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Hochschulmedizin Zürich SleepLoop

Ecosystem Development

Wearable EEG monitoring and stimulation
Walter Karlen (ETH), Reto Huber (UZH, Kinderspital), Nicole Wenderoth (ETH)

We will enable long-term, individualized in-home sleep enhancement with the development of a wearable and provide a first proof of concept for a mobile device to enhance slow waves during deep sleep and deliver important information about long-term effects of such an application on sleep quality and health. This novel system will be useful for long-term experiments in healthy and clinical populations, scaling to large patient cohorts.

Stimulation parameter optimization
Reto Huber (UZH, Kinderspital), Walter Karlen (ETH)

In this sub-project, we will examine whether acoustic stimulation can be targeted to modulate slow waves locally, and whether the same approach can be used for a reduction of slow waves. We will establish the parameters needed for automatic detection of slow wave sleep and efficient slow wave modulation. A locally targeted, specific manipulation of slow waves should reduce side effects by sparing brain areas that show normal functionality and lead to benefits for a broader range of patient populations in the future.

Personalization strategy based on motor behavior
Roger Gassert (ETH), Walter Karlen (ETH)

When targeting a specific disease, a feedback modality should provide information regarding the major desired outcomes of the sleep enhancement. In Parkinson patients, the sleep parameters would be supplemented by the monitoring of motor behavior. We will use these modalities for developing novel strategies that will automatically measure the success of the sleep enhancement and use this measure for personalizing the stimulation protocol.

Diagnosis of central disorders of hypersomnolence with wearable SleepLoop device
Christian Baumann (USZ, UZH), Walter Karlen (ETH), Reto Huber (Kinderspital)

The aim of this new sub-project is to validate the SleepLoop device as a cost-effective novel diagnostic tool embedding EEG measures in a user-friendly wearable system and to provide accurate measures and novel biomarkers to distinguish distinct central disorders of hypersomnolence.