The relevance of sleep quality examination for clinical diagnosis is increasing with the discovery of new relationships with several diseases and the overall wellness. This assessment is commonly performed by conducting interviews with the subjects, evaluating the self-report and psychological variables. However, this approach has a major constraint since the subject is a poor self-observer of sleep behaviors. To address this issue, a method based on the examination of a physiological signal was developed. Specifically, the single-lead electrocardiogram signal was examined to estimate the cardiopulmonary coupling between the electrocardiogram derived respiration signal and the normal-to-normal sinus interbeat interval series. A one dimensional array was created from the coupling signal and was fed to a convolutional neural network to estimate the sleep quality. The age-related cyclic alternating pattern rate percentages in healthy subjects was considered as the classification reference. An accuracy of 91 % was attained by the developed model, with an area under the receiver operating characteristic curve of 97 %. The performance is in the upper range of the reported performance by the works presented in the state of the art, advocating the relevance of the proposed method. The model was implemented in a small field programmable gate array board. Hence, a home monitoring device was created, composed of a processing unit, a sensing module and a display unit. The device is resilient, easy to self-assemble and operate, and can conceivably be employed for clinical analysis.

随着发现与多种疾病和整体健康的新关系，睡眠质量检查与临床诊断的相关性正在增加。这种评估通常通过与受试者进行访谈、评估自我报告和心理变量来进行。然而，这种方法有一个主要限制，因为受试者是睡眠行为的不良自我观察者。为了解决这个问题，开发了一种基于生理信号检查的方法。具体而言，检查单导联心电图信号以估计心电图导出的呼吸信号与正常至正常的窦性搏动间隔系列之间的心肺耦合。根据耦合信号创建一维阵列，并将其馈送到卷积神经网络以估计睡眠质量。健康受试者中与年龄相关的循环交替模式率百分比被认为是分类参考。开发的模型达到了 91% 的准确度，接受者操作特征曲线下的面积为 97%。性能处于最先进的作品所报告性能的上限，提倡所提出方法的相关性。该模型是在一个小型现场可编程门阵列板中实现的。因此，家庭监控设备应运而生，由处理单元、传感模块和显示单元组成。该设备具有弹性，易于自组装和操作，可以想象用于临床分析。