scholarly journals A Non-Invasive Medical Device for Parkinson’s Patients with Episodes of Freezing of Gait

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 737 ◽  
Author(s):  
Catalina Punin ◽  
Boris Barzallo ◽  
Roger Clotet ◽  
Alexander Bermeo ◽  
Marco Bravo ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Mobile Application ◽  
Low Cost ◽  
Freezing Of Gait ◽  
Discrete Wavelet ◽  
Vibratory Stimulation ◽  
Non Invasive ◽  
Sensitivity Specificity ◽  
Gait Monitoring ◽  
Stimulation Of

A critical symptom of Parkinson’s disease (PD) is the occurrence of Freezing of Gait (FOG), an episodic disorder that causes frequent falls and consequential injuries in PD patients. There are various auditory, visual, tactile, and other types of stimulation interventions that can be used to induce PD patients to escape FOG episodes. In this article, we describe a low cost wearable system for non-invasive gait monitoring and external delivery of superficial vibratory stimulation to the lower extremities triggered by FOG episodes. The intended purpose is to reduce the duration of the FOG episode, thus allowing prompt resumption of gait to prevent major injuries. The system, based on an Android mobile application, uses a tri-axial accelerometer device for gait data acquisition. Gathered data is processed via a discrete wavelet transform-based algorithm that precisely detects FOG episodes in real time. Detection activates external vibratory stimulation of the legs to reduce FOG time. The integration of detection and stimulation in one low cost device is the chief novel contribution of this work. We present analyses of sensitivity, specificity and effectiveness of the proposed system to validate its usefulness.

scholarly journals Method for auxiliary use of thermography in diagnosing inflammation in the coxofemoral joint in dogs

2018 ◽  
Vol 39 (4) ◽  
pp. 1565
Author(s):  
Fernanda Lúcia Passos Fukahori ◽  
Daniela Maria Bastos de Souza ◽  
Eduardo Alberto Tudury ◽  
George Chaves Jimenez ◽  
José Ferreira da Silva Neto ◽  
...  
Keyword(s):  
Thermal Emission ◽  
Low Cost ◽  
Joint Inflammation ◽  
Domestic Animals ◽  
The Body ◽  
Joint Diseases ◽  
Infrared Images ◽  
Infrared Thermometry ◽  
Non Invasive ◽  
Sensitivity Specificity

Joint diseases are relatively common in domestic animals, such as dogs. The involved inflammation produces thermal emission, which can be imaged using specific sensors that allow capturing of infrared images. Given that there have been few reports on the use of thermography in the diagnosis of inflammation associated with diseases of the hip joint in dogs, we here propose a method for identification of inflammatory foci in dogs by using infrared thermometry. The present study aimed to find non-invasive and low-cost resources that couldfacilitate a clinical diagnosis in cases withinflammation in the coxofemoral joint of dogs.To this end, we developed a system in whichthe Flir Systems TG165 thermograph is coupled to a black PVC cannula with a 30-cm focus-to-animal distance.External effects of the environment on the temperature of the animalswere compared with the body temperature as measured by a conventional thermometer.Thirty-one dogs with and without inflammation in the coxofemoral joint underwent clinical evaluation.We verified that the temperature registered by the thermograph inthe animals with joint inflammation was significantlydifferentfrom that incontrol animals without inflammation, in the lateral projection.The method showed a sensitivity of 80%, specificity of 87.5%, and accuracy of 83.87%. This standardized method of diagnosis of inflammatory foci in the coxofemoral articulation of dogs by way of thermography showed sensitivity, specificity, and satisfactory accuracy.

scholarly journals Comparative study of colour Doppler vs. FNNAC findings in WHO grade II thyroid enlargement in adult females

2019 ◽  
Vol 5 (4) ◽  
pp. 1025
Author(s):  
Sudhakar Rao M. S. ◽  
Navneeth T. P. ◽  
John C. J.
Keyword(s):  
Pulsatility Index ◽  
Low Cost ◽  
Resistive Index ◽  
Colour Doppler ◽  
Who Grade ◽  
Predictive Values ◽  
Non Invasive ◽  
Adult Females ◽  
The Mean ◽  
Sensitivity Specificity

<p class="abstract"><strong>Background:</strong> Thyroid gland disorders form one of the most common endocrinal and surgical problems encountered in clinical practice. FNNAC is widely accepted as the primary and better method than FNAC for investigation but has its disadvantages. Colour Doppler is a non-invasive, low cost, easily available and repeatable investigation with least patient discomfort and can be valuable in detection of benign and malignant thyroid enlargements.</p><p class="abstract"><strong>Methods:</strong> Forty cases of adult females with WHO grade 2 thyroid enlargement attending the department of otorhinolaryngology selected on simple random basis were included in this study. Following written consent, Colour Doppler scanning and FNNAC test were done on the thyroid swelling and the results were analysed.  </p><p class="abstract"><strong>Results:</strong> The mean age of patients was 32.44 years. The mean age of malignancy was 44.66 years and showed statistically significant association. The Resistive and Pulsatility index and combination of both were found to have statistically significant results in detecting malignant and benign lesions The sensitivity, specificity, positive and negative predictive values of RI and PI were 83.33%, 94.12%, 71.43%, 96.97% and 50%, 94.12%, 60% and 91.43% respectively. On combining both the indices, the sensitivity was 91.67% and the positive predictive value was 97.06%.</p><p class="abstract"><strong>Conclusions:</strong> Colour Doppler can differentiate between benign and malignant thyroid enlargements using Resistive index (of&gt;0.75) and Pulsatility Index (of&gt;1.5) and can be a complementary diagnostic tool in the thyroid enlargement lesions, considering its accuracy, cost-effectiveness, easy availability and non-invasive repeatable nature.</p>

scholarly journals Discrete wavelet transform-based freezing of gait detection in Parkinson’s disease

2018 ◽  
pp. 1-17 ◽  
Author(s):  
Amira El-Attar ◽  
Amira S. Ashour ◽  
Nilanjan Dey ◽  
Hatem Abdelkader ◽  
Mostafa M. Abd El-Naby ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Freezing Of Gait ◽  
Discrete Wavelet

Decomposition Level Comparison of Stationary Wavelet Transform Filter for Visual Task Electroencephalogram

2015 ◽  
Vol 74 (6) ◽  
Author(s):  
Syarifah Noor Syakiylla Sayed Daud ◽  
Rubita Sudirman
Keyword(s):  
Wavelet Transform ◽  
Low Cost ◽  
Stationary Wavelet Transform ◽  
Decomposition Level ◽  
Non Invasive ◽  
Positron Emission ◽  
Filtering Technique ◽  
To Come ◽  
Power Line Interference ◽  
Invasive Techniques

There has been a lot of research on the study of the human brain. Many modalities such as medical resonance imaging (MRI), computerized tomography (CT), positron emission tomography (PET), electroencephalography (EEG) and etc. has been invented. However, between this modality the electroencephalography widely chosen by researchers due to it is low cost, non-invasive techniques, and safely use. One of the major problems, the signal is corrupted by artifacts, whether to come from the muscle movement (electromyography artifact), eye blink and movement (electrooculography artifact) and power line interference. Filtering technique is applied to the signal in order to remove these artifacts. Wavelet approach is one of the technique that can filter out the artifact. This paper aim to determine which decomposition level is suitable for filtering EEG signal at channel Fp1, Fz, F8, Pz, O1 and O2 use stationary wavelet transform filter at db3 mother wavelet. Eight different decomposition levels have been selected and analyze based on mean square error (MSE) parameter. The Neurofax 9200 was used to record the brain signal at selected channel. Result shows that the decomposition at level 5 is suitable for filtering process using this stationary wavelet transform approach without losing important information.

scholarly journals Controlling the behaviour of Drosophila melanogaster via smartphone optogenetics

