paper-with-me

홈 › Papers

Advancing ECG Diagnosis Using Reinforcement Learning on Global Waveform Variations Related to P Wave and PR Interval

2024-01-10 · Rumsha Fatima, Shahzad Younis, Faraz Shaikh, Hamna Imran, Haseeb Sultan, Shahzad Rasool, Mehak Rafiq

The reliable diagnosis of cardiac conditions through electrocardiogram (ECG) analysis critically depends on accurately detecting P waves and measuring the PR interval. However, achieving consistent and generalizable diagnoses across diverse populations presents challenges due to the inherent global variations observed in ECG signals. This paper is focused on applying the Q learning reinforcement algorithm to the various ECG datasets available in the PhysioNet/Computing in Cardiology Challenge (CinC). Five ECG beats, including Normal Sinus Rhythm, Atrial Flutter, Atrial Fibrillation, 1st Degree Atrioventricular Block, and Left Atrial Enlargement, are included to study variations of P waves and PR Interval on Lead II and Lead V1. Q-Agent classified 71,672 beat samples in 8,867 patients with an average accuracy of 90.4% and only 9.6% average hamming loss over misclassification. The average classification time at the 100th episode containing around 40,000 samples is 0.04 seconds. An average training reward of 344.05 is achieved at an alpha, gamma, and SoftMax temperature rate of 0.001, 0.9, and 0.1, respectively.

📄 PDF Abstract BibTeX arXiv:2401.04938

Code (0)

등록된 구현이 없습니다.

Tasks

Q-LearningRhythm

Methods 이 논문이 사용한 방법론

Softmax The Softmax output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$…

Similar Papers 제목 키워드 기반

Advancing machine fault diagnosis: A detailed examination of convolutional neural networks

2025-02-12 · Govind Vashishtha, Sumika Chauhan, Mert Sehri, Justyna Hebda-Sobkowicz 외

The growing complexity of machinery and the increasing demand for operational efficiency and safety have driven the development of advanced fault diagnosis techniques. Among these, convolutional neural networks (CNNs) ha…

Data AugmentationFault DetectionFault DiagnosisTransfer Learning

Graph-Based Pseudo-multimodal Contrastive Learning for 12-Lead ECG Representations

2026-08-27 · Mengyu Wang, Kozo Okada, Takafumi Goto, Natsuko Jinba 외 arxiv

12-lead electrocardiogram (ECG) is a standard, non-invasive examination widely used for diagnosing coronary artery disease, where clinical interpretation relies on comparing waveform patterns across multiple leads. Howev…

Contrastive Learning

Camera PPG waveforms at the forehead

2023-06-16 · A. C. den Brinker, H. H. Der Sarkissian, J. H. Wülbern, B. Balmaekers 외

In order to obtain insights into the feasibility of replacing ECG-guided triggering in magnetic resonance imaging (MRI) by a system based on video photoplethysmography (PPG), PPG and ECG data were collected from voluntee…

Photoplethysmography (PPG)

Standardization of Neuromuscular Reflex Analysis -- Role of Fine-Tuned Vision-Language Model Consortium and OpenAI gpt-oss Reasoning LLM Enabled Decision Support System

2025-08-17 · Eranga Bandara, Ross Gore, Sachin Shetty, Ravi Mukkamala 외 arxiv

Accurate assessment of neuromuscular reflexes, such as the H-reflex, plays a critical role in sports science, rehabilitation, and clinical neurology. Traditional analysis of H-reflex EMG waveforms is subject to variabili…

Prompt Engineering

Prompt Transfer for Dual-Aspect Cross Domain Cognitive Diagnosis

2024-12-06 · Fei Liu, Yizhong Zhang, Shuochen Liu, Shengwei Ji 외

Cognitive Diagnosis (CD) aims to evaluate students' cognitive states based on their interaction data, enabling downstream applications such as exercise recommendation and personalized learning guidance. However, existing…

cognitive diagnosisDiversity