ArticleBiomedical engineering letters2024
CIsense: an automated framework for early screening of cerebral infarction using PPG sensor data.
Article in Biomedical engineering letters, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Review of Recent (2015-2024) Popular Entropy Definitions Applied to Physiological Signals.Entropy (Basel, Switzerland) · 2025Review
- Machine learning-based feature selection and classification for cerebral infarction screening: an experimental study.PeerJ. Computer science · 2025Article
- Automated assessment of mental workload from PPG sensor data using cross-wavelet coherence and transfer learning.Biomedical engineering letters · 2024Article
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3 authors.
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Abstract
A cerebral infarction (CI), often known as a stroke, is a cognitive impairment in which a group of brain cells perishes from a lack of blood supply. The early prediction and evaluation of this problem are essential to avoid atrial fibrillation, heart valve disease, and other cardiac disorders. Different clinical strategies like Computerized tomography (CT) scans, Magnetic resonance imaging (MRI), and Carotid (ka-ROT-id) ultrasound are available to diagnose this problem. However, these methods are time-consuming and expensive. Wearable devices based on photoplethysmography (PPG) are gaining prevalence in diagnosing various cardiovascular diseases. This work uses the PPG signal to classify the CI subjects from the normal. We propose an automated framework and fiducial point-independent approach to predict CI with sufficient accuracy. The experiment is performed with a publicly available database having PPG and other physiological data of 219 individuals. The best validation and test accuracy of
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