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Ilenia Meloni ◽  
Divya Sachidanandan ◽  
Andreas S. Thum ◽  
Robert J. Kittel ◽  
Caroline Murawski
Keyword(s):  
Drosophila Melanogaster ◽  
Low Cost ◽  
Model Organism ◽  
Light Sensitivity ◽  
Light Sources ◽  
Neuronal Populations ◽  
Non Invasive ◽  
Invertebrate Species ◽  
Temporal And Spatial ◽  
Stimulation Of

Abstract Invertebrates such as Drosophila melanogaster have proven to be a valuable model organism for studies of the nervous system. In order to control neuronal activity, optogenetics has evolved as a powerful technique enabling non-invasive stimulation using light. This requires light sources that can deliver patterns of light with high temporal and spatial precision. Currently employed light sources for stimulation of small invertebrates, however, are either limited in spatial resolution or require sophisticated and bulky equipment. In this work, we used smartphone displays for optogenetic control of Drosophila melanogaster. We developed an open-source smartphone app that allows time-dependent display of light patterns and used this to activate and inhibit different neuronal populations in both larvae and adult flies. Characteristic behavioural responses were observed depending on the displayed colour and brightness and in agreement with the activation spectra and light sensitivity of the used channelrhodopsins. By displaying patterns of light, we constrained larval movement and were able to guide larvae on the display. Our method serves as a low-cost high-resolution testbench for optogenetic experiments using small invertebrate species and is particularly appealing to application in neuroscience teaching labs.

scholarly journals EEG Motor-Imagery BCI System Based on Maximum Overlap Discrete Wavelet Transform (MODWT) and Machine learning algorithm

2021 ◽  
Vol 17 (2) ◽  
pp. 38-45
Author(s):  
Samaa Abdulwahab ◽  
Hussain Khleaf ◽  
Manal Jassim
Keyword(s):  
Wavelet Transform ◽  
Decision Tree ◽  
Discrete Wavelet Transform ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Low Cost ◽  
Brain Diseases ◽  
Discrete Wavelet ◽  
K Nearest Neighbor ◽  
Brain Connection

The ability of the human brain to communicate with its environment has become a reality through the use of a Brain-Computer Interface (BCI)-based mechanism. Electroencephalography (EEG) has gained popularity as a non-invasive way of brain connection. Traditionally, the devices were used in clinical settings to detect various brain diseases. However, as technology advances, companies such as Emotiv and NeuroSky are developing low-cost, easily portable EEG-based consumer-grade devices that can be used in various application domains such as gaming, education. This article discusses the parts in which the EEG has been applied and how it has proven beneficial for those with severe motor disorders, rehabilitation, and as a form of communicating with the outside world. This article examines the use of the SVM, k-NN, and decision tree algorithms to classify EEG signals. To minimize the complexity of the data, maximum overlap discrete wavelet transform (MODWT) is used to extract EEG features. The mean inside each window sample is calculated using the Sliding Window Technique. The vector machine (SVM), k-Nearest Neighbor, and optimize decision tree load the feature vectors.

Real-Time EEG Device for Epilepsy Detection Using Wavelet Transform and Support Vector Machine

2021 ◽  
pp. 104-135
Author(s):  
Sharad Sarjerao Jagtap ◽  
Rajesh Kumar M.
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Wavelet Transform ◽  
Real Time ◽  
Low Cost ◽  
Support Vector ◽  
Svm Classifier ◽  
Discrete Wavelet ◽  
Feature Extraction Technique

This chapter gives an effective and efficient technique that can detect epilepsy in real time. It is low cost, low power, and real-time devices that can easily detect epilepsy. Along with EEG device, one can upgrade with GSM module to alert the doctors and parents of patients about its occurrence to prevent a sudden fall, which may cause injury and death. The accuracy of this EEG device depends on the quality of feature extraction technique and classification algorithm. In this chapter, support vector machine (SVM) is used as a classifier. Wavelet transform gives feature extraction, which helps to train data and to detect normal or seizure patients. Discrete wavelet transform (DWT) decomposes the signals into three decomposition levels. In this detection, mean, median, and non-linear parameter entropy were calculated for every sub-band as key parameters. The extracted features are then applied to SVM classifier for the classification. Better accuracy of classification is obtained using wavelet and SVM classifier.

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