International Joint Conference on Neural Networks

https://www.repositorio.ufop.br/bitstream/123456789/7011/1/ARTIGO_ECGHeartBeat.pdf

ECG-based heartbeat classification for arrhythmia detection

Arrhythmia detection using deep convolutional neural network with long duration ECG signals.

A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification

Deep learning approach for active classification of electrocardiogram signals

Feature extraction for ECG heartbeats using higher order statistics of WPD coefficients

A multi-stage automatic arrhythmia recognition and classification system

Goal: Chronic obstructive pulmonary disease (COPD) is one of the deadliest diseases in the world. Because COPD is an incurable disease and requires considerable time to be diagnosed even by an experienced specialist, it becomes important to provide analysis abnormalities in simple ways. The aim of the study is to compare multiple machine-learning algorithms for the early diagnosis of COPD using multichannel lung sounds. Methods: Deep learning (DL) is an efficient machine-learning algorithm, which comprises unsupervised training to reduce optimization and supervised training by a feature-based distribution of classification parameters. This study focuses on analyzing multichannel lung sounds using statistical features of frequency modulations that are extracted using the Hilbert–Huang transform. Results: Deep-learning algorithm was used in the classification stage of the proposed model to separate the patients with COPD and healthy subjects. The proposed DL model with the Hilbert–Huang transform based statistical features was successful in achieving high classification performance rates of 93.67%, 91%, and 96.33% for accuracy, sensitivity, and specificity, respectively. Conclusion: The proposed computerized analysis of the multichannel lung sounds using DL algorithms provides a standardized assessment with high classification performance. Significance: Our study is a pioneer study that directly focuses on the lung sounds to separate COPD and non-COPD patients. Analyzing 12-channel lung sounds gives the advantages of assessing the entire lung obstructions.

Deep Learning on Computerized Analysis of Chronic Obstructive Pulmonary Disease

Electromagnetic pollution has been categorized as a new form of pollution The ability to reflect electromagnetic energy can be defined as shielding effectiveness This shielding effectiveness depends on the frequency of the electromagnetic wave and the electromagnetic properties of the shielding material This article presents a comparison of shielding effectiveness of double-square and ring patches reflecting at 900 and 1,800 MHz

Yakup Kutlu

Papers

Feature extraction for ECG heartbeats using higher order statistics of WPD coefficients

A multi-stage automatic arrhythmia recognition and classification system

Deep Learning on Computerized Analysis of Chronic Obstructive Pulmonary Disease

Effective electromagnetic shielding

Deep learning with 3D-second order difference plot on respiratory sounds