The Prediction of Sleep Quality Using Heart Rate Variability Modulations During Wakefulness

Sleep quality is a vital component of one's overall health and wellbeing. Inadequate sleep quality is linked to various adverse consequences, including cognitive decline, mood disruptions, and an elevated susceptibility to non-communicable diseases. Hence, it is crucial to precisely evaluate the quality of sleep, in order to identify individuals who are at risk and to develop successful interventions. Importantly, it has been shown that sleep quality can impact physiological processes even when a person is awake, leading to changes in heart rate variability (HRV). From this standpoint, the utilization of wearables and contactless technologies that can measure HRV without causing any discomfort is extremely well-suited for evaluating sleep quality. Nevertheless, there is a dearth of studies that analyze the correlation between HRV and sleep quality during waking. The aim of this study is to create a machine-(ML) learning model that uses HRV data to estimate sleep quality, as evaluated by the Pittsburgh Sleep Quality Index (PSQI). The measurement of HRV was conducted using a wearable photo-plethysmography (PPG) sensor positioned on the fingertip. Subsequently, models were created to classify sleep quality based on the PSQI score. By employing the current approach, a classification good accuracy of 76.7% was achieved. In summary, this study has the potential to facilitate the use of wearable and contactless technology for monitoring sleep quality in ergonomic applications.