CNN's capture local temporal and spectral patterns, which enables improved predictive performance, but they are often large and computationally expensive. The multitude of filters added by a CNN will impose some redundancy, where some filters are irrelevant to predictive performance. This redundancy elicited prohibitive costs incurred in resource-constrained real-time clinical settings. Existing compression and pruning methods are variably acceptable in that they all seek to limit the size of the model at the expense of accuracy. Specifically, when accuracy applies, price-performance tradeoffs are unacceptable if the application is medical, where an even greater expectation of reliability exists alongside cost-to-performance considerations. Methods derived from the vision literature that were developed to mitigate the impact of redundancy on the real-time operationalization of convolutional networks are unlikely to be effective with data obtained from EEG signals, due to the unique nature of those signals [4]. In contrast to vision data, these data sources are highly susceptible to noise and temporal variations. This paper presents a pruning framework that is sensitive to seizures for the detection of seizures. We first train a light-weight 1D CNN using early stopping to stabilize the generalization from the training data to the test data.

Cardiopulmonary resuscitation (CPR) is a critical, life-saving intervention aimed at restoring blood circulation and breathing in individuals experiencing cardiac arrest or respiratory failure. Accurate and real-time analysis of biomedical signals during CPR is essential for monitoring and decision-making, from the pre-hospital stage to the intensive care unit (ICU). However, CPR signals are often corrupted by noise and artifacts, making precise interpretation challenging. Traditional denoising methods, such as filters, struggle to adapt to the varying and complex noise patterns present in CPR signals. Given the high-stakes nature of CPR, where rapid and accurate responses can determine survival, there is a pressing need for more robust and adaptive denoising techniques. In this context, an unsupervised machine learning (ML) methodology is particularly valuable, as it removes the dependence on labeled data, which can be scarce or impractical in emergency scenarios. This paper introduces a novel unsupervised ML approach for denoising CPR signals using a multi-modality framework, which leverages multiple signal sources to enhance the denoising process. The proposed approach not only improves noise reduction and signal fidelity but also preserves critical inter-signal correlations (0.9993) which is crucial for downstream tasks. Furthermore, it outperforms existing methods in an unsupervised context in terms of signal-to-noise ratio (SNR) and peak signal-to-noise ratio (PSNR), making it highly effective for real-time applications. The integration of multi-modality further enhances the system's adaptability to various biomedical signals beyond CPR, improving both automated CPR systems and clinical decision-making.

Multimodality has recently gained attention in the medical domain, where imaging or video modalities may be integrated with biomedical signals or health records. Yet, two challenges remain: balancing the contributions of modalities, especially in cases with a limited amount of data available, and tackling missing modalities. To address both issues, in this paper, we introduce the AnchoreD multimodAl Physiological Transformer (ADAPT), a multimodal, scalable framework with two key components: (i) aligning all modalities in the space of the strongest, richest modality (called anchor) to learn a joint embedding space, and (ii) a Masked Multimodal Transformer, leveraging both inter- and intra-modality correlations while handling missing modalities. We focus on detecting physiological changes in two real-life scenarios: stress in individuals induced by specific triggers and fighter pilots' loss of consciousness induced by $g$-forces. We validate the generalizability of ADAPT through extensive experiments on two datasets for these tasks, where we set the new state of the art while demonstrating its robustness across various modality scenarios and its high potential for real-life applications.

Machine learning tasks involving biomedical signals frequently grapple with issues such as limited data availability, imbalanced datasets, labeling complexities, and the interference of measurement noise. These challenges often hinder the optimal training of machine learning algorithms. Addressing these concerns, we introduce BioDiffusion, a diffusion-based probabilistic model optimized for the synthesis of multivariate biomedical signals. BioDiffusion demonstrates excellence in producing high-fidelity, non-stationary, multivariate signals for a range of tasks including unconditional, label-conditional, and signal-conditional generation. Leveraging these synthesized signals offers a notable solution to the aforementioned challenges. Our research encompasses both qualitative and quantitative assessments of the synthesized data quality, underscoring its capacity to bolster accuracy in machine learning tasks tied to biomedical signals. Furthermore, when juxtaposed with current leading time-series generative models, empirical evidence suggests that BioDiffusion outperforms them in biomedical signal generation quality.

Today, I will walk you through the electrocardiogram (ECG) biomedical signal data with the aim of learning similarity representations between the two recorded signal data events. ECG is one of the most commonly heard types of signal data in context to human medical recordings. So, let's first simply understand what exactly is "Signal" in layman terms, what is an ECG signal, why is it needed, what exactly is Siamese Neural Network, how it can be useful towards comparing two vectors, and finally we will see an use-case starting with the ECG data analysis including uni/multivariate plotting, rolling window sum plots, data profiling, filtering outliers, detecting r-signal-to-signal peaks, and finally identifying the ECG signal similarities with Siamese Network model. The fundamental quantity of representing some information is called a "signal" in simple engineering terms. While in context to mathematical world, a signal is just a function that simply conveys some information, where the information could be a function of time [y y (t) ] or it could be function of spatial coordinates [ y y (x, y) ] or it could be a function of distance from source [ y y (r) ], etc. as an example.

To date, very few biomedical signals have transitioned from research applications to clinical applications. This is largely due to the lack of trust in the diagnostic ability of non-stationary signals. To reach the level of clinical diagnostic application, classification using high-quality signal features is necessary. While there has been considerable progress in machine learning in recent years, especially deep learning, progress has been quite limited in the field of feature engineering. This study proposes a feature extraction algorithm based on group intelligence which we call a Plant Root System (PRS) algorithm. Importantly, the correlation between features produced by this PRS algorithm and traditional features is low, and the accuracy of several widely-used classifiers was found to be substantially improved with the addition of PRS features. It is expected that more biomedical signals can be applied to clinical diagnosis using the proposed algorithm.