ArticleBiomedical signal processing and control2021
An automated snoring sound classification method based on local dual octal pattern and iterative hybrid feature selector.
Article in Biomedical signal processing and control, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Role of Artificial Intelligence in COVID-19 Detection.Sensors (Basel, Switzerland) · 2021Pooled it
- Vision Transformers-Based Deep Feature Generation Framework for Hydatid Cyst Classification in Computed Tomography Images.Journal of imaging informatics in medicine · 2026Article
- Optimizing early diagnosis by integrating multiple classifiers for predicting brain stroke and critical diseases.Scientific reports · 2024Article
- Coherent Feature Extraction with Swarm Intelligence Based Hybrid Adaboost Weighted ELM Classification for Snoring Sound Classification.Diagnostics (Basel, Switzerland) · 2024Article
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- Deep-Stacked Convolutional Neural Networks for Brain Abnormality Classification Based on MRI Images.Journal of digital imaging · 2023Article
- A quantization algorithm of visual fatigue based on underdamped second order stochastic resonance for steady state visual evoked potentials.Frontiers in neuroscience · 2023Article
- EEG driving fatigue detection based on log-Mel spectrogram and convolutional recurrent neural networks.Frontiers in neuroscience · 2023Article
- An automated COVID-19 detection based on fused dynamic exemplar pyramid feature extraction and hybrid feature selection using deep learning.Computers in biology and medicine · 2021Article
- An Efficient Classification of Neonates Cry Using Extreme Gradient Boosting-Assisted Grouped-Support-Vector Network.Journal of healthcare engineering · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this research, a novel snoring sound classification (SSC) method is presented by proposing a new feature generation function to yield a high classification rate. The proposed feature extractor is named as Local Dual Octal Pattern (LDOP). A novel LDOP based SSC method is presented to solve the low success rate problems for Munich-Passau Snore Sound Corpus (MPSSC) dataset. Multilevel discrete wavelet transform (DWT) decomposition and the LDOP based feature generation, informative features selection with ReliefF and iterative neighborhood component analysis (RFINCA), and classification using k nearest neighbors (kNN) are fundamental phases of the proposed SSC method.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.