1
|
Dentamaro V, Impedovo D, Musti L, Pirlo G, Taurisano P. Enhancing early Parkinson's disease detection through multimodal deep learning and explainable AI: insights from the PPMI database. Sci Rep 2024; 14:20941. [PMID: 39251639 PMCID: PMC11385236 DOI: 10.1038/s41598-024-70165-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Accepted: 08/13/2024] [Indexed: 09/11/2024] Open
Abstract
Parkinson's is the second most common neurodegenerative disease, affecting nearly 8.5M people and steadily increasing. In this research, Multimodal Deep Learning is investigated for the Prodromal stage detection of Parkinson's Disease (PD), combining different 3D architectures with the novel Excitation Network (EN) and supported by Explainable Artificial Intelligence (XAI) techniques. Utilizing data from the Parkinson's Progression Markers Initiative, this study introduces a joint co-learning approach for multimodal fusion, enabling end-to-end training of deep neural networks and facilitating the learning of complementary information from both imaging and clinical modalities. DenseNet with EN outperformed other models, showing a substantial increase in accuracy when supplemented with clinical data. XAI methods, such as Integrated Gradients for ResNet and DenseNet, and Attention Heatmaps for Vision Transformer (ViT), revealed that DenseNet focused on brain regions believed to be critical to prodromal pathophysiology, including the right temporal and left pre-frontal areas. Similarly, ViT highlighted the lateral ventricles associated with cognitive decline, indicating their potential in the Prodromal stage. These findings underscore the potential of these regions as early-stage PD biomarkers and showcase the proposed framework's efficacy in predicting subtypes of PD and aiding in early diagnosis, paving the way for innovative diagnostic tools and precision medicine.
Collapse
Affiliation(s)
- Vincenzo Dentamaro
- Dipartimento di Informatica, University of Bari Aldo Moro, 70125, Bari, Italy.
| | - Donato Impedovo
- Dipartimento di Informatica, University of Bari Aldo Moro, 70125, Bari, Italy
| | - Luca Musti
- Dipartimento di Informatica, University of Bari Aldo Moro, 70125, Bari, Italy
| | - Giuseppe Pirlo
- Dipartimento di Informatica, University of Bari Aldo Moro, 70125, Bari, Italy
| | - Paolo Taurisano
- Dipartimento di Biomedicina Traslazionale e Neuroscienze (DiBraiN), University of Bari Aldo Moro, Bari, Italy
| |
Collapse
|
2
|
Umar TP, Jain N, Papageorgakopoulou M, Shaheen RS, Alsamhori JF, Muzzamil M, Kostiks A. Artificial intelligence for screening and diagnosis of amyotrophic lateral sclerosis: a systematic review and meta-analysis. Amyotroph Lateral Scler Frontotemporal Degener 2024; 25:425-436. [PMID: 38563056 DOI: 10.1080/21678421.2024.2334836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 03/04/2024] [Accepted: 03/18/2024] [Indexed: 04/04/2024]
Abstract
INTRODUCTION Amyotrophic lateral sclerosis (ALS) is a rare and fatal neurological disease that leads to progressive motor function degeneration. Diagnosing ALS is challenging due to the absence of a specific detection test. The use of artificial intelligence (AI) can assist in the investigation and treatment of ALS. METHODS We searched seven databases for literature on the application of AI in the early diagnosis and screening of ALS in humans. The findings were summarized using random-effects summary receiver operating characteristic curve. The risk of bias (RoB) analysis was carried out using QUADAS-2 or QUADAS-C tools. RESULTS In the 34 analyzed studies, a meta-prevalence of 47% for ALS was noted. For ALS detection, the pooled sensitivity of AI models was 94.3% (95% CI - 63.2% to 99.4%) with a pooled specificity of 98.9% (95% CI - 92.4% to 99.9%). For ALS classification, the pooled sensitivity of AI models was 90.9% (95% CI - 86.5% to 93.9%) with a pooled specificity of 92.3% (95% CI - 84.8% to 96.3%). Based on type of input for classification, the pooled sensitivity of AI models for gait, electromyography, and magnetic resonance signals was 91.2%, 92.6%, and 82.2%, respectively. The pooled specificity for gait, electromyography, and magnetic resonance signals was 94.1%, 96.5%, and 77.3%, respectively. CONCLUSIONS Although AI can play a significant role in the screening and diagnosis of ALS due to its high sensitivities and specificities, concerns remain regarding quality of evidence reported in the literature.
Collapse
Affiliation(s)
- Tungki Pratama Umar
- Department of Medical Profession, Faculty of Medicine, Universitas Sriwijaya, Palembang, Indonesia
| | - Nityanand Jain
- Faculty of Medicine, Riga Stradinš University, Riga, Latvia
| | | | | | | | - Muhammad Muzzamil
- Department of Public Health, Health Services Academy, Islamabad, Pakistan, and
| | - Andrejs Kostiks
- Department of Neurology, Riga East University Clinical Hospital, Riga, Latvia
| |
Collapse
|
3
|
Altham C, Zhang H, Pereira E. Machine learning for the detection and diagnosis of cognitive impairment in Parkinson's Disease: A systematic review. PLoS One 2024; 19:e0303644. [PMID: 38753740 PMCID: PMC11098383 DOI: 10.1371/journal.pone.0303644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Accepted: 04/29/2024] [Indexed: 05/18/2024] Open
Abstract
BACKGROUND Parkinson's Disease is the second most common neurological disease in over 60s. Cognitive impairment is a major clinical symptom, with risk of severe dysfunction up to 20 years post-diagnosis. Processes for detection and diagnosis of cognitive impairments are not sufficient to predict decline at an early stage for significant impact. Ageing populations, neurologist shortages and subjective interpretations reduce the effectiveness of decisions and diagnoses. Researchers are now utilising machine learning for detection and diagnosis of cognitive impairment based on symptom presentation and clinical investigation. This work aims to provide an overview of published studies applying machine learning to detecting and diagnosing cognitive impairment, evaluate the feasibility of implemented methods, their impacts, and provide suitable recommendations for methods, modalities and outcomes. METHODS To provide an overview of the machine learning techniques, data sources and modalities used for detection and diagnosis of cognitive impairment in Parkinson's Disease, we conducted a review of studies published on the PubMed, IEEE Xplore, Scopus and ScienceDirect databases. 70 studies were included in this review, with the most relevant information extracted from each. From each study, strategy, modalities, sources, methods and outcomes were extracted. RESULTS Literatures demonstrate that machine learning techniques have potential to provide considerable insight into investigation of cognitive impairment in Parkinson's Disease. Our review demonstrates the versatility of machine learning in analysing a wide range of different modalities for the detection and diagnosis of cognitive impairment in Parkinson's Disease, including imaging, EEG, speech and more, yielding notable diagnostic accuracy. CONCLUSIONS Machine learning based interventions have the potential to glean meaningful insight from data, and may offer non-invasive means of enhancing cognitive impairment assessment, providing clear and formidable potential for implementation of machine learning into clinical practice.
