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ANALYSIS OF OBSTRUCTIVE SLEEP APNEA DISORDER WITH ACCURACY PREDICTION USING SVM FOR SMART ENVIRONMENT
ACM Transactions on Multimedia Computing, Communications, and Applications ( IF 5.2 ) Pub Date : 2020-07-07 , DOI: 10.1145/3382782
Madhumitha Ramamurthy , Ilango Krishnamurthi 1 , Vimal S 2 , Suresh Annamalai 3
Affiliation  

One of the most crucial sleep disorders that has a direct hit on the quality of life is sleep apnea disorder. Declined memory and disorders related to personality are some of the consequences of Sleep Apnea Disorder. Identifying the difference between normal and abnormal levels of snoring sound is important for the detection of sleep apnea. Diagnosis of the sleep apnea usually takes place in hospitals under the direct supervision of medical professionals. Frequent visits to hospital for diagnosis tend to be an inconvenience to the elderly. Along with it, usage of body monitors also acts as a parameter in disrupting sleep. To overcome them, the analysis equipment is placed in the usual sleeping environment of the patient and LM393 sound sensor is used to detect the snoring levels. The analysis between the normal and the acquired snoring levels using threshold values and SVM helps confirm the presence or absence of the sleep apnea disorder.

中文翻译:

智能环境下支持向量机准确预测阻塞性睡眠呼吸暂停障碍的分析

直接影响生活质量的最重要的睡眠障碍之一是睡眠呼吸暂停障碍。记忆力下降和与人格相关的障碍是睡眠呼吸暂停障碍的一些后果。识别正常和异常打鼾声音之间的差异对于检测睡眠呼吸暂停很重要。睡眠呼吸暂停的诊断通常在医疗专业人员的直接监督下在医院进行。频繁到医院就诊往往会给老年人带来不便。除此之外,身体监测器的使用也可以作为干扰睡眠的参数。为了克服这些问题,将分析设备放置在患者通常的睡眠环境中,并使用 LM393 声音传感器来检测打鼾程度。
更新日期:2020-07-07
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