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Peng Y, Ma C, Li M, Liu Y, Yu J, Pan L, Zhang Z. Intelligent devices for assessing essential tremor: a comprehensive review. J Neurol 2024; 271:4733-4750. [PMID: 38816480 DOI: 10.1007/s00415-024-12354-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 03/25/2024] [Accepted: 03/26/2024] [Indexed: 06/01/2024]
Abstract
Essential tremor (ET) stands as the most prevalent movement disorder, characterized by rhythmic and involuntary shaking of body parts. Achieving an accurate and comprehensive assessment of tremor severity is crucial for effectively diagnosing and managing ET. Traditional methods rely on clinical observation and rating scales, which may introduce subjective biases and hinder continuous evaluation of disease progression. Recent research has explored new approaches to quantifying ET. A promising method involves the use of intelligent devices to facilitate objective and quantitative measurements. These devices include inertial measurement units, electromyography, video equipment, and electronic handwriting boards, and more. Their deployment enables real-time monitoring of human activity data, featuring portability and efficiency. This capability allows for more extensive research in this field and supports the shift from in-lab/clinic to in-home monitoring of ET symptoms. Therefore, this review provides an in-depth analysis of the application, current development, potential characteristics, and roles of intelligent devices in assessing ET.
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Affiliation(s)
- Yumeng Peng
- Center for Artificial Intelligence in Medicine, Medical Innovation Research Department, PLA General Hospital, Beijing, 100853, China
- Department of Neurology, 923th Hospital of the Joint Logistics Support Force of PLA, Nanning, 530021, China
- Chinese PLA Medical School, Beijing, 100853, China
| | - Chenbin Ma
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Mengwei Li
- Center for Artificial Intelligence in Medicine, Medical Innovation Research Department, PLA General Hospital, Beijing, 100853, China
- Chinese PLA Medical School, Beijing, 100853, China
| | - Yunmo Liu
- Chinese PLA Medical School, Beijing, 100853, China
| | - Jinze Yu
- School of Computer Science and Engineering, Beihang University, Beijing, 100191, China
| | - Longsheng Pan
- Department of Neurosurgery, First Medical Center, PLA General Hospital, Beijing, 100853, China.
| | - Zhengbo Zhang
- Center for Artificial Intelligence in Medicine, Medical Innovation Research Department, PLA General Hospital, Beijing, 100853, China.
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Wu H, Qi J, Purwanto E, Zhu X, Yang P, Chen J. Multi-Scale Feature and Multi-Channel Selection toward Parkinson's Disease Diagnosis with EEG. SENSORS (BASEL, SWITZERLAND) 2024; 24:4634. [PMID: 39066031 PMCID: PMC11280892 DOI: 10.3390/s24144634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2024] [Revised: 07/10/2024] [Accepted: 07/13/2024] [Indexed: 07/28/2024]
Abstract
OBJECTIVE Motivated by Health Care 4.0, this study aims to reducing the dimensionality of traditional EEG features based on manual extracted features, including statistical features in the time and frequency domains. METHODS A total of 22 multi-scale features were extracted from the UNM and Iowa datasets using a 4th order Butterworth filter and wavelet packet transform. Based on single-channel validation, 29 channels with the highest R2 scores were selected from a pool of 59 common channels. The proposed channel selection scheme was validated on the UNM dataset and tested on the Iowa dataset to compare its generalizability against models trained without channel selection. RESULTS The experimental results demonstrate that the proposed model achieves an optimal classification accuracy of 100%. Additionally, the generalization capability of the channel selection method is validated through out-of-sample testing based on the Iowa dataset Conclusions: Using single-channel validation, we proposed a channel selection scheme based on traditional statistical features, resulting in a selection of 29 channels. This scheme significantly reduced the dimensionality of EEG feature vectors related to Parkinson's disease by 50%. Remarkably, this approach demonstrated considerable classification performance on both the UNM and Iowa datasets. For the closed-eye state, the highest classification accuracy achieved was 100%, while for the open-eye state, the highest accuracy reached 93.75%.
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Affiliation(s)
- Haoyu Wu
- Department of Computing, Xi’an Jiaotong-Liverpool Univeristy, Suzhou 215000, China; (H.W.); (E.P.); (X.Z.)
| | - Jun Qi
- Department of Computing, Xi’an Jiaotong-Liverpool Univeristy, Suzhou 215000, China; (H.W.); (E.P.); (X.Z.)
| | - Erick Purwanto
- Department of Computing, Xi’an Jiaotong-Liverpool Univeristy, Suzhou 215000, China; (H.W.); (E.P.); (X.Z.)
| | - Xiaohui Zhu
- Department of Computing, Xi’an Jiaotong-Liverpool Univeristy, Suzhou 215000, China; (H.W.); (E.P.); (X.Z.)
| | - Po Yang
- Department of Computer Science, The University of Sheffield, Sheffield S10 2TN, UK;
| | - Jianjun Chen
- Department of Computing, Xi’an Jiaotong-Liverpool Univeristy, Suzhou 215000, China; (H.W.); (E.P.); (X.Z.)
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Rakhmatulin I, Dao MS, Nassibi A, Mandic D. Exploring Convolutional Neural Network Architectures for EEG Feature Extraction. SENSORS (BASEL, SWITZERLAND) 2024; 24:877. [PMID: 38339594 PMCID: PMC10856895 DOI: 10.3390/s24030877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 01/12/2024] [Accepted: 01/20/2024] [Indexed: 02/12/2024]
Abstract
The main purpose of this paper is to provide information on how to create a convolutional neural network (CNN) for extracting features from EEG signals. Our task was to understand the primary aspects of creating and fine-tuning CNNs for various application scenarios. We considered the characteristics of EEG signals, coupled with an exploration of various signal processing and data preparation techniques. These techniques include noise reduction, filtering, encoding, decoding, and dimension reduction, among others. In addition, we conduct an in-depth analysis of well-known CNN architectures, categorizing them into four distinct groups: standard implementation, recurrent convolutional, decoder architecture, and combined architecture. This paper further offers a comprehensive evaluation of these architectures, covering accuracy metrics, hyperparameters, and an appendix that contains a table outlining the parameters of commonly used CNN architectures for feature extraction from EEG signals.
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Affiliation(s)
- Ildar Rakhmatulin
- Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK; (A.N.)
| | - Minh-Son Dao
- National Institute of Information and Communications Technology (NICT), Tokyo 184-0015, Japan
| | - Amir Nassibi
- Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK; (A.N.)
| | - Danilo Mandic
- Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK; (A.N.)
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Bicknell B, Liebert A, Herkes G. Parkinson's Disease and Photobiomodulation: Potential for Treatment. J Pers Med 2024; 14:112. [PMID: 38276234 PMCID: PMC10819946 DOI: 10.3390/jpm14010112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 01/07/2024] [Accepted: 01/16/2024] [Indexed: 01/27/2024] Open
Abstract
Parkinson's disease is the second most common neurodegenerative disease and is increasing in incidence. The combination of motor and non-motor symptoms makes this a devastating disease for people with Parkinson's disease and their care givers. Parkinson's disease is characterised by mitochondrial dysfunction and neuronal death in the substantia nigra, a reduction in dopamine, accumulation of α-synuclein aggregates and neuroinflammation. The microbiome-gut-brain axis is also important in Parkinson's disease, involved in the spread of inflammation and aggregated α-synuclein. The mainstay of Parkinson's disease treatment is dopamine replacement therapy, which can reduce some of the motor signs. There is a need for additional treatment options to supplement available medications. Photobiomodulation (PBM) is a form of light therapy that has been shown to have multiple clinical benefits due to its enhancement of the mitochondrial electron transport chain and the subsequent increase in mitochondrial membrane potential and ATP production. PBM also modulates cellular signalling and has been shown to reduce inflammation. Clinically, PBM has been used for decades to improve wound healing, treat pain, reduce swelling and heal deep tissues. Pre-clinical experiments have indicated that PBM has the potential to improve the clinical signs of Parkinson's disease and to provide neuroprotection. This effect is seen whether the PBM is directed to the head of the animal or to other parts of the body (remotely). A small number of clinical trials has given weight to the possibility that using PBM can improve both motor and non-motor clinical signs and symptoms of Parkinson's disease and may potentially slow its progression.
