Although deep learning algorithms have proven their efficiency in automatic sleep staging, the widespread skepticism about their "black-box" nature has limited its clinical acceptance. In this study, we propose WaveSleepNet, an interpretable neural network for sleep staging that reasons in a similar way to sleep experts. In this network, we utilize the latent space representations generated during training to identify characteristic wave prototypes corresponding to different sleep stages. The feature representation of an input signal is segmented into patches within the latent space, each of which is compared against the learned wave prototypes. The proximity between these patches and the wave prototypes is quantified through scores, indicating the prototypes' presence and relative proportion within the signal. The scores are served as the decision-making criteria for final sleep staging. During training, an ensemble of loss functions is employed for the prototypes' diversity and robustness. Furthermore, the learned wave prototypes are visualized by analysing occlusion sensitivity. The efficacy of WaveSleepNet is validated across three public datasets, achieving sleep staging performance that are on par with the state-of-the-art models when several WaveSleepNets are combine into a larger network. A detailed case study examined the decision-making process of the WaveSleepNet which aligns closely with American Academy of Sleep Medicine (AASM) manual guidelines. Another case study systematically explained the misidentified reason behind each sleep stage. WaveSleepNet's transparent process provides specialists with direct access to the physiological significance of its criteria, allowing for future adaptation or enrichment by sleep experts.

"Dataloop has pinpointed a large obstacle in an important and fast-growing market. Most companies these days have a dedicated team working on data management and AI integrations, and they all face the same challenges," said Christian Noske, Partner at NGP Capital.

The principal calls this a mindful school. Johane Ligondé is effusively warm but with the kind of emotional solidity you'd expect from someone who wakes each morning to manage more than 1,000 kids at the only public middle school in the village of Freeport in Long Island, New York. She is also an aromatherapist and life coach who hangs a sign reading "I AM AN OPTIMIST" in her windowless office. At John W. Dodd Middle School, some of the students' primary struggles are common to many young teenagers: depression, anxiety, self-harm and the looming shadow of sudden violence. So every morning during homeroom, a student or staff member leads the entire building through eight minutes of breathing meditation over the PA system. In detention, students are "invited," Ligondé said, to do mindfulness exercises, "so it's not just a space for punishment, it's a space for reflection." A "social-emotional learning curriculum" has been introduced, teaching them conflict and relationship management. At 11 AM, four periods into a drizzly Wednesday in June, Ligondé watches seventh graders shuffle in for science class and take their seats between model skeletons and posters of plant-cell structures. Some stare blankly into the middle distance. Their assignment is to meditate. Half the students slump their foreheads into the crook of their arm, resting on top of tables or thick ring binders. They are the control group. The other half strap on purple, cardboard VR headsets and clip pulse monitors to their fingers. The teacher, Vanessa Vidalon, turns down the lights, and the class hushes, save for some snapping of elastic headbands over white earbuds and the clacks of phones dropped on desks.