Collapse
Affiliation(s)
- Callum Altham
- Department of Computer Science, Edge Hill University, Ormskirk, Lancashire, United Kingdom
| | - Huaizhong Zhang
- Department of Computer Science, Edge Hill University, Ormskirk, Lancashire, United Kingdom
| | - Ella Pereira
- Department of Computer Science, Edge Hill University, Ormskirk, Lancashire, United Kingdom
| |
Collapse
|
4
|
Anjum MF, Espinoza AI, Cole RC, Singh A, May P, Uc EY, Dasgupta S, Narayanan NS. Resting-state EEG measures cognitive impairment in Parkinson's disease. NPJ Parkinsons Dis 2024; 10:6. [PMID: 38172519 PMCID: PMC10764756 DOI: 10.1038/s41531-023-00602-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 11/14/2023] [Indexed: 01/05/2024] Open
Abstract
Cognitive dysfunction is common in Parkinson's disease (PD). We developed and evaluated an EEG-based biomarker to index cognitive functions in PD from a few minutes of resting-state EEG. We hypothesized that synchronous changes in EEG across the power spectrum can measure cognition. We optimized a data-driven algorithm to efficiently capture these changes and index cognitive function in 100 PD and 49 control participants. We compared our EEG-based cognitive index with the Montreal cognitive assessment (MoCA) and cognitive tests across different domains from National Institutes of Health (NIH) Toolbox using cross-validations, regression models, and randomization tests. Finally, we externally validated our approach on 32 PD participants. We observed cognition-related changes in EEG over multiple spectral rhythms. Utilizing only 8 best-performing electrodes, our proposed index strongly correlated with cognition (MoCA: rho = 0.68, p value < 0.001; NIH-Toolbox cognitive tests: rho ≥ 0.56, p value < 0.001) outperforming traditional spectral markers (rho = -0.30-0.37). The index showed a strong fit in regression models (R2 = 0.46) with MoCA, yielded 80% accuracy in detecting cognitive impairment, and was effective in both PD and control participants. Notably, our approach was equally effective (rho = 0.68, p value < 0.001; MoCA) in out-of-sample testing. In summary, we introduced a computationally efficient data-driven approach for cross-domain cognition indexing using fewer than 10 EEG electrodes, potentially compatible with dynamic therapies like closed-loop neurostimulation. These results will inform next-generation neurophysiological biomarkers for monitoring cognition in PD and other neurological diseases.
Collapse
Affiliation(s)
- Md Fahim Anjum
- Department of Neurology, University of California San Francisco, San Francisco, CA, 94143, USA.
| | - Arturo I Espinoza
- Department of Neurology, The University of Iowa, Iowa city, IA, 52240, USA
| | - Rachel C Cole
- Department of Neurology, The University of Iowa, Iowa city, IA, 52240, USA
| | - Arun Singh
- Division of Basic Biomedical Sciences, Sanford School of Medicine, University of South Dakota, South Dakota, SD, 57069, USA
| | - Patrick May
- Department of Electrical and Computer Engineering, The University of Iowa, Iowa city, IA, 52240, USA
| | - Ergun Y Uc
- Department of Neurology, The University of Iowa, Iowa city, IA, 52240, USA
- Neurology Service, Iowa City VA Medical Center, Iowa city, IA, 52240, USA
| | - Soura Dasgupta
- Department of Electrical and Computer Engineering, The University of Iowa, Iowa city, IA, 52240, USA
| | | |
Collapse
|
5
|
Oliveira AM, Coelho L, Carvalho E, Ferreira-Pinto MJ, Vaz R, Aguiar P. Machine learning for adaptive deep brain stimulation in Parkinson's disease: closing the loop. J Neurol 2023; 270:5313-5326. [PMID: 37530789 PMCID: PMC10576725 DOI: 10.1007/s00415-023-11873-1] [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: 05/09/2023] [Revised: 07/08/2023] [Accepted: 07/10/2023] [Indexed: 08/03/2023]
Abstract
Parkinson's disease (PD) is the second most common neurodegenerative disease bearing a severe social and economic impact. So far, there is no known disease modifying therapy and the current available treatments are symptom oriented. Deep Brain Stimulation (DBS) is established as an effective treatment for PD, however current systems lag behind today's technological potential. Adaptive DBS, where stimulation parameters depend on the patient's physiological state, emerges as an important step towards "smart" DBS, a strategy that enables adaptive stimulation and personalized therapy. This new strategy is facilitated by currently available neurotechnologies allowing the simultaneous monitoring of multiple signals, providing relevant physiological information. Advanced computational models and analytical methods are an important tool to explore the richness of the available data and identify signal properties to close the loop in DBS. To tackle this challenge, machine learning (ML) methods applied to DBS have gained popularity due to their ability to make good predictions in the presence of multiple variables and subtle patterns. ML based approaches are being explored at different fronts such as the identification of electrophysiological biomarkers and the development of personalized control systems, leading to effective symptom relief. In this review, we explore how ML can help overcome the challenges in the development of closed-loop DBS, particularly its role in the search for effective electrophysiology biomarkers. Promising results demonstrate ML potential for supporting a new generation of adaptive DBS, with better management of stimulation delivery, resulting in more efficient and patient-tailored treatments.
Collapse
Affiliation(s)
- Andreia M Oliveira
- Faculdade de Engenharia da Universidade do Porto, Porto, Portugal
- Neuroengineering and Computational Neuroscience Lab, Instituto de Investigação e Inovação da Universidade do Porto, Porto, Portugal
| | - Luis Coelho
- Instituto Superior de Engenharia do Porto, Porto, Portugal
| | - Eduardo Carvalho
- Neuroengineering and Computational Neuroscience Lab, Instituto de Investigação e Inovação da Universidade do Porto, Porto, Portugal
- ICBAS-School of Medicine and Biomedical Sciences, University of Porto, Porto, Portugal
| | - Manuel J Ferreira-Pinto
- Centro Hospitalar Universitário de São João, Porto, Portugal
- Faculdade de Medicina da Universidade do Porto, Porto, Portugal
| | - Rui Vaz
- Centro Hospitalar Universitário de São João, Porto, Portugal
- Faculdade de Medicina da Universidade do Porto, Porto, Portugal
| | - Paulo Aguiar
- Faculdade de Engenharia da Universidade do Porto, Porto, Portugal.
- Neuroengineering and Computational Neuroscience Lab, Instituto de Investigação e Inovação da Universidade do Porto, Porto, Portugal.
- Faculdade de Medicina da Universidade do Porto, Porto, Portugal.