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Affiliation(s)
- Brian Bicknell
- NICM Health Research Institute, University of Western Sydney, Westmead 2145, Australia;
| | - Ann Liebert
- NICM Health Research Institute, University of Western Sydney, Westmead 2145, Australia;
- Sydney Adventist Hospital, Wahroonga 2076, Australia
- Faculty of medicine and Health, Sydney University, Camperdown 2050, Australia
| | - Geoffrey Herkes
- Neurologist, Sydney Adventist Hospital, Wahroonga 2076, Australia;
- College of Health and Medicine, Australian National University, Canberra 2600, Australia
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Wong SB, Tsao Y, Tsai WH, Wang TS, Wu HC, Wang SS. Application of bidirectional long short-term memory network for prediction of cognitive age. Sci Rep 2023; 13:20197. [PMID: 37980387 PMCID: PMC10657465 DOI: 10.1038/s41598-023-47606-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 11/16/2023] [Indexed: 11/20/2023] Open
Abstract
Electroencephalography (EEG) measures changes in neuronal activity and can reveal significant changes from infancy to adulthood concomitant with brain maturation, making it a potential physiological marker of brain maturation and cognition. To investigate a promising deep learning tool for EEG classification, we applied the bidirectional long short-term memory (BLSTM) algorithm to analyze EEG data from the pediatric EEG laboratory of Taipei Tzu Chi Hospital. The trained BLSTM model was 86% accurate when identifying EEGs from young children (8 months-6 years) and adolescents (12-20 years). However, there was only a modest classification accuracy (69.3%) when categorizing EEG samples into three age groups (8 months-6 years, 6-12 years, and 12-20 years). For EEG samples from patients with intellectual disability, the prediction accuracy of the trained BLSTM model was 46.4%, which was significantly lower than its accuracy for EEGs from neurotypical patients, indicating that the individual's intelligence plays a major role in the age prediction. This study confirmed that scalp EEG can reflect brain maturation and the BLSTM algorithm is a feasible deep learning tool for the identification of cognitive age. The trained model can potentially be applied to clinical services as a supportive measurement of neurodevelopmental status.
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Affiliation(s)
- Shi-Bing Wong
- Department of Pediatrics, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei City, Taiwan.
- School of Medicine, Tzu Chi University, Hualien, Taiwan.
| | - Yu Tsao
- Research Center for Information Technology Innovation, Academia Sinica, Taipei, Taiwan
| | - Wen-Hsin Tsai
- Department of Pediatrics, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei City, Taiwan
- School of Medicine, Tzu Chi University, Hualien, Taiwan
| | - Tzong-Shi Wang
- School of Medicine, Tzu Chi University, Hualien, Taiwan
- Department of Psychiatry, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei City, Taiwan
| | - Hsin-Chi Wu
- School of Medicine, Tzu Chi University, Hualien, Taiwan
- Department of Physical Medicine and Rehabilitation, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei City, Taiwan
| | - Syu-Siang Wang
- Department of Electrical Engineering, Yuan Ze University, Taoyuan, Taiwan.
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Gour N, Hassan T, Owais M, Ganapathi II, Khanna P, Seghier ML, Werghi N. Transformers for autonomous recognition of psychiatric dysfunction via raw and imbalanced EEG signals. Brain Inform 2023; 10:25. [PMID: 37689601 PMCID: PMC10492733 DOI: 10.1186/s40708-023-00201-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 07/17/2023] [Indexed: 09/11/2023] Open
Abstract
Early identification of mental disorders, based on subjective interviews, is extremely challenging in the clinical setting. There is a growing interest in developing automated screening tools for potential mental health problems based on biological markers. Here, we demonstrate the feasibility of an AI-powered diagnosis of different mental disorders using EEG data. Specifically, this work aims to classify different mental disorders in the following ecological context accurately: (1) using raw EEG data, (2) collected during rest, (3) during both eye open, and eye closed conditions, (4) at short 2-min duration, (5) on participants with different psychiatric conditions, (6) with some overlapping symptoms, and (7) with strongly imbalanced classes. To tackle this challenge, we designed and optimized a transformer-based architecture, where class imbalance is addressed through focal loss and class weight balancing. Using the recently released TDBRAIN dataset (n= 1274 participants), our method classifies each participant as either a neurotypical or suffering from major depressive disorder (MDD), attention deficit hyperactivity disorder (ADHD), subjective memory complaints (SMC), or obsessive-compulsive disorder (OCD). We evaluate the performance of the proposed architecture on both the window-level and the patient-level. The classification of the 2-min raw EEG data into five classes achieved a window-level accuracy of 63.2% and 65.8% for open and closed eye conditions, respectively. When the classification is limited to three main classes (MDD, ADHD, SMC), window level accuracy improved to 75.1% and 69.9% for eye open and eye closed conditions, respectively. Our work paves the way for developing novel AI-based methods for accurately diagnosing mental disorders using raw resting-state EEG data.
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Affiliation(s)
- Neha Gour
- Khalifa University Center for Autonomous Robotic System and Cyber-Physical Security System Center, Department of Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi, United Arab Emirates.
| | - Taimur Hassan
- Khalifa University Center for Autonomous Robotic System and Cyber-Physical Security System Center, Department of Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi, United Arab Emirates
- Departement of Electrical and Computer Engineering, Abu Dhabi University, Abu Dhabi, United Arab Emirates
| | - Muhammad Owais
- Khalifa University Center for Autonomous Robotic System and Cyber-Physical Security System Center, Department of Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi, United Arab Emirates
| | - Iyyakutti Iyappan Ganapathi
- Khalifa University Center for Autonomous Robotic System and Cyber-Physical Security System Center, Department of Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi, United Arab Emirates
| | - Pritee Khanna
- Department of Computer Science and Engineering, Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, India
| | - Mohamed L Seghier
- Healthcare Engineering Innovation Center, Department of Biomedical Engineering, Khalifa University, Abu Dhabi, United Arab Emirates
| | - Naoufel Werghi
- Khalifa University Center for Autonomous Robotic System and Cyber-Physical Security System Center, Department of Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi, United Arab Emirates
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Vlieger R, Suominen H, Apthorp D, Lueck CJ, Daskalaki E. Evaluating methods of oversampling and averaging resting-state electroencephalography data in classifying Parkinson's disease . ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-5. [PMID: 38082678 DOI: 10.1109/embc40787.2023.10340819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Collecting resting-state electroencephalography (RSEEG) data is time-consuming and data sets are therefore often small. Because many machine learning (ML) algorithms work better with ample data, researchers looking to use RSEEG and ML to develop diagnostic models have used oversampling methods that may seem to contradict averaging methods used in conventional electroencephalography (EEG) research to improve the signal-to-noise ratio. Using eyes open (EO) and eyes closed (EC) recordings from 3 different research groups, we investigated the effect of different averaging and oversampling methods on classification metrics when classifying people with Parkinson's disease (PD) and controls. Both EC and EO recordings were used due to differences found between these methods. Our results indicated that grouping 58 electrodes into regions-of-interest (ROI) based on anatomical location is preferable to using single electrodes. Furthermore, although recording EO data led to slightly better classification, the number of data points for each participant was reduced and recordings for three participants entirely lost during pre-processing due to a higher level of artefacts than in the EC data.Clinical relevance- RSEEG is a potential biomarker for the diagnosis and prognostication of PD, but for RSEEG to have clinical relevance, it is necessary to establish which averaging and oversampling of data most reliably segregates the classes for people with PD and controls. We found that using of ROIs and EC data performed the best, as EO data was often contaminated with artefacts.
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Karakaş MF, Latifoğlu F. Distinguishing Parkinson's Disease with GLCM Features from the Hankelization of EEG Signals. Diagnostics (Basel) 2023; 13:diagnostics13101769. [PMID: 37238253 DOI: 10.3390/diagnostics13101769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 04/30/2023] [Accepted: 05/12/2023] [Indexed: 05/28/2023] Open
Abstract
This study proposes a novel method that uses electroencephalography (EEG) signals to classify Parkinson's Disease (PD) and demographically matched healthy control groups. The method utilizes the reduced beta activity and amplitude decrease in EEG signals that are associated with PD. The study involved 61 PD patients and 61 demographically matched controls groups, and EEG signals were recorded in various conditions (eyes closed, eyes open, eyes both open and closed, on-drug, off-drug) from three publicly available EEG data sources (New Mexico, Iowa, and Turku). The preprocessed EEG signals were classified using features obtained from gray-level co-occurrence matrix (GLCM) features through the Hankelization of EEG signals. The performance of classifiers with these novel features was evaluated using extensive cross-validations (CV) and leave-one-out cross-validation (LOOCV) schemes. This method under 10 × 10 fold CV, the method was able to differentiate PD groups from healthy control groups using a support vector machine (SVM) with an accuracy of 92.4 ± 0.01, 85.7 ± 0.02, and 77.1 ± 0.06 for New Mexico, Iowa, and Turku datasets, respectively. After a head-to-head comparison with state-of-the-art methods, this study showed an increase in the classification of PD and controls.
