Trends of ischemic heart disease mortality attributable to household air pollution during 1990–2019 in China and India: an age-period-cohort analysis

Wang, Tong; Ma, Yudiyang; Li, Ruiqing; Sun, Jinyi; Huang, Lihong; Wang, Shuwen; Yu, Chuanhua

doi:10.1007/s11356-022-21770-1

Cited by 4 publications

(2 citation statements)

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“…Anxiety and depression are the most common disorders, and the COVID-19 pandemic has led to an increase in persons living with these conditions [3]. FIGURE 1: Summary of the statistical report of WHO regarding leading causes of deaths globally for the year 2000 and up to 2023 Psychiatric disorders, including bipolar disorder, schizophrenia, eating disorders, ADHD, and autism spectrum disorder (ASD), are characterized by significant difficulties in thinking, emotional regulation, and behavior.…”

Section: Introductionmentioning

confidence: 99%

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EEG Signal Processing for Medical Diagnosis, Healthcare, and Monitoring: A Comprehensive Review

Amer,

Belhaouari

2023

IEEE Access

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EEG is a common and safe test that uses small electrodes to record electrical signals from the brain. It has a broad range of applications in medical diagnosis, including diagnosis of epileptic seizure, Alzheimer's, brain tumors, head injury, sleep disorders, stroke, and other seizure and neurological disorders. EEG can also be used to help diagnose death in people who are in a persistent coma. The use of digital signal processing and machine learning to improve EEG analysis for medical diagnosis has gained traction in recent years. This is because EEG visual analysis can be complex and time-consuming, as it mostly involves high dimensions and consists of large datasets. The development of novel sensors for EEG recording, digital signal processing algorithms, feature engineering, and detection algorithms increases the need for efficient diagnostic systems. An extensive review of the recent approaches for EEG preprocessing, extraction of features, and diagnosis of brain disorders is provided. In this paper, the main focus is to identify reliable algorithms for preprocessing, feature engineering, and classification of EEG, applied to medical healthcare and diagnosis, providing practitioners with insights into the most effective strategies, as well as potential future directions for improving accuracy of the automatic diagnostic systems. The study of reliable feature extraction and classification algorithms is crucial for a more accurate analysis of EEG signals. This paper can provide valuable information to researchers and practitioners working in the fields of EEG analysis and machine learning, as it provides a summary of recent developments and highlights key areas for future research. This paper can help researchers and clinicians to stay up-to-date on the latest developments in this field.

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Section: Introductionmentioning

confidence: 99%

“…EEG signals are then analyzed by extracting features that consider the complexity of brain dynamics. EEG signals are currently being studied to improve preprocessing and feature extraction methods, which can be applied to EEG processing, enabling the extraction of reliable features [3].…”

Section: Introductionmentioning

confidence: 99%