Parkinson Disease Detection Based on Voice and EMG Pattern Classification Method for Indonesian Case Study

Putri, Farika Tono; Caesarendra, Wahyu; Pamanasari, Elta Diah; Ariyanto, Mochammad; Setiawan, Joga Dharma

doi:10.22219/jemmme.v3i2.6977

Cited by 5 publications

(3 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Like the analysis of the brain, motor symptoms of PD can also be assessed by physiological signals, namely EMG. However, only one deep learning study has attempted to use EMG for PD diagnosis with the ANN model [76], and the performance of their proposed model was 71%, less than that of the studies that focused on gait, handwriting, and speech (Appendix A Table A2). Hence, for EMG to be recognized as a potential biomarker for PD diagnosis, more research in this area is required.…”

Section: Motor Symptomsmentioning

confidence: 97%

Application of Deep Learning Models for Automated Identification of Parkinson’s Disease: A Review (2011–2021)

Loh

Hong²,

Ooi

et al. 2021

Sensors

View full text Add to dashboard Cite

Parkinson’s disease (PD) is the second most common neurodegenerative disorder affecting over 6 million people globally. Although there are symptomatic treatments that can increase the survivability of the disease, there are no curative treatments. The prevalence of PD and disability-adjusted life years continue to increase steadily, leading to a growing burden on patients, their families, society and the economy. Dopaminergic medications can significantly slow down the progression of PD when applied during the early stages. However, these treatments often become less effective with the disease progression. Early diagnosis of PD is crucial for immediate interventions so that the patients can remain self-sufficient for the longest period of time possible. Unfortunately, diagnoses are often late, due to factors such as a global shortage of neurologists skilled in early PD diagnosis. Computer-aided diagnostic (CAD) tools, based on artificial intelligence methods, that can perform automated diagnosis of PD, are gaining attention from healthcare services. In this review, we have identified 63 studies published between January 2011 and July 2021, that proposed deep learning models for an automated diagnosis of PD, using various types of modalities like brain analysis (SPECT, PET, MRI and EEG), and motion symptoms (gait, handwriting, speech and EMG). From these studies, we identify the best performing deep learning model reported for each modality and highlight the current limitations that are hindering the adoption of such CAD tools in healthcare. Finally, we propose new directions to further the studies on deep learning in the automated detection of PD, in the hopes of improving the utility, applicability and impact of such tools to improve early detection of PD globally.

show abstract

Section: Motor Symptomsmentioning

confidence: 97%

Application of Deep Learning Models for Automated Identification of Parkinson’s Disease: A Review (2011–2021)

Loh

Hong²,

Ooi

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…The final classification can be based on machine learning technology using a neural network approach. Parkinson disease detection using pattern recognition method has been presented in literature by Putri [4]. It extracted 22 speech features and 12 EMG features and used artificial neural networks as a classification method.…”

Section: Reviewmentioning

confidence: 99%

IT Diagnostics of Parkinson’s Disease Based on the Analysis of Voice Markers and Machine Learning

Vishniakou

Xia

2023

Doklady Belorusskogo gosudarstvennogo universiteta informatiki

View full text Add to dashboard Cite

The results of studying the parameters of the spectra of speech signals by machine learning with the use of neural networks are presented. This study was carried out in order to confirm experimentally the possibility of performing an assessment of these parameters for the detection of Parkinson’s disease in the early stages (IT diagnostics). During the study, the public database was used, which systematized the spectra of vowel sounds uttered by patients with Parkinson’s disease. The applied method is binary data classification. In the course of the study, the speech data spectrum was first preprocessed, which consisted of filtering it in order to remove its noise components and eliminate bursts and gaps in it. Then the parameters of the processed spectrum of speech data were determined: average value, maximum and minimum, peak, wavelet coefficients, MFCC and TQWT. After that, the object was selected using the PCA algorithm. The model was trained using the Knn and Random Forest algorithms, as well as the Bayesian neural network. The Bayesian optimization algorithm and the GridSearch method were used to find the best model hyperparameters. It has been established that when using Knn, Random Forest and Bayesian neural network, it is possible to increase the accuracy of recognition of Parkinson’s disease by 94.7; 88.16 and 74.74 %, respectively. A similar study by other scientists showed that the recognition accuracy of data sets was only 86 %.

show abstract

“…Another variety of interfacing pursued in supporting servo control for therapy devices, such as in studies that focus on bio-electric sensors using electromyography (EMG), electroencephalography (EEG), electrocardiography (ECG), and phyisichal sensory eg, Flex sensor, leap motion, Intertial measurement unit sensory (IMU), even multisensory approach and inference models on hand rehabilitation devices [24,25,38,39]. Thus sensor will have a high level of accuracy when using a multichannel design with a sensitive, relatively expensive, and larger acquisition device and hig computational power.…”

Section: Introductionmentioning

confidence: 99%

Development of Speech Command Control Based TinyML System for Post-Stroke Dysarthria Therapy Device

Riyanta,

Irianta,

Kamiel

2023

Journal of Robotics and Control

View full text Add to dashboard Cite

Post-stroke dysarthria (PSD) is a widespread outcome of a stroke. To help in the objective evaluation of dysarthria, the development of pathological voice recognition and technology has a lot of attention. Soft robotics therapy devices have been received as an alternative rehabilitation and hand grasp assistance for improving activity daily living (ADL). Despite the significant progress in this field, most soft robotic therapy devices use a complex, bulky, lack of pathological voice recognition model, large computational power, and stationary controller. This study aims to develop a portable wirelessly multi-controller with a simulated dysarthric vowel speech in Bahasa Indonesia and non-dysarthric micro speech recognition, using tiny machine learning (TinyMl) system for hardware efficiency. The speech interface using INMP441, compute with a lightweight Deep Convolutional Neural network (DCNN) design and embedded into ESP-32. Feature model using Short Time Fourier Transform (STFT) and fed into CNN. This method has proven useful in micro-speech recognition with low computational power in both speech scenarios with a level of accuracy above 90%. Realtime inference performance on ESP-32 using hand prosthetics, with 3-level household noise intensity respectively 24db,42db, and 62db, and has respectively resulted from 95%, 85%, and 50% Accuracy. Wireless connectivity success rate with both controllers is around 0.2 - 0.5 ms.

show abstract

Parkinson Disease Detection Based on Voice and EMG Pattern Classification Method for Indonesian Case Study

Cited by 5 publications

References 11 publications

Application of Deep Learning Models for Automated Identification of Parkinson’s Disease: A Review (2011–2021)

Application of Deep Learning Models for Automated Identification of Parkinson’s Disease: A Review (2011–2021)

IT Diagnostics of Parkinson’s Disease Based on the Analysis of Voice Markers and Machine Learning

Development of Speech Command Control Based TinyML System for Post-Stroke Dysarthria Therapy Device

Contact Info

Product

Resources

About