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Aujla S, Mohamed A, Tan R, Magtibay K, Tan R, Gao L, Khan N, Umapathy K. Classification of lung pathologies in neonates using dual-tree complex wavelet transform. Biomed Eng Online 2023; 22:115. [PMID: 38049880 PMCID: PMC10696711 DOI: 10.1186/s12938-023-01184-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 11/27/2023] [Indexed: 12/06/2023] Open
Abstract
INTRODUCTION Undiagnosed and untreated lung pathologies are among the leading causes of neonatal deaths in developing countries. Lung Ultrasound (LUS) has been widely accepted as a diagnostic tool for neonatal lung pathologies due to its affordability, portability, and safety. However, healthcare institutions in developing countries lack well-trained clinicians to interpret LUS images, which limits the use of LUS, especially in remote areas. An automated point-of-care tool that could screen and capture LUS morphologies associated with neonatal lung pathologies could aid in rapid and accurate diagnosis. METHODS We propose a framework for classifying the six most common neonatal lung pathologies using spatially localized line and texture patterns extracted via 2D dual-tree complex wavelet transform (DTCWT). We acquired 1550 LUS images from 42 neonates with varying numbers of lung pathologies. Furthermore, we balanced our data set to avoid bias towards a pathology class. RESULTS Using DTCWT and clinical features as inputs to a linear discriminant analysis (LDA), our approach achieved a per-image cross-validated classification accuracy of 74.39% for the imbalanced data set. Our classification accuracy improved to 92.78% after balancing our data set. Moreover, our proposed framework achieved a maximum per-subject cross-validated classification accuracy of 64.97% with an imbalanced data set while using a balanced data set improves its classification accuracy up to 81.53%. CONCLUSION Our work could aid in automating the diagnosis of lung pathologies among neonates using LUS. Rapid and accurate diagnosis of lung pathologies could help to decrease neonatal deaths in healthcare institutions that lack well-trained clinicians, especially in developing countries.
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Affiliation(s)
- Sagarjit Aujla
- Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, 350 Victoria Street, Toronto, ON, M5B 2K3, Canada.
| | - Adel Mohamed
- Department of Pediatrics, Mount Sinai Hospital, 600 University Ave, Toronto, ON, M5G 1X5, Canada
| | - Ryan Tan
- Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, 350 Victoria Street, Toronto, ON, M5B 2K3, Canada
| | - Karl Magtibay
- Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, 350 Victoria Street, Toronto, ON, M5B 2K3, Canada
| | - Randy Tan
- Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, 350 Victoria Street, Toronto, ON, M5B 2K3, Canada
| | - Lei Gao
- Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, 350 Victoria Street, Toronto, ON, M5B 2K3, Canada
| | - Naimul Khan
- Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, 350 Victoria Street, Toronto, ON, M5B 2K3, Canada
| | - Karthikeyan Umapathy
- Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, 350 Victoria Street, Toronto, ON, M5B 2K3, Canada
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McDougall M, Albaqami H, Mubashar Hassan G, Datta A. Patient Independent Interictal Epileptiform Discharge Detection. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-6. [PMID: 38082573 DOI: 10.1109/embc40787.2023.10341194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Epilepsy is a highly prevalent brain condition with many serious complications arising from it. The majority of patients which present to a clinic and undergo electroencephalogram (EEG) monitoring would be unlikely to experience seizures during the examination period, thus the presence of interictal epileptiform discharges (IEDs) become effective markers for the diagnosis of epilepsy. Furthermore, IED shapes and patterns are highly variable across individuals, yet trained experts are still able to identify them through EEG recordings - meaning that commonalities exist across IEDs that an algorithm can be trained on to detect and generalise to the larger population. This research proposes an IED detection system for the binary classification of epilepsy using scalp EEG recordings. The proposed system features an ensemble based deep learning method to boost the performance of a residual convolutional neural network, and a bidirectional long short-term memory network. This is implemented using raw EEG data, sourced from Temple University Hospital's EEG Epilepsy Corpus, and is found to outperform the current state of the art model for IED detection across the same dataset. The achieved accuracy and Area Under Curve (AUC) of 94.92% and 97.45% demonstrates the effectiveness of an ensemble method, and that IED detection can be achieved with high performance using raw scalp EEG data, thus showing promise for the proposed approach in clinical settings.
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Albaqami H, Hassan GM, Datta A. MP-SeizNet: A multi-path CNN Bi-LSTM Network for seizure-type classification using EEG. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
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Zambrana-Vinaroz D, Vicente-Samper JM, Manrique-Cordoba J, Sabater-Navarro JM. Wearable Epileptic Seizure Prediction System Based on Machine Learning Techniques Using ECG, PPG and EEG Signals. SENSORS (BASEL, SWITZERLAND) 2022; 22:9372. [PMID: 36502071 PMCID: PMC9736525 DOI: 10.3390/s22239372] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 11/26/2022] [Accepted: 11/27/2022] [Indexed: 06/17/2023]
Abstract
Epileptic seizures have a great impact on the quality of life of people who suffer from them and further limit their independence. For this reason, a device that would be able to monitor patients' health status and warn them for a possible epileptic seizure would improve their quality of life. With this aim, this article proposes the first seizure predictive model based on Ear EEG, ECG and PPG signals obtained by means of a device that can be used in a static and outpatient setting. This device has been tested with epileptic people in a clinical environment. By processing these data and using supervised machine learning techniques, different predictive models capable of classifying the state of the epileptic person into normal, pre-seizure and seizure have been developed. Subsequently, a reduced model based on Boosted Trees has been validated, obtaining a prediction accuracy of 91.5% and a sensitivity of 85.4%. Thus, based on the accuracy of the predictive model obtained, it can potentially serve as a support tool to determine the status epilepticus and prevent a seizure, thereby improving the quality of life of these people.
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Affiliation(s)
- David Zambrana-Vinaroz
- Neuroengineering Biomedical Research Group, Miguel Hernández University of Elche, 03202 Elche, Spain
| | - Jose Maria Vicente-Samper
- Neuroengineering Biomedical Research Group, Miguel Hernández University of Elche, 03202 Elche, Spain
| | - Juliana Manrique-Cordoba
- Neuroengineering Biomedical Research Group, Miguel Hernández University of Elche, 03202 Elche, Spain
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