Implementing machine learning algorithms to detect and screen obstructive sleep apnoea episodes using a headband

机器学习算法能否用于高准确度和高精度地检测阻塞性睡眠呼吸暂停？机器学习算法与诊断阻塞性睡眠呼吸暂停的传统方法（多导睡眠图）相比如何？实施机器学习算法，检测疑似阻塞性睡眠呼吸暂停患者的阻塞性睡眠呼吸暂停发作。对于临床医生来说，手动检查患者的睡眠数据以检测阻塞性睡眠呼吸暂停是昂贵且劳动密集的。尤其是因为有不止一个方面需要考虑。例如，声音数据（打鼾）本身不足以检测阻塞性睡眠呼吸暂停。此外，某些事件的发生频率也很重要。因此，临床医生需要通过大量的数据才能诊断出阻塞性睡眠呼吸暂停患者。很可能，人为错误将导致这种费力的活动。因此，如果在分析期间遗漏了重要事件，则可能难以做出决策和诊断患者。传感器噪声也会掩盖某些事件和信号，使决策变得困难。总的目的是减轻临床工作人员手动睡眠评分患者的负担。因此，学习算法将用于自动突出阻塞性睡眠呼吸暂停发作。它是一种大规模筛查工具，而不是诊断测试。将生成睡眠评分，因此临床医生可以决定是否需要PSG。机器学习工具将使用多个传感器的数据。这是为了防止收集的数据是冗余的，如果一个传感器失败或连接丢失等算法将考虑到多个夜晚的数据，这将有一个优势，比昂贵的PSG。要使用的带包括多个传感器，这些传感器对于检测阻塞性睡眠呼吸暂停是必不可少的。该带可以带回家，并且患者不需要在诊所或医院。因此，提取的数据更可靠，因为患者在医院不会感到舒适，他们可能不会像在家里那样睡觉。不同的机器学习算法已被研究和审查，以更准确，更适合于阻塞性睡眠呼吸暂停的检测。然而，很少有实现到实际设备中。

Can machine learning algorithms be used to detect obstructive sleep apnoea with high accuracy and precision? How do machine learning algorithms compare to the traditional method (polysomnography) of diagnosing obstructive sleep apnoea? Implement machine learning algorithms to detect obstructive sleep apnoea episodes in patients who are suspected of obstructive sleep apnoea. It is costly and labour-intensive for a clinician to manually go through a patient's sleep data to detect obstructive sleep apnoea. Especially because there is more than one aspect that needs to be looked at. For example, sound data (snoring) is not enough on its own to be able to detect obstructive sleep apnoea. Also, the frequency of certain events is important. Therefore, the clinician needs to go through a lot of data to be able to diagnose the patient with obstructive sleep apnoea. It is likely that the human error will result from this laborious activity. Therefore, it can be difficult to make decisions and diagnose the patients if important events have been missed during the analysis. Sensor noise can also mask certain events and signals making it hard to make decisions. The general aim is to reduce the burden on clinical staff that manually sleep score patients. So, learning algorithms will be used to automatically highlight the obstructive sleep apnoea episodes. It is a mass screening tool rather than a diagnostic test. A sleep score will be generated, so a clinician can decide whether a PSG is required. The machine learning tool will use data for multiple sensors. This is to prevent the collected data being redundant if one of the sensors fail or a connection is lost etc. The algorithm will take into account multiple nights of data which will have an advantage over the expensive PSG. The band that is to be used includes multiple sensors that are essential for the detection of obstructive sleep apnoea. The band can be taken home, and it is not necessary for the patient to be at a clinic or a hospital. Therefore, the extracted data is more reliable because patients being at hospital do not feel comfortable and they might not sleep as they would at home. Different machine learning algorithms have been researched and reviewed as to which is more accurate and suitable for detection of obstructive sleep apnoea. However, there has been little implementation into an actual device.