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Affiliation(s)
- Mehmet Fatih Karakaş
- Faculty of Engineering and Architecture, Department of Biomedical Engineering, Erzincan Binali Yildirim University, Erzincan 24002, Turkey
| | - Fatma Latifoğlu
- Faculty of Engineering, Department of Biomedical Engineering, Erciyes University, Kayseri 38280, Turkey
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Nogales A, García-Tejedor ÁJ, Chazarra P, Ugalde-Canitrot A. Discriminating and understanding brain states in children with epileptic spasms using deep learning and graph metrics analysis of brain connectivity. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 232:107427. [PMID: 36870168 DOI: 10.1016/j.cmpb.2023.107427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 02/13/2023] [Accepted: 02/15/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVE Epilepsy is a brain disorder consisting of abnormal electrical discharges of neurons resulting in epileptic seizures. The nature and spatial distribution of these electrical signals make epilepsy a field for the analysis of brain connectivity using artificial intelligence and network analysis techniques since their study requires large amounts of data over large spatial and temporal scales. For example, to discriminate states that would otherwise be indistinguishable from the human eye. This paper aims to identify the different brain states that appear concerning the intriguing seizure type of epileptic spasms. Once these states have been differentiated, an attempt is made to understand their corresponding brain activity. METHODS The representation of brain connectivity can be done by graphing the topology and intensity of brain activations. Graph images from different instants within and outside the actual seizure are used as input to a deep learning model for classification purposes. This work uses convolutional neural networks to discriminate the different states of the epileptic brain based on the appearance of these graphs at different times. Next, we apply several graph metrics as an aid to interpret what happens in the brain regions during and around the seizure. RESULTS Results show that the model consistently finds distinctive brain states in children with epilepsy with focal onset epileptic spasms that are indistinguishable under the expert visual inspection of EEG traces. Furthermore, differences are found in brain connectivity and network measures in each of the different states. CONCLUSIONS Computer-assisted discrimination using this model can detect subtle differences in the various brain states of children with epileptic spasms. The research reveals previously undisclosed information regarding brain connectivity and networks, allowing for a better understanding of the pathophysiology and evolving characteristics of this particular seizure type. From our data, we speculate that the prefrontal, premotor, and motor cortices could be more involved in a hypersynchronized state occurring in the few seconds immediately preceding the visually evident EEG and clinical ictal features of the first spasm in a cluster. On the other hand, a disconnection in centro-parietal areas seems a relevant feature in the predisposition and repetitive generation of epileptic spasms within clusters.
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Affiliation(s)
- Alberto Nogales
- CEIEC Research Institute, Universidad Francisco de Vitoria, Ctra. M-515 Pozuelo-Majadahonda km. 1,800, Pozuelo de Alarcón 28223, Spain.
| | - Álvaro J García-Tejedor
- CEIEC Research Institute, Universidad Francisco de Vitoria, Ctra. M-515 Pozuelo-Majadahonda km. 1,800, Pozuelo de Alarcón 28223, Spain
| | - Pedro Chazarra
- CEIEC Research Institute, Universidad Francisco de Vitoria, Ctra. M-515 Pozuelo-Majadahonda km. 1,800, Pozuelo de Alarcón 28223, Spain
| | - Arturo Ugalde-Canitrot
- School of Medicine. Universidad Francisco de Vitoria, Ctra. M-515 Pozuelo-Majadahonda km. 1,800. Pozuelo de Alarcón 28223, Spain; Epilepsy Unit, Neurology and Clinical Neurophysiology Service, Hospital Universitario La Paz, Paseo de la Castellana, 261, Madrid 28046, Spain
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Sushkova OS, Morozov AA, Kershner IA, Khokhlova MN, Gabova AV, Karabanov AV, Chigaleichick LA, Illarioshkin SN. Investigation of Phase Shifts Using AUC Diagrams: Application to Differential Diagnosis of Parkinson's Disease and Essential Tremor. SENSORS (BASEL, SWITZERLAND) 2023; 23:1531. [PMID: 36772568 PMCID: PMC9921843 DOI: 10.3390/s23031531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 01/20/2023] [Accepted: 01/23/2023] [Indexed: 06/18/2023]
Abstract
This study was motivated by the well-known problem of the differential diagnosis of Parkinson's disease and essential tremor using the phase shift between the tremor signals in the antagonist muscles of patients. Different phase shifts are typical for different diseases; however, it remains unclear how this parameter can be used for clinical diagnosis. Neurophysiological papers have reported different estimations of the accuracy of this parameter, which varies from insufficient to 100%. To address this issue, we developed special types of area under the ROC curve (AUC) diagrams and used them to analyze the phase shift. Different phase estimations, including the Hilbert instantaneous phase and the cross-wavelet spectrum mean phase, were applied. The results of the investigation of the clinical data revealed several regularities with opposite directions in the phase shift of the electromyographic signals in patients with Parkinson's disease and essential tremor. The detected regularities provide insights into the contradictory results reported in the literature. Moreover, the developed AUC diagrams show the potential for the investigation of neurodegenerative diseases related to the hyperkinetic movements of the extremities and the creation of high-accuracy methods of clinical diagnosis.
