Sweat artifact and respiratory artifact occurring simultaneously in polysomnogram

SLEEP-SEE-THROUGH: Explainable Deep Learning for Sleep Event Detection and Quantification From Wearable Somnography

Evidence is rapidly accumulating that multifactorial nocturnal monitoring, through the coupling of wearable devices and deep learning, may be disruptive for early diagnosis and assessment of sleep disorders. In this work, optical, differential air-pressure and acceleration signals, acquired by a chest-worn sensor, are elaborated into five somnographic-like signals, which are then used to feed a deep network. This addresses a three-fold classification problem to predict the overall signal quality (normal, corrupted), three breathing-related patterns (normal, apnea, irregular) and three sleep-related patterns (normal, snoring, noise). In order to promote explainability, the developed architecture generates additional information in the form of qualitative (saliency maps) and quantitative (confidence indices) data, which helps to improve the interpretation of the predictions. Twenty healthy subjects enrolled in this study were monitored overnight for approximately ten hours during sleep. Somnographic-like signals were manually labeled according to the three class sets to build the training dataset. Both record- and subject-wise analyses were performed to evaluate the prediction performance and the coherence of the results. The network was accurate (0.96) in distinguishing normal from corrupted signals. Breathing patterns were predicted with higher accuracy (0.93) than sleep patterns (0.76). The prediction of irregular breathing was less accurate (0.88) than that of apnea (0.97). In the sleep pattern set, the distinction between snoring (0.73) and noise events (0.61) was less effective. The confidence index associated with the prediction allowed us to elucidate ambiguous predictions better. The saliency map analysis provided useful insights to relate predictions to the input signal content. While preliminary, this work supported the recent perspective on the use of deep learning to detect particular sleep events in multiple somnographic signals, thus representing a step towards bringing the use of AI-based tools for sleep disorder detection incrementally closer to clinical translation.

Sympathetic skin responses from the scalp evoked by electrical stimulation in seborrheic dermatitis

Although the role of autonomic nervous system in seborrheic dermatitis (SD) is still unclear, seborrhea is sometimes accepted as a sign of autonomic dysfunction in several nervous system diseases. Therefore, we aimed to investigate the sympathetic nervous system (SNS) activity in SD by recording sympathetic skin responses (SSR) from the scalp (S-SSR). Thirty-one control subjects and 22 SD patients were studied by evoking right and left S-SSR with electrical stimulation of the right median nerve at the wrist. Mean latencies and maximum amplitudes were calculated for both sides in each group. In seven out of 31 control subjects and in 13 out of 22 patients, the S-SSR could not be elicited on either side. There were four subjects with unilateral response in the patient group. There were significantly more non-responders among the patients with SD (P < 0.000). This study suggests that in SD, the autonomic nervous system may be involved. The S-SSR is a new site for recording SSR. The responses are relatively symmetrical and can be evoked easily by electrical stimulation, and may be used to evaluate the SNS function in SD patients and also in healthy subjects.