- i3S-Instituto de Investigação e Inovação em Saúde, Rua Alfredo Allen, 208, 4200-135, Porto, Portugal.
| |
Collapse
|
6
|
Lilhore UK, Dalal S, Faujdar N, Margala M, Chakrabarti P, Chakrabarti T, Simaiya S, Kumar P, Thangaraju P, Velmurugan H. Hybrid CNN-LSTM model with efficient hyperparameter tuning for prediction of Parkinson's disease. Sci Rep 2023; 13:14605. [PMID: 37669970 PMCID: PMC10480168 DOI: 10.1038/s41598-023-41314-y] [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: 02/21/2023] [Accepted: 08/24/2023] [Indexed: 09/07/2023] Open
Abstract
The patients' vocal Parkinson's disease (PD) changes could be identified early on, allowing for management before physically incapacitating symptoms appear. In this work, static as well as dynamic speech characteristics that are relevant to PD identification are examined. Speech changes or communication issues are among the challenges that Parkinson's individuals may encounter. As a result, avoiding the potential consequences of speech difficulties brought on by the condition depends on getting the appropriate diagnosis early. PD patients' speech signals change significantly from those of healthy individuals. This research presents a hybrid model utilizing improved speech signals with dynamic feature breakdown using CNN and LSTM. The proposed hybrid model employs a new, pre-trained CNN with LSTM to recognize PD in linguistic features utilizing Mel-spectrograms derived from normalized voice signal and dynamic mode decomposition. The proposed Hybrid model works in various phases, which include Noise removal, extraction of Mel-spectrograms, feature extraction using pre-trained CNN model ResNet-50, and the final stage is applied for classification. An experimental analysis was performed using the PC-GITA disease dataset. The proposed hybrid model is compared with traditional NN and well-known machine learning-based CART and SVM & XGBoost models. The accuracy level achieved in Neural Network, CART, SVM, and XGBoost models is 72.69%, 84.21%, 73.51%, and 90.81%. The results show that under these four machine approaches of tenfold cross-validation and dataset splitting without samples overlapping one individual, the proposed hybrid model achieves an accuracy of 93.51%, significantly outperforming traditional ML models utilizing static features in detecting Parkinson's disease.
Collapse
Affiliation(s)
- Umesh Kumar Lilhore
- Department of Computer Science and Engineering, Chandigarh University, Chandigarh, Punjab, India
| | - Surjeet Dalal
- Amity School of Engineering and Technology, Amity University Haryana, Gurugram, India
| | - Neetu Faujdar
- Department of Computer Engineering and Application, GLA University, Mathura, Uttar Pradesh, India
| | - Martin Margala
- School of Computing and Informatics, University of Louisiana at Lafayette, Lafayette, USA
| | - Prasun Chakrabarti
- Department of Computer Science and Engineering, Sir Padampat Singhania University, Udaipur, 313601, Rajasthan, India
| | | | - Sarita Simaiya
- Department of Computer Science and Engineering, Chandigarh University, Chandigarh, Punjab, India
- Apex Institute of Technology, Department of Computer Science and Engineering, Chandigarh University, Mohali, Punjab, India
| | - Pawan Kumar
- Department of Computer Science and Engineering, Chandigarh University, Chandigarh, Punjab, India
- College of Computing Sciences & IT, Teerthanker Mahaveer University, Moradabad, Uttar Pradesh, India
| | - Pugazhenthan Thangaraju
- Department of Pharmacology, All India Institute of Medical Sciences, Raipur, Chhattisgarh, India.
| | - Hemasri Velmurugan
- Department of Pharmacology, All India Institute of Medical Sciences, Raipur, Chhattisgarh, India
| |
Collapse
|
7
|
Gupta R, Kumari S, Senapati A, Ambasta RK, Kumar P. New era of artificial intelligence and machine learning-based detection, diagnosis, and therapeutics in Parkinson's disease. Ageing Res Rev 2023; 90:102013. [PMID: 37429545 DOI: 10.1016/j.arr.2023.102013] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 06/26/2023] [Accepted: 07/06/2023] [Indexed: 07/12/2023]
Abstract
Parkinson's disease (PD) is characterized by the loss of neuronal cells, which leads to synaptic dysfunction and cognitive defects. Despite the advancements in treatment strategies, the management of PD is still a challenging event. Early prediction and diagnosis of PD are of utmost importance for effective management of PD. In addition, the classification of patients with PD as compared to normal healthy individuals also imposes drawbacks in the early diagnosis of PD. To address these challenges, artificial intelligence (AI) and machine learning (ML) models have been implicated in the diagnosis, prediction, and treatment of PD. Recent times have also demonstrated the implication of AI and ML models in the classification of PD based on neuroimaging methods, speech recording, gait abnormalities, and others. Herein, we have briefly discussed the role of AI and ML in the diagnosis, treatment, and identification of novel biomarkers in the progression of PD. We have also highlighted the role of AI and ML in PD management through altered lipidomics and gut-brain axis. We briefly explain the role of early PD detection through AI and ML algorithms based on speech recordings, handwriting patterns, gait abnormalities, and neuroimaging techniques. Further, the review discuss the potential role of the metaverse, the Internet of Things, and electronic health records in the effective management of PD to improve the quality of life. Lastly, we also focused on the implementation of AI and ML-algorithms in neurosurgical process and drug discovery.
Collapse
Affiliation(s)
- Rohan Gupta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological, University, USA.
| | - Smita Kumari
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological, University, USA
| | | | - Rashmi K Ambasta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological, University, USA
| | - Pravir Kumar
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological, University, USA.
| |
Collapse
|
8
|
Redhya M, Sathesh Kumar K. Refining PD classification through ensemble bionic machine learning architecture with adaptive threshold based image denoising. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
|
9
|
Anjum MF, Espinoza A, Cole R, Singh A, May P, Uc E, Dasgupta S, Narayanan N. Resting-state EEG measures cognitive impairment in Parkinson's disease. RESEARCH SQUARE 2023:rs.3.rs-2666578. [PMID: 36993450 PMCID: PMC10055637 DOI: 10.21203/rs.3.rs-2666578/v1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Background Cognitive dysfunction is common in Parkinson's disease (PD) and is diagnosed by complex, time-consuming psychometric tests which are affected by language and education, subject to learning effects, and not suitable for continuous monitoring of cognition. Objectives We developed and evaluated an EEG-based biomarker to index cognitive functions in PD from a few minutes of resting-state EEG. Methods We hypothesized that synchronous changes in EEG across the power spectrum can measure cognition. We optimized a data-driven algorithm to efficiently capture these changes and index cognitive function in 100 PD and 49 control participants. We compared our EEG-based cognitive index with the Montreal cognitive assessment (MoCA) and cognitive tests across different domains from the National Institutes of Health (NIH) Toolbox using cross-validation schemes, regression models, and randomization tests. Results We observed cognition-related changes in EEG activities over multiple spectral rhythms. Utilizing only 8 best-performing EEG electrodes, our proposed index strongly correlated with cognition (rho = 0.68, p value < 0.001 with MoCA; rho ≥ 0.56, p value < 0.001 with cognitive tests from the NIH Toolbox) outperforming traditional spectral markers (rho = -0.30 - 0.37). The index showed a strong fit in regression models (R2 = 0.46) with MoCA, yielded 80% accuracy in detecting cognitive impairment, and was effective in both PD and control participants. Conclusions Our approach is computationally efficient for real-time indexing of cognition across domains, implementable even in hardware with limited computing capabilities, making it potentially compatible with dynamic therapies such as closed-loop neurostimulation, and will inform next-generation neurophysiological biomarkers for monitoring cognition in PD and other neurological diseases.
Collapse
|
10
|
Tannemaat MR, Kefalas M, Geraedts VJ, Remijn-Nelissen L, Verschuuren AJM, Koch M, Kononova AV, Wang H, Bäck THW. Distinguishing normal, neuropathic and myopathic EMG with an automated machine learning approach. Clin Neurophysiol 2023; 146:49-54. [PMID: 36535091 DOI: 10.1016/j.clinph.2022.11.019] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 10/30/2022] [Accepted: 11/26/2022] [Indexed: 12/13/2022]
Abstract
OBJECTIVE Distinguishing normal, neuropathic and myopathic electromyography (EMG) traces can be challenging. We aimed to create an automated time series classification algorithm. METHODS EMGs of healthy controls (HC, n = 25), patients with amyotrophic lateral sclerosis (ALS, n = 20) and inclusion body myositis (IBM, n = 20), were retrospectively selected based on longitudinal clinical follow-up data (ALS and HC) or muscle biopsy (IBM). A machine learning pipeline was applied based on 5-second EMG fragments of each muscle. Diagnostic yield expressed as area under the curve (AUC) of a receiver-operator characteristics curve, accuracy, sensitivity, and specificity were determined per muscle (muscle-level) and per patient (patient-level). RESULTS Diagnostic yield of the classification ALS vs. HC was: AUC 0.834 ± 0.014 at muscle-level and 0.856 ± 0.009 at patient-level. For the classification HC vs. IBM, AUC was 0.744 ± 0.043 at muscle-level and 0.735 ± 0.029 at patient-level. For the classification ALS vs. IBM, AUC was 0.569 ± 0.024 at muscle-level and 0.689 ± 0.035 at patient-level. CONCLUSIONS An automated time series classification algorithm can distinguish EMGs from healthy individuals from those of patients with ALS with a high diagnostic yield. Using longer EMG fragments with different levels of muscle activation may improve performance. SIGNIFICANCE In the future, machine learning algorithms may help improve the diagnostic accuracy of EMG examinations.
Collapse
Affiliation(s)
- M R Tannemaat
- Leiden University Medical Centre, Department of Neurology, The Netherlands.
| | - M Kefalas
- Leiden Institute of Advanced Computer Science, The Netherlands
| | - V J Geraedts
- Leiden University Medical Centre, Department of Neurology, The Netherlands; Leiden University Medical Centre, Department of Clinical Epidemiology, The Netherlands
| | - L Remijn-Nelissen
- Leiden University Medical Centre, Department of Neurology, The Netherlands
| | - A J M Verschuuren
- Leiden University Medical Centre, Department of Neurology, The Netherlands
| | - M Koch
- Leiden Institute of Advanced Computer Science, The Netherlands
| | - A V Kononova
- Leiden Institute of Advanced Computer Science, The Netherlands
| | - H Wang
- Leiden Institute of Advanced Computer Science, The Netherlands
| | - T H W Bäck
- Leiden Institute of Advanced Computer Science, The Netherlands
| |
Collapse
|
11
|
Chawla P, Rana SB, Kaur H, Singh K, Yuvaraj R, Murugappan M. A decision support system for automated diagnosis of Parkinson’s disease from EEG using FAWT and entropy features. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
|
12
|
Luján MÁ, Mateo Sotos J, Torres A, Santos JL, Quevedo O, Borja AL. Mental Disorder Diagnosis from EEG Signals Employing Automated Leaning Procedures Based on Radial Basis Functions. J Med Biol Eng 2022; 42:853-859. [PMID: 36407571 PMCID: PMC9651124 DOI: 10.1007/s40846-022-00758-9] [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: 02/05/2022] [Accepted: 10/21/2022] [Indexed: 11/13/2022]
Abstract
Purpose In this paper, a new automated procedure based on deep learning methods for schizophrenia diagnosis is presented. Methods To this aim, electroencephalogram signals obtained using a 32-channel helmet are prominently used to analyze high temporal resolution information from the brain. By these means, the data collected is employed to evaluate the class likelihoods using a neuronal network based on radial basis functions and a fuzzy means algorithm. Results The results obtained with real datasets validate the high accuracy of the proposed classification method. Thus, effectively characterizing the changes in EEG signals acquired from schizophrenia patients and healthy volunteers. More specifically, values of accuracy better than 93% has been obtained in the present research. Additionally, a comparative study with other approaches based on well-knows machine learning methods shows that the proposed method provides better results than recently proposed algorithms in schizophrenia detection. Conclusion The proposed method can be used as a diagnostic tool in the detection of the schizophrenia, helping for early diagnosis and treatment.
