termicznego akcelerometru MEMS, który jest zasadniczym elementem skonstruowanego przyrządu. W celu zaprezentowania działania urządzenia zamieszczono przebiegi czasowe sygnału pomiarowego pochodzące od dwóch pacjentów ze zdiagnozowaną chorobą Parkinsona.

EN

The main aim of this article is to describe a prototype device for measuring tremor in Parkinson Disease. Device is based on thermal accelerometer constructed in MEMS technology The article includes criteria for selecting a sensor for measuring tremor and explains the principle of operation of thermal MEMS accelerometer. Article included measuring signal waveforms derived from two patients diagnosed with Parkinson's disease.