{"id":25757,"date":"2026-09-03T12:37:45","date_gmt":"2026-09-03T12:37:45","guid":{"rendered":"https:\/\/scientificassociation.org\/?post_type=journal-paper&#038;p=25757"},"modified":"2026-09-03T12:37:45","modified_gmt":"2026-09-03T12:37:45","slug":"efficient-arrhythmia-classification-on-the-mit-bih-database-via-lightweight-1d-convolutional-neural-networks-for-cpu-constrained-environments","status":"publish","type":"journal-paper","link":"https:\/\/scientificassociation.org\/ar\/journal-paper\/efficient-arrhythmia-classification-on-the-mit-bih-database-via-lightweight-1d-convolutional-neural-networks-for-cpu-constrained-environments\/","title":{"rendered":"Efficient Arrhythmia Classification on the MIT-BIH Database via Lightweight 1D-Convolutional Neural Networks for CPU-Constrained Environments"},"content":{"rendered":"<div class=\"padding_abstract justify ltr\">Objective: Deployable Electrocardiogram (ECG) arrhythmia detection on standard Central Processing Units (CPUs) remains challenging due to deep learning models&#8217; computational demands. This paper proposes a lightweight One-Dimensional Convolutional Neural Network (1D-CNN) optimized for CPU execution. Methods: The model (two convolutional layers, &lt;10,000 parameters) evaluated on the MIT-BIH Arrhythmia Database using single-split\u00a0 (80\/20) and 10-fold cross-validation protocols. Results: Single-split evaluation achieved 97.52% accuracy in 133 seconds. Cross-validation revealed 95.62% mean accuracy. (\u00b18.51% SD) with 0.665 macro F1-score, processing each fold in 34.75 seconds (347.5 seconds total). The 300-second CPU constraint was satisfied for single-split but exceeded by 15.8% for cross-validation. Conclusion: The proposed model achieves an acceptable accuracy-efficiency trade-off for CPU-based screening applications. However, the 8.51% standard deviation across folds and persistent accuracy-F1 gap (95.62% vs. 0.665) indicate that patient-wise validation and class imbalance mitigation are necessary for diagnostic deployment. Significance: This work establishes a baseline for CPU-efficient ECG analysis while identifying critical methodological requirements.<\/div>\n","protected":false},"featured_media":25762,"template":"","meta":{"_acf_changed":false},"journal-name":[220],"paper-tag":[232,278,276],"class_list":["post-25757","journal-paper","type-journal-paper","status-publish","has-post-thumbnail","hentry","journal-name-jaiep","paper-tag-issue-2","paper-tag-november-2026","paper-tag-volume-3"],"acf":[],"_links":{"self":[{"href":"https:\/\/scientificassociation.org\/ar\/wp-json\/wp\/v2\/journal-paper\/25757","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/scientificassociation.org\/ar\/wp-json\/wp\/v2\/journal-paper"}],"about":[{"href":"https:\/\/scientificassociation.org\/ar\/wp-json\/wp\/v2\/types\/journal-paper"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/scientificassociation.org\/ar\/wp-json\/wp\/v2\/media\/25762"}],"wp:attachment":[{"href":"https:\/\/scientificassociation.org\/ar\/wp-json\/wp\/v2\/media?parent=25757"}],"wp:term":[{"taxonomy":"journal-name","embeddable":true,"href":"https:\/\/scientificassociation.org\/ar\/wp-json\/wp\/v2\/journal-name?post=25757"},{"taxonomy":"paper-tag","embeddable":true,"href":"https:\/\/scientificassociation.org\/ar\/wp-json\/wp\/v2\/paper-tag?post=25757"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}