Collapse
Affiliation(s)
- Miguel Ángel Luján
- Departamento de Ingeniería Eléctrica, Automática y Comunicaciones, Universidad de Castilla-La Mancha, Electrónica, 02071 Albacete, Spain
| | - Jorge Mateo Sotos
- Instituto de Tecnología, Construcción y Telecomunicaciones, Universidad de Castilla-La Mancha, 16071 Cuenca, Spain
| | - Ana Torres
- Instituto de Tecnología, Construcción y Telecomunicaciones, Universidad de Castilla-La Mancha, 16071 Cuenca, Spain
| | - José L. Santos
- Servicio de Psiquiatría, Hospital Virgen de la Luz, 16002 Cuenca, Spain
| | - Oscar Quevedo
- KTH Royal Institute of Technology, 114 28 Stockholm, Sweden
| | - Alejandro L. Borja
- Departamento de Ingeniería Eléctrica, Automática y Comunicaciones, Universidad de Castilla-La Mancha, Electrónica, 02071 Albacete, Spain
| |
Collapse
|
13
|
Saudargiene A, Radziunas A, Dainauskas JJ, Kucinskas V, Vaitkiene P, Pranckeviciene A, Laucius O, Tamasauskas A, Deltuva V. Radiomic features of amygdala nuclei and hippocampus subfields help to predict subthalamic deep brain stimulation motor outcomes for Parkinson‘s disease patients. Front Neurosci 2022; 16:1028996. [PMID: 36312034 PMCID: PMC9606748 DOI: 10.3389/fnins.2022.1028996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 09/26/2022] [Indexed: 11/13/2022] Open
Abstract
Background and purposeThe aim of the study is to predict the subthalamic nucleus (STN) deep brain stimulation (DBS) outcomes for Parkinson’s disease (PD) patients using the radiomic features extracted from pre-operative magnetic resonance images (MRI).MethodsThe study included 34 PD patients who underwent DBS implantation in the STN. Five patients (15%) showed poor DBS motor outcome. All together 9 amygdalar nuclei and 12 hippocampus subfields were segmented using Freesurfer 7.0 pipeline from pre-operative MRI images. Furthermore, PyRadiomics platform was used to extract 120 radiomic features for each nuclei and subfield resulting in 5,040 features. Minimum Redundancy Maximum Relevance (mRMR) feature selection method was employed to reduce the number of features to 20, and 8 machine learning methods (regularized binary logistic regression (LR), decision tree classifier (DT), linear discriminant analysis (LDA), naive Bayes classifier (NB), kernel support vector machine (SVM), deep feed-forward neural network (DNN), one-class support vector machine (OC-SVM), feed-forward neural network-based autoencoder for anomaly detection (DNN-A)) were applied to build the models for poor vs. good and very good STN-DBS motor outcome prediction.ResultsThe highest mean prediction accuracy was obtained using regularized LR (96.65 ± 7.24%, AUC 0.98 ± 0.06) and DNN (87.25 ± 14.80%, AUC 0.87 ± 0.18).ConclusionThe results show the potential power of the radiomic features extracted from hippocampus and amygdala MRI in the prediction of STN-DBS motor outcomes for PD patients.
Collapse
Affiliation(s)
- Ausra Saudargiene
- Neuroscience Institute, Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
- *Correspondence: Ausra Saudargiene,
| | - Andrius Radziunas
- Department of Neurosurgery, Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Justinas J. Dainauskas
- Neuroscience Institute, Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Vytautas Kucinskas
- Neuroscience Institute, Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Paulina Vaitkiene
- Neuroscience Institute, Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Aiste Pranckeviciene
- Department of Health Psychology, Faculty of Public Health, Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
- Department of Neurology, Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Ovidijus Laucius
- Department of Neurology, Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Arimantas Tamasauskas
- Neuroscience Institute, Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
- Department of Neurosurgery, Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Vytenis Deltuva
- Neuroscience Institute, Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
- Department of Neurosurgery, Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
| |
Collapse
|
14
|
Survey of Machine Learning Techniques in the Analysis of EEG Signals for Parkinson’s Disease: A Systematic Review. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12146967] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background: Parkinson’s disease (PD) affects 7–10 million people worldwide. Its diagnosis is clinical and can be supported by image-based tests, which are expensive and not always accessible. Electroencephalograms (EEG) are non-invasive, widely accessible, low-cost tests. However, the signals obtained are difficult to analyze visually, so advanced techniques, such as Machine Learning (ML), need to be used. In this article, we review those studies that consider ML techniques to study the EEG of patients with PD. Methods: The review process was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, which are used to provide quality standards for the objective evaluation of various studies. All publications before February 2022 were included, and their main characteristics and results were evaluated and documented through three key points associated with the development of ML techniques: dataset quality, data preprocessing, and model evaluation. Results: 59 studies were included. The predominating models were Support Vector Machine (SVM) and Artificial Neural Networks (ANNs). In total, 31 articles diagnosed PD with a mean accuracy of 97.35 ± 3.46%. There was no standard cleaning protocol for EEG and a great heterogeneity in EEG characteristics was shown, although spectral features predominated by 88.37%. Conclusions: Neither the cleaning protocol nor the number of EEG channels influenced the classification results. A baseline value was provided for the PD diagnostic problem, although recent studies focus on the identification of cognitive impairment.
Collapse
|
15
|
EEG-Based Identification of Emotional Neural State Evoked by Virtual Environment Interaction. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19042158. [PMID: 35206341 PMCID: PMC8872045 DOI: 10.3390/ijerph19042158] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 02/11/2022] [Accepted: 02/11/2022] [Indexed: 11/17/2022]
Abstract
Classifying emotional states is critical for brain–computer interfaces and psychology-related domains. In previous studies, researchers have tried to identify emotions using neural data such as electroencephalography (EEG) signals or brain functional magnetic resonance imaging (fMRI). In this study, we propose a machine learning framework for emotion state classification using EEG signals in virtual reality (VR) environments. To arouse emotional neural states in brain signals, we provided three VR stimuli scenarios to 15 participants. Fifty-four features were extracted from the collected EEG signals under each scenario. To find the optimal classification in our research design, three machine learning algorithms (XGBoost classifier, support vector classifier, and logistic regression) were applied. Additionally, various class conditions were used in machine learning classifiers to validate the performance of our framework. To evaluate the classification performance, we utilized five evaluation metrics (precision, recall, f1-score, accuracy, and AUROC). Among the three classifiers, the XGBoost classifiers showed the best performance under all experimental conditions. Furthermore, the usability of features, including differential asymmetry and frequency band pass categories, were checked from the feature importance of XGBoost classifiers. We expect that our framework can be applied widely not only to psychological research but also to mental health-related issues.
Collapse
|
16
|
Nami M, Thatcher R, Kashou N, Lopes D, Lobo M, Bolanos JF, Morris K, Sadri M, Bustos T, Sanchez GE, Mohd-Yusof A, Fiallos J, Dye J, Guo X, Peatfield N, Asiryan M, Mayuku-Dore A, Krakauskaite S, Soler EP, Cramer SC, Besio WG, Berenyi A, Tripathi M, Hagedorn D, Ingemanson M, Gombosev M, Liker M, Salimpour Y, Mortazavi M, Braverman E, Prichep LS, Chopra D, Eliashiv DS, Hariri R, Tiwari A, Green K, Cormier J, Hussain N, Tarhan N, Sipple D, Roy M, Yu JS, Filler A, Chen M, Wheeler C, Ashford JW, Blum K, Zelinsky D, Yamamoto V, Kateb B. A Proposed Brain-, Spine-, and Mental- Health Screening Methodology (NEUROSCREEN) for Healthcare Systems: Position of the Society for Brain Mapping and Therapeutics. J Alzheimers Dis 2022; 86:21-42. [PMID: 35034899 DOI: 10.3233/jad-215240] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
The COVID-19 pandemic has accelerated neurological, mental health disorders, and neurocognitive issues. However, there is a lack of inexpensive and efficient brain evaluation and screening systems. As a result, a considerable fraction of patients with neurocognitive or psychobehavioral predicaments either do not get timely diagnosed or fail to receive personalized treatment plans. This is especially true in the elderly populations, wherein only 16% of seniors say they receive regular cognitive evaluations. Therefore, there is a great need for development of an optimized clinical brain screening workflow methodology like what is already in existence for prostate and breast exams. Such a methodology should be designed to facilitate objective early detection and cost-effective treatment of such disorders. In this paper we have reviewed the existing clinical protocols, recent technological advances and suggested reliable clinical workflows for brain screening. Such protocols range from questionnaires and smartphone apps to multi-modality brain mapping and advanced imaging where applicable. To that end, the Society for Brain Mapping and Therapeutics (SBMT) proposes the Brain, Spine and Mental Health Screening (NEUROSCREEN) as a multi-faceted approach. Beside other assessment tools, NEUROSCREEN employs smartphone guided cognitive assessments and quantitative electroencephalography (qEEG) as well as potential genetic testing for cognitive decline risk as inexpensive and effective screening tools to facilitate objective diagnosis, monitor disease progression, and guide personalized treatment interventions. Operationalizing NEUROSCREEN is expected to result in reduced healthcare costs and improving quality of life at national and later, global scales.
