Electrocardiogram Quality Assessment Using Unsupervised Deep Learning.

OBJECTIVE: Noise and disturbances hinder effective interpretation of recorded ECG. To identify the clean parts of a recording, free from such disturbances, various quality indicators have been developed. Previous instances of these indicators focus on human-defined desirable properties of a clean signal. The reliance on human-specified properties places an inherent limitation on the potential power of signal quality indicators. To move away from this limitation, we propose a data-driven quality indicator. METHODS: We use an unsupervised deep learning model, the auto-encoder, to derive the quality indicator. For different quality assessment settings we compare the performance of our quality indicator with traditional indicators. RESULTS: The data-driven method performs consistently strong across tasks while performance of the traditional indicators varies strongly from task to task. CONCLUSION: This strong performance indicates the potential of data-driven quality indicators for use in ECG processing, removing the reliance on expert-specified desirable properties. SIGNIFICANCE: The proposed methodology can easily be extended towards learning quality indicators in other data modalities.

An Interpretable Performance Metric for Auditory Attention Decoding Algorithms in a Context of Neuro-Steered Gain Control.

In a multi-speaker scenario, a hearing aid lacks information on which speaker the user intends to attend, and therefore it often mistakenly treats the latter as noise while enhancing an interfering speaker. Recently, it has been shown that it is possible to decode the attended speaker from the brain activity, e.g., recorded by electroencephalography sensors. While numerous of these auditory attention decoding (AAD) algorithms appeared in the literature, their performance is generally evaluated in a non-uniform manner. Furthermore, AAD algorithms typically introduce a trade-off between the AAD accuracy and the time needed to make an AAD decision, which hampers an objective benchmarking as it remains unclear which point in each algorithm's trade-off space is the optimal one in a context of neuro-steered gain control. To this end, we present an interpretable performance metric to evaluate AAD algorithms, based on an adaptive gain control system, steered by AAD decisions. Such a system can be modeled as a Markov chain, from which the minimal expected switch duration (MESD) can be calculated and interpreted as the expected time required to switch the operation of the hearing aid after an attention switch of the user, thereby resolving the trade-off between AAD accuracy and decision time. Furthermore, we show that the MESD calculation provides an automatic and theoretically founded procedure to optimize the number of gain levels and decision time in an AAD-based adaptive gain control system.