Benchmarking Real-Time Algorithms for In-Phase Auditory Stimulation of Low Amplitude Slow Waves With Wearable EEG Devices During Sleep

Abstract

OBJECTIVE: In-phase stimulation of EEG slow waves (SW) during deep sleep has shown to improve cognitive function. SW enhancement is particularly desirable in subjects with low-amplitude SW such as older adults or patients suffering from neurodegeneration. However, existing algorithms to estimate the up-phase of EEG suffer from a poor phase accuracy at low amplitudes and when SW frequencies are not constant. METHODS: We introduce two novel algorithms for real-time EEG phase estimation on autonomous wearable devices, a phase-locked loop (PLL) and, for the first time, a phase vocoder (PV). We compared these phase tracking algorithms with a simple amplitude threshold approach. The optimized algorithms were benchmarked for phase accuracy, the capacity to estimate phase at SW amplitudes between 20 and 60 V, and SW frequencies above 1 Hz on 324 home-based recordings from healthy older adults and Parkinson disease (PD) patients. Furthermore, the algorithms were implemented on a wearable device and the computational efficiency and the performance was evaluated in simulation and with a PD patient. RESULTS: All three algorithms delivered more than 70% of the stimulation triggers during the SW up-phase. The PV showed the highest capacity on targeting low-amplitude SW and SW with frequencies above 1 Hz. The hardware testing revealed that both PV and PLL have marginal impact on microcontroller load, while the efficiency of the PV was 4% lower. Active stimulation did not influence the phase tracking. CONCLUSION: This work demonstrated that phase-accurate auditory stimulation can also be delivered during fully remote sleep interventions in populations with low-amplitude SW.

Effects of Non-Pharmacological Sleep Interventions in Older Adults: A Systematic Review and Meta-Analysis

“…Although some studies measured deep sleep duration using a bioactive device or measured blood melatonin levels, most studies assessed subjective outcomes using questionnaires. In recent years, technological advances have enabled the use of noninvasive and efficient means, including the Dayzz app [ 61 ], the Personal Activity Intelligence e-Health program [ 62 ], the Calm mindfulness meditation app [ 18 ], and wearable devices [ 20 ]. Future experimental studies should employ objective sleep outcomes in addition to subjective assessments tailored to the characteristics of older adults.…”

“…Non-pharmacological interventions are particularly attractive to the general population because these interventions are perceived as not causing adverse effects [ 11 ]. Interventions such as exercise [ 12 ], aromatherapy [ 13 ], auricular acupressure [ 14 , 15 ], cognitive behavioral therapy [ 16 , 17 ], meditation [ 18 , 19 ], and stimulation therapy [ 20 ] have been validated and well utilized. However, sleep problems remain a substantial health issue and a highly prevalent health problem with unclear guidelines as to effective management [ 21 ].…”

Technical feasibility of using auditory phase-targeted stimulation after pediatric severe traumatic brain injury in an intensive care setting

“…Live visualisation of the EEG signal would enable realtime assessment of electrode connection, avoiding data loss due to bad signal quality, and, thus, a device allowing for online monitoring would be preferable. In the meantime, this has already been realized with the more integrated successor of the MHSL-SleepBand version 3 [32]. However, the biggest challenge, is the low patient numbers: In a period of over two years, only 4 out of 16 patients with severe TBI could be included in the study due to several reasons (Fig.…”

The promise of portable remote auditory stimulation tools to enhance slow‐wave sleep and prevent cognitive decline

“…Its biggest advantages are full methodological transparency, high configurability, and full availability of raw data. With multiple selectable amplitude-independent algorithms and detailed reports of their performance, including in low-amplitude data, SleepLoop ® is suitable to apply PLAS in older adults, both healthy and experiencing PD (Ferster et al, 2022). Studies specifically testing feasibility in people with MCI do not exist, but a sample of individuals with PD was chosen in the benchmarking report to investigate robustness and flexibility of the device and its PLAS algorithms under challenging conditions and high inter-individual variability regarding sleep patterns (Ferster et al, 2022).…”