Collapse
Affiliation(s)
- Mohammad Nami
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA.,Neuroscience Center, Instituto de Investigaciones Científicas y Servicios de Alta Tecnología (INDICASAT AIP), City of Knowledge, Panama.,Department of Neuroscience, School of Advanced Medical Sciences and Technologies, and Dana Brain Health Institute, Shiraz University of Medical Sciences, Shiraz, Iran.,Inclusive Brain Health and BrainLabs International, Swiss Alternative Medicine, Geneva, Switzerland
| | - Robert Thatcher
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Applied Neuroscience, Inc., St Petersburg, FL, USA
| | - Nasser Kashou
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA
| | - Dahabada Lopes
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA
| | - Maria Lobo
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA
| | - Joe F Bolanos
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA
| | - Kevin Morris
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA
| | - Melody Sadri
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA
| | - Teshia Bustos
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA
| | - Gilberto E Sanchez
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA
| | - Alena Mohd-Yusof
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA
| | - John Fiallos
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA
| | - Justin Dye
- Department of Neurosurgery, Loma Linda University, Loma Linda, CA, USA
| | - Xiaofan Guo
- Department of Neurology, Loma Linda University, CA, USA
| | | | - Milena Asiryan
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA
| | - Alero Mayuku-Dore
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA
| | - Solventa Krakauskaite
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA
| | - Ernesto Palmero Soler
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA
| | - Steven C Cramer
- Department of Neurology, UCLA, and California Rehabilitation Institute, Los Angeles, CA, USA
| | - Walter G Besio
- Electrical Computer and Biomedical Engineering Department and Interdisciplinary Neuroscience Program, University of Rhode Island, RI, USA
| | - Antal Berenyi
- The Neuroscience Institute, New York University, New York, NY, USA
| | - Manjari Tripathi
- Department of Neurology, All India Institute of Medical Sciences, New Delhi, India
| | | | | | | | - Mark Liker
- Department of Neurosurgery, Children's Hospital Los Angeles, Los Angeles, CA, USA
| | - Yousef Salimpour
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | | | | | | | | | - Dawn S Eliashiv
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,UCLA David Geffen, School of Medicine, Department of Neurology, Los Angeles, CA, USA
| | - Robert Hariri
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA.,Celularity Corporation, Warren, NJ, USA.,Weill Cornell School of Medicine, Department of Neurosurgery, New York, NY, USA.,Brain Technology and Innovation Park, Los Angeles, CA, USA
| | - Ambooj Tiwari
- Departments of Neurology, Radiology & Neurosurgery - NYU Grossman School of Medicine, New York, NY, USA
| | - Ken Green
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA
| | - Jason Cormier
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA.,Lafayette Surgical Specialty Hospital, Lafayette, LA, USA
| | - Namath Hussain
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Department of Psychiatry, Faculty of Medicine, Uskudar University, Turkey
| | - Nevzat Tarhan
- Department of Psychiatry, Faculty of Medicine, Uskudar University, Turkey
| | - Daniel Sipple
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA.,Midwest Spine and Brain Institute, Roseville, MN, USA
| | - Michael Roy
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA.,Uniformed Services University Health Science (USUHS), Baltimore, MD, USA
| | - John S Yu
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Aaron Filler
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Institute for Nerve Medicine, Santa Monica, CA, USA.,Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Mike Chen
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Department of Neurosurgery, City of Hope Comprehensive Cancer Center, Duarte, CA, USA
| | - Chris Wheeler
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA
| | | | - Kenneth Blum
- Division of Addiction Research, Center for Psychiatry, Medicine, and Primary Care, Western Health Sciences, Pomona, CA, USA
| | | | - Vicky Yamamoto
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA.,USC Keck School of Medicine, The USC Caruso Department of Otolaryngology-Head and Neck Surgery, Los Angeles, CA, USA.,USC-Norris Comprehensive Cancer Center, Los Angeles, CA, USA
| | - Babak Kateb
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA.,Loma Linda University, Department of Neurosurgery, Loma Linda, CA, USA.,National Center for NanoBioElectronic (NCNBE), Los Angeles, CA, USA.,Brain Technology and Innovation Park, Los Angeles, CA, USA
| |
Collapse
|
17
|
Yao S, Zhu J, Li S, Zhang R, Zhao J, Yang X, Wang Y. Bibliometric Analysis of Quantitative Electroencephalogram Research in Neuropsychiatric Disorders From 2000 to 2021. Front Psychiatry 2022; 13:830819. [PMID: 35677873 PMCID: PMC9167960 DOI: 10.3389/fpsyt.2022.830819] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 04/05/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND With the development of quantitative electroencephalography (QEEG), an increasing number of studies have been published on the clinical use of QEEG in the past two decades, particularly in the diagnosis, treatment, and prognosis of neuropsychiatric disorders. However, to date, the current status and developing trends of this research field have not been systematically analyzed from a macroscopic perspective. The present study aimed to identify the hot spots, knowledge base, and frontiers of QEEG research in neuropsychiatric disorders from 2000 to 2021 through bibliometric analysis. METHODS QEEG-related publications in the neuropsychiatric field from 2000 to 2021 were retrieved from the Web of Science Core Collection (WOSCC). CiteSpace and VOSviewer software programs, and the online literature analysis platform (bibliometric.com) were employed to perform bibliographic and visualized analysis. RESULTS A total of 1,904 publications between 2000 and 2021 were retrieved. The number of QEEG-related publications in neuropsychiatric disorders increased steadily from 2000 to 2021, and research in psychiatric disorders requires more attention in comparison to research in neurological disorders. During the last two decades, QEEG has been mainly applied in neurodegenerative diseases, cerebrovascular diseases, and mental disorders to reveal the pathological mechanisms, assist clinical diagnosis, and promote the selection of effective treatments. The recent hot topics focused on QEEG utilization in neurodegenerative disorders like Alzheimer's and Parkinson's disease, traumatic brain injury and related cerebrovascular diseases, epilepsy and seizure, attention-deficit hyperactivity disorder, and other mental disorders like major depressive disorder and schizophrenia. In addition, studies to cross-validate QEEG biomarkers, develop new biomarkers (e.g., functional connectivity and complexity), and extract compound biomarkers by machine learning were the emerging trends. CONCLUSION The present study integrated bibliometric information on the current status, the knowledge base, and future directions of QEEG studies in neuropsychiatric disorders from a macroscopic perspective. It may provide valuable insights for researchers focusing on the utilization of QEEG in this field.
