AI in ECG: Validating an ambulatory semiology labeller and predictor.

Autor: Muralidharan P; Machine Intelligence Research Laboratory, Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, Tamil Nadu 641112, India., Sankaran R; Department of Physical Medicine and Rehabilitation, Amrita Institute of Medical Sciences and Research Centre, Amrita Vishwa Vidyapeetham, Cochin, Kerala 682041, India., Bendapudi P; Department of Neonatology, Amrita Institute of Medical Sciences and Research Centre, Amrita Vishwa Vidyapeetham, Cochin, Kerala 682041, India., Kumar CS; Machine Intelligence Research Laboratory, Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, Tamil Nadu 641112, India. Electronic address: cs_kumar@cb.amrita.edu., Kumar AA; Department of Neurology, Amrita Institute of Medical Sciences and Research Centre, Amrita Vishwa Vidyapeetham, Cochin, Kerala 682041, India.
Jazyk: angličtina
Zdroj: Epilepsy research [Epilepsy Res] 2024 Aug; Vol. 204, pp. 107403. Date of Electronic Publication: 2024 Jun 28.
DOI: 10.1016/j.eplepsyres.2024.107403
Abstrakt: Objectives: Early prediction of epileptic seizures can help reduce morbidity and mortality. In this work, we explore using electrocardiographic (ECG) signal as input to a seizure prediction system and note that the performance can be improved by using selected signal processing techniques.
Methods: We used frequency domain analysis with a deep neural network backend for all our experiments in this work. We further analysed the effect of the proposed system for different seizure semiologies and prediction horizons. We explored refining the signal using signal processing to enhance the system's performance.
Results: Our final system using the Temple University Hospital's Seizure (TUHSZ) corpus gave an overall prediction accuracy of 84.02 %, sensitivity of 87.59 %, specificity of 81.9 %, and an area under the receiver operating characteristic curve (AUROC) of 0.9112. Notably, these results surpassed the state-of-the-art outcomes reported using the TUHSZ database; all findings are statistically significant. We also validated our study using the Siena scalp EEG database. Using the frequency domain data, our baseline system gave a performance of 75.17 %, 79.17 %, 70.04 % and 0.82 for prediction accuracy, sensitivity, specificity and AUROC, respectively. After selecting the optimal frequency band of 0.8-15 Hz, we obtained a performance of 80.49 %, 89.51 %, 75.23 % and 0.89 for prediction accuracy, sensitivity, specificity and AUROC, respectively which is an improvement of 5.32 %, 10.34 %, 5.19 % and 0.08 for prediction accuracy, sensitivity, specificity and AUROC, respectively.
Conclusions: The seizure information in ECG is concentrated in a narrow frequency band. Identifying and selecting that band can help improve the performance of seizure detection and prediction.
Significance: EEG is susceptible to artefacts and is not preferred in a low-cost ambulatory device. ECG can be used in wearable devices (like chest bands) and is feasible for developing a low-cost ambulatory device for seizure prediction. Early seizure prediction can provide patients and clinicians with the required alert to take necessary precautions and prevent a fatality, significantly improving the patient's quality of life.
Competing Interests: Declaration of Competing Interest There are no conflicts of interest to disclose among the authors.
(Copyright © 2024 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
Externí odkaz: