Biomedical Signal Processing for the Diagnosis and Monitoring of Neurological Disorders

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 2251

Special Issue Editor

Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy
Interests: eeg; psychogenic nonepileptic seizures; machine learning

Special Issue Information

Dear Colleagues,

Neurological disorders affecting patients' health and daily life are increasing becoming more common. Early and accurate diagnosis can improve the success of disease management. Signal processing techniques and machine learning play key roles in detecting neurological abnormalities and improving diagnosis and treatment consistency.

This Special Issue aims to highlight biomedical signal processing applications for neurological disorder diagnosis, with topics of interest including but not limited to pre-processing, feature extraction, and classification.

Dr. Barbara Calabrese
Guest Editor

Manuscript Submission Information

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Keywords

  • biomedical signal processing
  • machine learning
  • EEG
  • MEG
  • neurological disorders

Published Papers (2 papers)

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12 pages, 4221 KiB  
Article
Development and Validation of a Nomograph Model for Post-Operative Central Nervous System Infection after Craniocerebral Surgery
by Li Cheng, Wenhui Bai, Ping Song, Long Zhou, Zhiyang Li, Lun Gao, Chenliang Zhou and Qiang Cai
Diagnostics 2023, 13(13), 2207; https://doi.org/10.3390/diagnostics13132207 - 29 Jun 2023
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Abstract
Purpose: A nomograph model of predicting the risk of post-operative central nervous system infection (PCNSI) after craniocerebral surgery was established and validated. Methods: The clinical medical records of patients after cranial surgery in Renmin Hospital of Wuhan University from January 2020 to September [...] Read more.
Purpose: A nomograph model of predicting the risk of post-operative central nervous system infection (PCNSI) after craniocerebral surgery was established and validated. Methods: The clinical medical records of patients after cranial surgery in Renmin Hospital of Wuhan University from January 2020 to September 2022 were collected, of whom 998 patients admitted to Shouyi Hospital District were used as the training set and 866 patients admitted to Guanggu Hospital District were used as the validation set. Lasso regression was applied to screen the independent variables in the training set, and the model was externally validated in the validation set. Results: A total of 1864 patients after craniocerebral surgery were included in this study, of whom 219 (11.75%) had PCNSI. Multivariate logistic regression analysis showed that age > 70 years, a previous history of diabetes, emergency operation, an operation time ≥ 4 h, insertion of a lumbar cistern drainage tube ≥ 72 h, insertion of an intracranial drainage tube ≥ 72 h, intraoperative blood loss ≥ 400 mL, complicated with shock, postoperative albumin ≤ 30 g/L, and an ICU length of stay ≥ 3 days were independent risk factors for PCNSI. The area under the curve (AUC) of the training set was 0.816 (95% confidence interval (95%CI), 0.773–0.859, and the AUC of the validation set was 0.760 (95%CI, 0.715–0.805). The calibration curves of the training set and the validation set showed p-values of 0.439 and 0.561, respectively, with the Hosmer–Lemeshow test. The analysis of the clinical decision curve showed that the nomograph model had high clinical application value. Conclusion: The nomograph model constructed in this study to predict the risk of PCNSI after craniocerebral surgery has a good predictive ability. Full article
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23 pages, 1823 KiB  
Case Report
EEG Correlates of Cognitive Functions in a Child with ASD and White Matter Signal Abnormalities: A Case Report with Two-and-a-Half-Year Follow-Up
by Milica Ćirović, Ljiljana Jeličić, Slavica Maksimović, Saška Fatić, Maša Marisavljević, Tatjana Bošković Matić and Miško Subotić
Diagnostics 2023, 13(18), 2878; https://doi.org/10.3390/diagnostics13182878 - 08 Sep 2023
Cited by 1 | Viewed by 980
Abstract
This research aimed to examine the EEG correlates of different stimuli processing instances in a child with ASD and white matter signal abnormalities and to investigate their relationship to the results of behavioral tests. The prospective case study reports two and a half [...] Read more.
This research aimed to examine the EEG correlates of different stimuli processing instances in a child with ASD and white matter signal abnormalities and to investigate their relationship to the results of behavioral tests. The prospective case study reports two and a half years of follow-up data from a child aged 38 to 66 months. Cognitive, speech–language, sensory, and EEG correlates of auditory–verbal and auditory–visual–verbal information processing were recorded during five test periods, and their mutual interrelation was analyzed. EEG findings revealed no functional theta frequency range redistribution in the frontal regions favoring the left hemisphere during speech processing. The results pointed to a positive linear trend in the relative theta frequency range and a negative linear trend in the relative alpha frequency range when listening to and watching the cartoon. There was a statistically significant correlation between EEG signals and behavioral test results. Based on the obtained results, it may be concluded that EEG signals and their association with the results of behavioral tests should be evaluated with certain restraints considering the characteristics of the stimuli during EEG recording. Full article
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