An effectual IOT coupled EEG analysing model for continuous patient monitoring
A persistent neurological condition known as epilepsy is characterized by aberrant brain electrical activity. Epilepsy is a disorder characterized by sporadic symptoms and aberrant electrical brain activity that is spasmodic. Such aberrations are supported by non-stationary, non-linear, and multidim...
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| Format: | Article |
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Elsevier
2022-12-01
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| Series: | Measurement: Sensors |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2665917422002318 |
| _version_ | 1798014408763375616 |
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| author | Simran Khiani M. Mohamed Iqbal Amol Dhakne B.V. Sai Thrinath PG. Gayathri R. Thiagarajan |
| author_facet | Simran Khiani M. Mohamed Iqbal Amol Dhakne B.V. Sai Thrinath PG. Gayathri R. Thiagarajan |
| author_sort | Simran Khiani |
| collection | DOAJ |
| description | A persistent neurological condition known as epilepsy is characterized by aberrant brain electrical activity. Epilepsy is a disorder characterized by sporadic symptoms and aberrant electrical brain activity that is spasmodic. Such aberrations are supported by non-stationary, non-linear, and multidimensional clinical data. Such data processing and analysis present a variety of computing research difficulties. One of the most often used signals obtained from the human brain in both science and therapeutic settings is the electroencephalogram (EEG). It is an effective resource for offering insightful knowledge of the mechanics of the brain. EEG signal analyses that are precise and thorough are crucial in the diagnosis of brain diseases like epilepsy. The EEG signal is well-liked in the field of biomedical study because of its notable temporal solution, non-invasive recording setup, and convenience with minimal costs. Currently, skilled neurologists make a diagnosis by visually examining EEG information to determine the most likely course of action. It takes time and is error-prone to visually inspect high-dimensional and non-stationary EEG recordings. An automated seizure detector is a crucial tool for better understanding how seizures are generated as well as a relief for medical experts who become exhausted when viewing a continuous, massive, long-term recording. As it records from at least 16 channels, the multichannel automatic seizure detection system is large. It is difficult to use such a method on distantly located subjects. The suggested system gathers data from IoT devices, and patient history-related electronic clinical data that are saved in the cloud were subjected to predictive analytics. The Bi-LSTM-based intelligent healthcare system for tracking and precisely forecasting brain risk. The trade-off between the current system and the current methods is anticipated in the results section. |
| first_indexed | 2024-04-11T15:16:58Z |
| format | Article |
| id | doaj.art-5f5b2cc294234a1f80a3db03a734e726 |
| institution | Directory Open Access Journal |
| issn | 2665-9174 |
| language | English |
| last_indexed | 2024-04-11T15:16:58Z |
| publishDate | 2022-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Measurement: Sensors |
| spelling | doaj.art-5f5b2cc294234a1f80a3db03a734e7262022-12-22T04:16:27ZengElsevierMeasurement: Sensors2665-91742022-12-0124100597An effectual IOT coupled EEG analysing model for continuous patient monitoringSimran Khiani0M. Mohamed Iqbal1Amol Dhakne2B.V. Sai Thrinath3PG. Gayathri4R. Thiagarajan5G H Raisoni College of Engineering and Management, Pune, IndiaSchool of Computer Science and Engineering, VIT-AP University, Andhra Pradesh, India; Corresponding author.Department of Computer Engineering, Dr. D. Y. Patil Institute of Engineering, Management and Research, Akurdi, Pune, SPPU Pune, IndiaDepartment of EEE, Sree Vidyanikethan Engineering College, Tirupati, IndiaDepartment of Artificial Intelligence and Data Science, Kongunadu College of Engineering and Technology, Trichy, Tamilnadu, IndiaDepartment of IT, Prathyusha Engineering College, IndiaA persistent neurological condition known as epilepsy is characterized by aberrant brain electrical activity. Epilepsy is a disorder characterized by sporadic symptoms and aberrant electrical brain activity that is spasmodic. Such aberrations are supported by non-stationary, non-linear, and multidimensional clinical data. Such data processing and analysis present a variety of computing research difficulties. One of the most often used signals obtained from the human brain in both science and therapeutic settings is the electroencephalogram (EEG). It is an effective resource for offering insightful knowledge of the mechanics of the brain. EEG signal analyses that are precise and thorough are crucial in the diagnosis of brain diseases like epilepsy. The EEG signal is well-liked in the field of biomedical study because of its notable temporal solution, non-invasive recording setup, and convenience with minimal costs. Currently, skilled neurologists make a diagnosis by visually examining EEG information to determine the most likely course of action. It takes time and is error-prone to visually inspect high-dimensional and non-stationary EEG recordings. An automated seizure detector is a crucial tool for better understanding how seizures are generated as well as a relief for medical experts who become exhausted when viewing a continuous, massive, long-term recording. As it records from at least 16 channels, the multichannel automatic seizure detection system is large. It is difficult to use such a method on distantly located subjects. The suggested system gathers data from IoT devices, and patient history-related electronic clinical data that are saved in the cloud were subjected to predictive analytics. The Bi-LSTM-based intelligent healthcare system for tracking and precisely forecasting brain risk. The trade-off between the current system and the current methods is anticipated in the results section.http://www.sciencedirect.com/science/article/pii/S2665917422002318SensorsIoTEEGBi-LSTMMultidimensional data smart healthcareCloud |
| spellingShingle | Simran Khiani M. Mohamed Iqbal Amol Dhakne B.V. Sai Thrinath PG. Gayathri R. Thiagarajan An effectual IOT coupled EEG analysing model for continuous patient monitoring Measurement: Sensors Sensors IoT EEG Bi-LSTM Multidimensional data smart healthcare Cloud |
| title | An effectual IOT coupled EEG analysing model for continuous patient monitoring |
| title_full | An effectual IOT coupled EEG analysing model for continuous patient monitoring |
| title_fullStr | An effectual IOT coupled EEG analysing model for continuous patient monitoring |
| title_full_unstemmed | An effectual IOT coupled EEG analysing model for continuous patient monitoring |
| title_short | An effectual IOT coupled EEG analysing model for continuous patient monitoring |
| title_sort | effectual iot coupled eeg analysing model for continuous patient monitoring |
| topic | Sensors IoT EEG Bi-LSTM Multidimensional data smart healthcare Cloud |
| url | http://www.sciencedirect.com/science/article/pii/S2665917422002318 |
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