1
|
Statsenko Y, Babushkin V, Talako T, Kurbatova T, Smetanina D, Simiyu GL, Habuza T, Ismail F, Almansoori TM, Gorkom KNV, Szólics M, Hassan A, Ljubisavljevic M. Automatic Detection and Classification of Epileptic Seizures from EEG Data: Finding Optimal Acquisition Settings and Testing Interpretable Machine Learning Approach. Biomedicines 2023; 11:2370. [PMID: 37760815 PMCID: PMC10525492 DOI: 10.3390/biomedicines11092370] [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: 06/05/2023] [Revised: 07/13/2023] [Accepted: 07/21/2023] [Indexed: 09/29/2023] Open
Abstract
Deep learning (DL) is emerging as a successful technique for automatic detection and differentiation of spontaneous seizures that may otherwise be missed or misclassified. Herein, we propose a system architecture based on top-performing DL models for binary and multigroup classifications with the non-overlapping window technique, which we tested on the TUSZ dataset. The system accurately detects seizure episodes (87.7% Sn, 91.16% Sp) and carefully distinguishes eight seizure types (95-100% Acc). An increase in EEG sampling rate from 50 to 250 Hz boosted model performance: the precision of seizure detection rose by 5%, and seizure differentiation by 7%. A low sampling rate is a reasonable solution for training reliable models with EEG data. Decreasing the number of EEG electrodes from 21 to 8 did not affect seizure detection but worsened seizure differentiation significantly: 98.24 ± 0.17 vs. 85.14 ± 3.14% recall. In detecting epileptic episodes, all electrodes provided equally informative input, but in seizure differentiation, their informative value varied. We improved model explainability with interpretable ML. Activation maximization highlighted the presence of EEG patterns specific to eight seizure types. Cortical projection of epileptic sources depicted differences between generalized and focal seizures. Interpretable ML techniques confirmed that our system recognizes biologically meaningful features as indicators of epileptic activity in EEG.
Collapse
Affiliation(s)
- Yauhen Statsenko
- Radiology Department, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
- Medical Imaging Platform, ASPIRE Precision Medicine Research Institute Abu Dhabi, Al Ain P.O. Box 15551, United Arab Emirates
- Big Data Analytics Center, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Vladimir Babushkin
- Radiology Department, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Tatsiana Talako
- Radiology Department, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
- Department of Oncohematology, Minsk Scientific and Practical Center for Surgery, Transplantology and Hematology, 220089 Minsk, Belarus
| | - Tetiana Kurbatova
- Radiology Department, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Darya Smetanina
- Radiology Department, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Gillian Lylian Simiyu
- Radiology Department, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Tetiana Habuza
- Big Data Analytics Center, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
- Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Fatima Ismail
- Pediatric Department, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Taleb M. Almansoori
- Radiology Department, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Klaus N.-V. Gorkom
- Radiology Department, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Miklós Szólics
- Neurology Division, Medicine Department, Tawam Hospital, Al Ain P.O. Box 15258, United Arab Emirates
- Internal Medicine Department, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Ali Hassan
- Neurology Division, Medicine Department, Tawam Hospital, Al Ain P.O. Box 15258, United Arab Emirates
| | - Milos Ljubisavljevic
- Physiology Department, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates;
- Neuroscience Platform, ASPIRE Precision Medicine Research Institute Abu Dhabi, Al Ain P.O. Box 15551, United Arab Emirates
| |
Collapse
|
2
|
Khan GH, Khan NA, Altaf MAB, Abbasi Q. A Shallow Autoencoder Framework for Epileptic Seizure Detection in EEG Signals. SENSORS (BASEL, SWITZERLAND) 2023; 23:4112. [PMID: 37112452 PMCID: PMC10144298 DOI: 10.3390/s23084112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 04/11/2023] [Accepted: 04/17/2023] [Indexed: 06/19/2023]
Abstract
This paper presents a trainable hybrid approach involving a shallow autoencoder (AE) and a conventional classifier for epileptic seizure detection. The signal segments of a channel of electroencephalogram (EEG) (EEG epochs) are classified as epileptic and non-epileptic by employing its encoded AE representation as a feature vector. Analysis on a single channel-basis and the low computational complexity of the algorithm allow its use in body sensor networks and wearable devices using one or few EEG channels for wearing comfort. This enables the extended diagnosis and monitoring of epileptic patients at home. The encoded representation of EEG signal segments is obtained based on training the shallow AE to minimize the signal reconstruction error. Extensive experimentation with classifiers has led us to propose two versions of our hybrid method: (a) one yielding the best classification performance compared to the reported methods using the k-nearest neighbor (kNN) classifier and (b) the second with a hardware-friendly architecture and yet with the best classification performance compared to other reported methods in this category using a support-vector machine (SVM) classifier. The algorithm is evaluated on the Children's Hospital Boston, Massachusetts Institute of Technology (CHB-MIT), and University of Bonn EEG datasets. The proposed method achieves 98.85% accuracy, 99.29% sensitivity, and 98.86% specificity on the CHB-MIT dataset using the kNN classifier. The best figures using the SVM classifier for accuracy, sensitivity, and specificity are 99.19%, 96.10%, and 99.19%, respectively. Our experiments establish the superiority of using an AE approach with a shallow architecture to generate a low-dimensionality yet effective EEG signal representation capable of high-performance abnormal seizure activity detection at a single-channel EEG level and with a fine granularity of 1 s EEG epochs.