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Affiliation(s)
- Olga S. Sushkova
- Kotel’nikov Institute of Radio Engineering and Electronics of RAS, Mokhovaya 11-7, 125009 Moscow, Russia
| | - Alexei A. Morozov
- Kotel’nikov Institute of Radio Engineering and Electronics of RAS, Mokhovaya 11-7, 125009 Moscow, Russia
| | - Ivan A. Kershner
- Kotel’nikov Institute of Radio Engineering and Electronics of RAS, Mokhovaya 11-7, 125009 Moscow, Russia
| | - Margarita N. Khokhlova
- Kotel’nikov Institute of Radio Engineering and Electronics of RAS, Mokhovaya 11-7, 125009 Moscow, Russia
| | - Alexandra V. Gabova
- Institute of Higher Nervous Activity and Neurophysiology of RAS, Butlerova 5A, 117485 Moscow, Russia
| | - Alexei V. Karabanov
- FSBI “Research Center of Neurology”, Volokolamskoe Shosse 80, 125367 Moscow, Russia
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Rana A, Dumka A, Singh R, Panda MK, Priyadarshi N. A Computerized Analysis with Machine Learning Techniques for the Diagnosis of Parkinson's Disease: Past Studies and Future Perspectives. Diagnostics (Basel) 2022; 12:2708. [PMID: 36359550 PMCID: PMC9689408 DOI: 10.3390/diagnostics12112708] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 10/30/2022] [Accepted: 11/02/2022] [Indexed: 08/03/2023] Open
Abstract
According to the World Health Organization (WHO), Parkinson's disease (PD) is a neurodegenerative disease of the brain that causes motor symptoms including slower movement, rigidity, tremor, and imbalance in addition to other problems like Alzheimer's disease (AD), psychiatric problems, insomnia, anxiety, and sensory abnormalities. Techniques including artificial intelligence (AI), machine learning (ML), and deep learning (DL) have been established for the classification of PD and normal controls (NC) with similar therapeutic appearances in order to address these problems and improve the diagnostic procedure for PD. In this article, we examine a literature survey of research articles published up to September 2022 in order to present an in-depth analysis of the use of datasets, various modalities, experimental setups, and architectures that have been applied in the diagnosis of subjective disease. This analysis includes a total of 217 research publications with a list of the various datasets, methodologies, and features. These findings suggest that ML/DL methods and novel biomarkers hold promising results for application in medical decision-making, leading to a more methodical and thorough detection of PD. Finally, we highlight the challenges and provide appropriate recommendations on selecting approaches that might be used for subgrouping and connection analysis with structural magnetic resonance imaging (sMRI), DaTSCAN, and single-photon emission computerized tomography (SPECT) data for future Parkinson's research.
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Affiliation(s)
- Arti Rana
- Computer Science & Engineering, Veer Madho Singh Bhandari Uttarakhand Technical University, Dehradun 248007, Uttarakhand, India
| | - Ankur Dumka
- Department of Computer Science and Engineering, Women Institute of Technology, Dehradun 248007, Uttarakhand, India
- Department of Computer Science & Engineering, Graphic Era Deemed to be University, Dehradun 248001, Uttarakhand, India
| | - Rajesh Singh
- Division of Research and Innovation, Uttaranchal Institute of Technology, Uttaranchal University, Dehradun 248007, Uttarakhand, India
- Department of Project Management, Universidad Internacional Iberoamericana, Campeche 24560, Mexico
| | - Manoj Kumar Panda
- Department of Electrical Engineering, G.B. Pant Institute of Engineering and Technology, Pauri 246194, Uttarakhand, India
| | - Neeraj Priyadarshi
- Department of Electrical Engineering, JIS College of Engineering, Kolkata 741235, West Bengal, India
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