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.
Keywords: 1D-CNN; CAP; ECG; FPGA; Sleep quality.
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