Collapse
Affiliation(s)
- Gul Hameed Khan
- Department of Electrical Engineering, Lahore University of Management Sciences (LUMS), Lahore 54792, Pakistan; (N.A.K.); (M.A.B.A.)
| | - Nadeem Ahmad Khan
- Department of Electrical Engineering, Lahore University of Management Sciences (LUMS), Lahore 54792, Pakistan; (N.A.K.); (M.A.B.A.)
| | - Muhammad Awais Bin Altaf
- Department of Electrical Engineering, Lahore University of Management Sciences (LUMS), Lahore 54792, Pakistan; (N.A.K.); (M.A.B.A.)
- Engineering and Design Department, Western Washington University, Bellingham, WA 98225, USA
| | - Qammer Abbasi
- Communications Sensing and Imaging Research Group, James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK;
| |
Collapse
|
3
|
Shivaraja TR, Remli R, Kamal N, Wan Zaidi WA, Chellappan K. Assessment of a 16-Channel Ambulatory Dry Electrode EEG for Remote Monitoring. SENSORS (BASEL, SWITZERLAND) 2023; 23:3654. [PMID: 37050713 PMCID: PMC10098757 DOI: 10.3390/s23073654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 03/10/2023] [Accepted: 03/13/2023] [Indexed: 06/19/2023]
Abstract
Ambulatory EEGs began emerging in the healthcare industry over the years, setting a new norm for long-term monitoring services. The present devices in the market are neither meant for remote monitoring due to their technical complexity nor for meeting clinical setting needs in epilepsy patient monitoring. In this paper, we propose an ambulatory EEG device, OptiEEG, that has low setup complexity, for the remote EEG monitoring of epilepsy patients. OptiEEG's signal quality was compared with a gold standard clinical device, Natus. The experiment between OptiEEG and Natus included three different tests: eye open/close (EOC); hyperventilation (HV); and photic stimulation (PS). Statistical and wavelet analysis of retrieved data were presented when evaluating the performance of OptiEEG. The SNR and PSNR of OptiEEG were slightly lower than Natus, but within an acceptable bound. The standard deviations of MSE for both devices were almost in a similar range for the three tests. The frequency band energy analysis is consistent between the two devices. A rhythmic slowdown of theta and delta was observed in HV, whereas photic driving was observed during PS in both devices. The results validated the performance of OptiEEG as an acceptable EEG device for remote monitoring away from clinical environments.
Collapse
Affiliation(s)
- Theeban Raj Shivaraja
- Department of Electrical, Electronics and System Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
| | - Rabani Remli
- Department of Medicine, Faculty of Medicine, Universiti Kebangsaan Malaysia, Cheras 56000, Malaysia
- Hospital Canselor Tuanku Muhriz, Universiti Kebangsaan Malaysia, Cheras 56000, Malaysia
| | - Noorfazila Kamal
- Department of Electrical, Electronics and System Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
| | - Wan Asyraf Wan Zaidi
- Department of Medicine, Faculty of Medicine, Universiti Kebangsaan Malaysia, Cheras 56000, Malaysia
- Hospital Canselor Tuanku Muhriz, Universiti Kebangsaan Malaysia, Cheras 56000, Malaysia
| | - Kalaivani Chellappan
- Department of Electrical, Electronics and System Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
| |
Collapse
|