OBJECTIVE: Building large-scale data bases of biomedical signal recordings for training artificial-intelligence systems involves substantial human effort in data processing and annotation. In the case of event detection, experts need to exhaustively scroll through the recordings and highlight events of interest. METHODS: We propose an iterative annotation support algorithm with a human in the loop to improve the efficiency of the annotation process. Our algorithm generates proposal events based on an event detection model trained on incomplete annotations. The human only needs to verify candidate events proposed by the tool instead of scrolling through the entire data set. Our algorithm iterates between proposal generation and verification to leverage the human-in-the-loop feedback to obtain a growing set of event annotations. RESULTS: Our algorithm finds a substantial amount of events at a fraction of the human time spent when comparing with a benchmark method and the normal manual process, finding all events in one data set and 70% of events in another with the human-in-the-loop only viewing 20% of the data. CONCLUSION: Our results show that combining human and computer effort can substantially speed up the annotation process for events in biomedical signal processing. SIGNIFICANCE: Due to its simplicity and minimal reliance on task-specific information, our algorithm is broadly applicable, unlocking substantial improvements in the scalability and efficiency of biomedical signal annotation.

Change Point Detection (CPD) refers to the task of identifying abrupt changes in the characteristics or statistics of time series data. Recent advancements have led to a shift away from traditional model-based CPD approaches, which rely on predefined statistical distributions, toward neural network-based and distribution-free methods using autoencoders. However, many state-of-the-art methods in this category often neglect to explicitly leverage spatial information across multiple channels, making them less effective at detecting changes in cross-channel statistics. In this paper, we introduce an unsupervised, distribution-free CPD method that explicitly incorporates both temporal and spatial (cross-channel) information in multi-channel time series data based on the so-called Time-Invariant Representation (TIRE) autoencoder. Our evaluation, conducted on both simulated and real-life datasets, illustrates the significant advantages of our proposed multi-channel TIRE (MC-TIRE) method, which consistently delivers more accurate CPD results.

Change point detection (CPD) refers to the problem of detecting changes in the statistics of pseudo-stationary signals or time series. A recent trend in CPD research is to replace the traditional statistical tests with distribution-free autoencoder-based algorithms, which can automatically learn complex patterns in time series data. In particular, the so-called time-invariant representation (TIRE) models have gained traction, as these separately encode time-variant and time-invariant subfeatures, as opposed to traditional autoencoders. However, optimizing the trade-off between two loss terms, i.e., the reconstruction loss and the time-invariant loss, is challenging. To address this issue, we propose a novel loss function that elegantly combines both losses without the need for manually tuning a trade-off hyperparameter. We demonstrate that this new hyperparameter-free loss, in combination with a relatively simple convolutional neural network (CNN), consistently achieves superior or comparable performance compared to the manually-tuned baseline TIRE models across diverse benchmark datasets, both simulated and real-life. In addition, we present a representation analysis, demonstrating that the distribution of the time-invariant features extracted by our model is more concentrated within the same segment (more so than with previous TIRE models), which implies that these features can potentially be used for other applications, such as classification and clustering.