Respiratory Effort Signal Based Sleep Apnea Detection System Using Improved Random Forest Classifier

Prabha, Anju; Yadav, Jyoti; Rani, Asha; Singh, Vijander

doi:10.1080/03772063.2021.1999864

Cited by 6 publications

(1 citation statement)

References 48 publications

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“… 12 Prabha et al extracted features from single-channel abdominal respiratory effort signals sourced from the ISRUC-sleep database and constructed a model to automatically detect and classify sleep apnea events. 13 Because the detailed information about data collection and participants in the public databases is open to investigators, public databases can serve as benchmarks for comparing differences in algorithm performance. 14 However, due to the lack of validation with a large number of clinical populations, existing automatic sleep analysis models are rarely used in clinical practice.…”

Section: Introductionmentioning

confidence: 99%

The Chinese Clinical Sleep Database: An Innovative Database System Includes Large-Scale Clinical Data of Chinese Population

Fang,

Cheng,

et al. 2024

NSS

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Purpose In this study, we established the Chinese Clinical Sleep Database (CCSD), aiming to provide a safe, scalable, and user-friendly database that includes high-quality clinical data from Chinese population to facilitate sleep research. Material and Methods We collect individual’s demographic data, scales, anthropometric measurements, clinical diagnosis, and polysomnography (PSG) recordings from the routine medical process of sleep medicine centers using standardized procedures. The distributed cluster storage technology are utilized to store these data. The structured data are stored in a high-performance MySQL database, while the unstructured data are stored in an object storage service. And we have developed an online data platform to share and manage our data. Results The data collection has been conducted in three hospitals. In the preliminary stage of data collection (from October 18, 2022 to September 4, 2023), our database included a total of 1183 patients. Among them, 56.8% were male and their ages ranged from 3 to 88 years. These patients were diagnosed with various types of sleep disorders. Conclusion Since the CCSD’s inception, it has demonstrated good stability, security, and scalability. As an public database, the CCSD also exhibits user-friendliness. The CCSD contains comprehensive clinical data, which can contribute to the advancement of the diagnosis and treatment strategies for sleep disorders, ultimately promoting sleep health.

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Section: Introductionmentioning

confidence: 99%