Collapse
Affiliation(s)
- Shun Yao
- Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China.,Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Jieying Zhu
- Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Shuiyan Li
- Department of Rehabilitation Medicine, School of Rehabilitation Medicine, Southern Medical University, Guangzhou, China
| | - Ruibin Zhang
- Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Jiubo Zhao
- Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China.,Department of Psychiatry, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Xueling Yang
- Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China.,Department of Psychiatry, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - You Wang
- Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China.,Department of Psychiatry, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| |
Collapse
|
18
|
Saleh C, Meyer A, Chaturvedi M, Beltrani S, Gschwandtner U, Fuhr P. Does Quantitative Electroencephalography Refine Preoperative Cognitive Assessment in Parkinson's Disease Patients Treated with Deep Brain Stimulation? A Follow-Up Study. Dement Geriatr Cogn Disord 2021; 50:349-356. [PMID: 34569496 DOI: 10.1159/000519053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 07/30/2021] [Indexed: 11/19/2022] Open
Abstract
OBJECTIVE Deep brain stimulation (DBS) in Parkinson's disease (PD) is associated with an increased risk of post-operative cognitive deterioration. Preoperative neuropsychological testing can be affected and limited by the patient's collaboration in advanced disease. The purpose of this study was to determine whether preoperative quantitative electroencephalography (qEEG) may be a useful complementary examination technique during preoperative assessment to predict cognitive changes in PD patients treated with DBS. METHODS We compared the cognitive performance of 16 PD patients who underwent bilateral subthalamic nucleus DBS to the performance of 15 PD controls (matched for age, sex, and education) at baseline and at 24 months. Cognitive scores were calculated for all patients across 5 domains. A preoperative 256-channel resting EEG was recorded from each patient. We computed the global relative power spectra. Correlation and linear regression models were used to assess associations of preoperative EEG measures with post-operative cognitive scores. RESULTS Slow waves (relative delta and theta band power) were negatively correlated with post-operative cognitive performance, while faster waves (alpha 1) were strongly positively correlated with the same scores (the overall cognitive score, attention, and executive function). Linear models revealed an association of delta power with the overall cognitive score (p = 0.00409, adjusted R2 = 0.6341). Verbal fluency (VF) showed a significant decline after DBS surgery, which was correlated with qEEG measures. CONCLUSIONS To analyse the side effects after DBS in PD patients, the most important parameter is verbal fluency capacity. In addition, correlation with EEG frequency bands might be useful to detect particularly vulnerable patients for cognitive impairment and be supportive in the selection process of patients considered for DBS.
Collapse
Affiliation(s)
- Christian Saleh
- Department of Neurophysiology and Neurology, University Hospital Basel, Basel, Switzerland
| | - Antonia Meyer
- Department of Neurophysiology and Neurology, University Hospital Basel, Basel, Switzerland
| | - Menorca Chaturvedi
- Department of Neurophysiology and Neurology, University Hospital Basel, Basel, Switzerland
| | - Selina Beltrani
- Department of Neurophysiology and Neurology, University Hospital Basel, Basel, Switzerland
| | - Ute Gschwandtner
- Department of Neurophysiology and Neurology, University Hospital Basel, Basel, Switzerland
| | - Peter Fuhr
- Department of Neurophysiology and Neurology, University Hospital Basel, Basel, Switzerland
| |
Collapse
|
19
|
Peralta M, Jannin P, Baxter JSH. Machine learning in deep brain stimulation: A systematic review. Artif Intell Med 2021; 122:102198. [PMID: 34823832 DOI: 10.1016/j.artmed.2021.102198] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 09/23/2021] [Accepted: 10/12/2021] [Indexed: 11/16/2022]
Abstract
Deep Brain Stimulation (DBS) is an increasingly common therapy for a large range of neurological disorders, such as abnormal movement disorders. The effectiveness of DBS in terms of controlling patient symptomatology has made this procedure increasingly used over the past few decades. Concurrently, the popularity of Machine Learning (ML), a subfield of artificial intelligence, has skyrocketed and its influence has more recently extended to medical domains such as neurosurgery. Despite its growing research interest, there has yet to be a literature review specifically on the use of ML in DBS. We have followed a fully systematic methodology to obtain a corpus of 73 papers. In each paper, we identified the clinical application, the type/amount of data used, the method employed, and the validation strategy, further decomposed into 12 different sub-categories. The papers overall illustrated some existing trends in how ML is used in the context of DBS, including the breath of the problem domain and evolving techniques, as well as common frameworks and limitations. This systematic review analyzes at a broad level how ML have been recently used to address clinical problems on DBS, giving insight into how these new computational methods are helping to push the state-of-the-art of functional neurosurgery. DBS clinical workflow is complex, involves many specialists, and raises several clinical issues which have partly been addressed with artificial intelligence. However, several areas remain and those that have been recently addressed with ML are by no means considered "solved" by the community nor are they closed to new and evolving methods.
Collapse
Affiliation(s)
- Maxime Peralta
- Univ Rennes, Inserm, LTSI - UMR 1099, F-35000 Rennes, France
| | - Pierre Jannin
- Univ Rennes, Inserm, LTSI - UMR 1099, F-35000 Rennes, France
| | - John S H Baxter
- Univ Rennes, Inserm, LTSI - UMR 1099, F-35000 Rennes, France.
