Discrete Wavelet Transform based statistical features for the diagnosis of epilepsy

Reddy, Vyza Yashwanth Sai; Akanksha, P. Sai; Suman, D.; Mudigonda, Malini

doi:10.1109/indicon.2015.7443494

Cited by 3 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…has gained popularity and is used as a signal decomposition technique (Reddy et al, 2015). Research works have also used machine learning techniques in channel detection, and selection of bands of EEG (Rasheed et al, 2020).…”

Section: Sstotmentioning

confidence: 99%

“…It can be observed from Table 2 that wavelet decomposition and Empirical Model Decomposition (EMD) (Das & Bhuiyan, 2016) is another well‐liked choice amongst existing EEG signal decomposition techniques (Das & Bhuiyan, 2016). Tunable Q‐factor Wavelet Transform (TQWT) has gained popularity and is used as a signal decomposition technique (Reddy et al, 2015). Research works have also used machine learning techniques in channel detection, and selection of bands of EEG (Rasheed et al, 2020).…”

Section: Software Developmentsmentioning

confidence: 99%

See 1 more Smart Citation

A review on software and hardware developments in automatic epilepsy diagnosis usingEEGdatasets

et al. 2023

View full text Add to dashboard Cite

Epilepsy is a common non-communicable, group of neurological disorders affecting more than 50 million individuals worldwide. Different approaches of basic, clinical, and translational research of the human brain have been explored to diagnose, treat, and manage the growing no. of cases of epilepsy. Various hospital information from video, images, signals, forms, and so forth, are retrieved and analysed to develop a consensus for such patients. Electroencephalography (EEG) tests are routinely used to diagnose the type of epilepsy in a clinical setting. Artificial Intelligence algorithms are assisting in the early detection and prediction of epileptic patterns observed in EEG signals. This paper reviews recent and emerging state-of-the-art (SOTA) software and hardware approaches in data selection, signal processing, feature estimation, classification, detection methods, and evaluation metrics applied to open and private EEG datasets from 2014 to 2022. The work summarizes and compares reported works through subjective and objective parameters. Rise in hardware pipeline, start-ups, and companies, deep learning methods in software pipeline and release of free EEG datasets have been observed from 2014 to 2022. SOTA

show abstract

Section: Sstotmentioning

confidence: 99%

Section: Software Developmentsmentioning

confidence: 99%

A review on software and hardware developments in automatic epilepsy diagnosis usingEEGdatasets

et al. 2023

View full text Add to dashboard Cite

show abstract

Software advancements in automatic epilepsy diagnosis and seizure detection: 10-year review

Handa,

Lavanya,

Goel

et al. 2024

Artif Intell Rev

View full text Add to dashboard Cite

Epilepsy is a chronic neurological disorder that may be diagnosed and monitored using routine diagnostic tests like Electroencephalography (EEG). However, manual introspection and analysis of EEG signals is presently difficult and repetitive task even for experienced neuro-technologists with high false-positive rates and inter- and intra-rater reliability. Software advancements using Artificial Intelligence (AI) algorithms have the potential to early detect and predict abnormal patterns observed in EEG signals. The present review focuses on systematically reporting software advancements and their implementation using hardware systems in automatic epilepsy diagnosis and seizure detection for the past 10 years. Traditional, hybrid, and end-to-end AI-based pipelines and associated EEG datasets have been discussed. The review summarizes and compares reported articles, datasets, and patents through various subjective and objective parameters in this field. Latest advancements demonstrate that AI-based pipelines can reduce the introspection time by at least 50% without compromising the diagnostic accuracy or abnormal event detection. A significant rise in hardware implementation of software-based pipelines, end-to-end deep learning architectures for real-time analysis, and granted patents has been noticed since 2011. More than twenty-eight datasets have been developed to automatically diagnose epileptic EEG signals from 2001 to 2023. Extensive analysis using explainability tools, cross-dataset generalizations, reproducibility analysis, and ablation experiments can further improve the existing AI-based pipelines in this field. There is a need for the development of standardized protocols for data collection and its AI pipeline for a robust, inter- and intra-rater reliability-free, and real-time automatic epilepsy diagnosis.

show abstract

Classification and Diagnosis of Sleep Apnea and Congestive Heart Failure Using DWT Based Statistical Features: An Extensive Study

Suraj

Muppalla

Reddy

et al. 2018

2018 15th IEEE India Council International Conference (INDICON)

View full text Add to dashboard Cite

Discrete Wavelet Transform based statistical features for the diagnosis of epilepsy

Cited by 3 publications

References 9 publications

A review on software and hardware developments in automatic epilepsy diagnosis usingEEGdatasets

A review on software and hardware developments in automatic epilepsy diagnosis usingEEGdatasets

Software advancements in automatic epilepsy diagnosis and seizure detection: 10-year review

Classification and Diagnosis of Sleep Apnea and Congestive Heart Failure Using DWT Based Statistical Features: An Extensive Study

Contact Info

Product

Resources

About