| |
Collapse
|
20
|
Research on the Segmentation of Biomarker for Chronic Central Serous Chorioretinopathy Based on Multimodal Fundus Image. DISEASE MARKERS 2021; 2021:1040675. [PMID: 34527086 PMCID: PMC8437641 DOI: 10.1155/2021/1040675] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 07/20/2021] [Indexed: 11/18/2022]
Abstract
At present, laser surgery is one of the effective ways to treat the chronic central serous chorioretinopathy (CSCR), in which the location of the leakage area is of great importance. In order to alleviate the pressure on ophthalmologists to manually label the biomarkers as well as elevate the biomarker segmentation quality, a semiautomatic biomarker segmentation method is proposed in this paper, aiming to facilitate the accurate and rapid acquisition of biomarker location information. Firstly, the multimodal fundus images are introduced into the biomarker segmentation task, which can effectively weaken the interference of highlighted vessels in the angiography images to the location of biomarkers. Secondly, a semiautomatic localization technique is adopted to reduce the search range of biomarkers, thus enabling the improvement of segmentation efficiency. On the basis of the above, the low-rank and sparse decomposition (LRSD) theory is introduced to construct the baseline segmentation scheme for segmentation of the CSCR biomarkers. Moreover, a joint segmentation framework consisting of the above method and region growing (RG) method is further designed to improve the performance of the baseline scheme. On the one hand, the LRSD is applied to offer the initial location information of biomarkers for the RG method, so as to ensure that the RG method can capture effective biomarkers. On the other hand, the biomarkers obtained by RG are fused with those gained by LRSD to make up for the defect of undersegmentation of the baseline scheme. Finally, the quantitative and qualitative ablation experiments have been carried out to demonstrate that the joint segmentation framework performs well than the baseline scheme in most cases, especially in the sensitivity and F1-score indicators, which not only confirms the effectiveness of the framework in the CSCR biomarker segmentation scene but also implies its potential application value in CSCR laser surgery.
Collapse
|
21
|
Bočková M, Rektor I. Electrophysiological biomarkers for deep brain stimulation outcomes in movement disorders: state of the art and future challenges. J Neural Transm (Vienna) 2021; 128:1169-1175. [PMID: 34245367 DOI: 10.1007/s00702-021-02381-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 07/02/2021] [Indexed: 11/25/2022]
Abstract
Several neurological diseases are accompanied by rhythmic oscillatory dysfunctions in various frequency ranges and disturbed cross-frequency relationships on regional, interregional, and whole brain levels. Knowledge of these disease-specific oscillopathies is important mainly in the context of deep brain stimulation (DBS) therapy. Electrophysiological biomarkers have been used as input signals for adaptive DBS (aDBS) as well as preoperative outcome predictors. As movement disorders, particularly Parkinson's disease (PD), are among the most frequent DBS indications, the current research of DBS is the most advanced in the movement disorders field. We reviewed the literature published mainly between 2010 and 2020 to identify the most important findings concerning the current evolution of electrophysiological biomarkers in DBS and to address future challenges for prospective research.
Collapse
Affiliation(s)
- Martina Bočková
- Central European Institute of Technology (CEITEC), Brain and Mind Research Program, Masaryk University, Brno, Czech Republic
- Movement Disorders Center, First Department of Neurology, Masaryk University School of Medicine, St. Anne's Hospital, Pekařská 53, 656 91, Brno, Czech Republic
| | - Ivan Rektor
- Central European Institute of Technology (CEITEC), Brain and Mind Research Program, Masaryk University, Brno, Czech Republic.
- Movement Disorders Center, First Department of Neurology, Masaryk University School of Medicine, St. Anne's Hospital, Pekařská 53, 656 91, Brno, Czech Republic.
| |
Collapse
|
22
|
Geraedts VJ, Koch M, Kuiper R, Kefalas M, Bäck THW, van Hilten JJ, Wang H, Middelkoop HAM, van der Gaag NA, Contarino MF, Tannemaat MR. Preoperative Electroencephalography-Based Machine Learning Predicts Cognitive Deterioration after Subthalamic Deep Brain Stimulation. Mov Disord 2021; 36:2324-2334. [PMID: 34080712 PMCID: PMC8596544 DOI: 10.1002/mds.28661] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 04/12/2021] [Accepted: 05/05/2021] [Indexed: 12/15/2022] Open
Abstract
Background Subthalamic deep brain stimulation (STN DBS) may relieve refractory motor complications in Parkinson's disease (PD) patients. Despite careful screening, it remains difficult to determine severity of alpha‐synucleinopathy involvement which influences the risk of postoperative complications including cognitive deterioration. Quantitative electroencephalography (qEEG) reflects cognitive dysfunction in PD and may provide biomarkers of postoperative cognitive decline. Objective To develop an automated machine learning model based on preoperative EEG data to predict cognitive deterioration 1 year after STN DBS. Methods Sixty DBS candidates were included; 42 patients had available preoperative EEGs to compute a fully automated machine learning model. Movement Disorder Society criteria classified patients as cognitively stable or deteriorated at 1‐year follow‐up. A total of 16,674 EEG‐features were extracted per patient; a Boruta algorithm selected EEG‐features to reflect representative neurophysiological signatures for each class. A random forest classifier with 10‐fold cross‐validation with Bayesian optimization provided class‐differentiation. Results Tweny‐five patients were classified as cognitively stable and 17 patients demonstrated cognitive decline. The model differentiated classes with a mean (SD) accuracy of 0.88 (0.05), with a positive predictive value of 91.4% (95% CI 82.9, 95.9) and negative predictive value of 85.0% (95% CI 81.9, 91.4). Predicted probabilities between classes were highly differential (hazard ratio 11.14 [95% CI 7.25, 17.12]); the risk of cognitive decline in patients with high probabilities of being prognosticated as cognitively stable (>0.5) was very limited. Conclusions Preoperative EEGs can predict cognitive deterioration after STN DBS with high accuracy. Cortical neurophysiological alterations may indicate future cognitive decline and can be used as biomarkers during the DBS screening. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society
Collapse
Affiliation(s)
- Victor J Geraedts
- Department of Neurology, Leiden University Medical Center, Leiden, The Netherlands.,Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Milan Koch
- Leiden Institute of Advanced Computer Science, Leiden, The Netherlands
| | - Roy Kuiper
- Department of Neurology, Leiden University Medical Center, Leiden, The Netherlands.,Department of Neurology, Haga Teaching Hospital, Den Haag, The Netherlands
| | - Marios Kefalas
- Leiden Institute of Advanced Computer Science, Leiden, The Netherlands
| | - Thomas H W Bäck
- Leiden Institute of Advanced Computer Science, Leiden, The Netherlands
| | - Jacobus J van Hilten
- Department of Neurology, Leiden University Medical Center, Leiden, The Netherlands
| | - Hao Wang
- Leiden Institute of Advanced Computer Science, Leiden, The Netherlands
| | - Huub A M Middelkoop
- Department of Neurology, Leiden University Medical Center, Leiden, The Netherlands.,Neuropsychology Unit, Leiden University Institute of Psychology, Leiden, The Netherlands
| | - Niels A van der Gaag
- Department of Neurosurgery, Leiden University Medical Center, Leiden, The Netherlands.,Department of Neurosurgery, Haga Teaching Hospital, Den Haag, The Netherlands
| | - Maria Fiorella Contarino
- Department of Neurology, Leiden University Medical Center, Leiden, The Netherlands.,Department of Neurology, Haga Teaching Hospital, Den Haag, The Netherlands
| | - Martijn R Tannemaat
- Department of Neurology, Leiden University Medical Center, Leiden, The Netherlands
| |
Collapse
|