Sharp detection of oscillation packets in rich time-frequency representations of neural signals

Brain oscillations most often occur in bursts, called oscillation packets, which span a finite extent in time and frequency. Recent studies have shown that these packets portray a much more dynamic picture of synchronization and transient communication between sites than previously thought. To understand their nature and statistical properties, techniques are needed to objectively detect oscillation packets and to quantify their temporal and frequency extent, as well as their magnitude. There are various methods to detect bursts of oscillations. The simplest ones divide the signal into band limited sub-components, quantifying the strength of the resulting components. These methods cannot by themselves cope with broadband transients that look like genuine oscillations when restricted to a narrow band. The most successful detection methods rely on time-frequency representations, which can readily show broadband transients and harmonics. However, the performance of such methods is conditioned by the ability of the representation to localize packets simultaneously in time and frequency, and by the capabilities of packet detection techniques, whose current state of the art is limited to extraction of bounding boxes. Here, we focus on the second problem, introducing two detection methods that use concepts derived from clustering and topographic prominence. These methods are able to delineate the packets’ precise contour in the time-frequency plane. We validate the new approaches using both synthetic and real data recorded in humans and animals and rely on a super-resolution time-frequency representation, namely the superlets, as input to the detection algorithms. In addition, we define robust tests for benchmarking and compare the new methods to previous techniques. Results indicate that the two methods we introduce shine in low signal-to-noise ratio conditions, where they only miss a fraction of packets undetected by previous methods. Finally, algorithms that delineate precisely the border of spectral features and their subcomponents offer far more valuable information than simple rectangular bounding boxes (time and frequency span) and can provide a solid foundation to investigate neural oscillations’ dynamics

Can YOLO Detect Retinal Pathologies? A Step Towards Automated OCT Analysis

Background: Optical Coherence Tomography has become a common imaging technique that enables a non-invasive and detailed visualization of the retina and allows for the identification of various diseases. Through the advancement of technology, the volume and complexity of OCT data have rendered manual analysis infeasible, creating the need for automated means of detection. Methods: This study investigates the ability of state-of-the-art object detection models, including the latest YOLO versions (from v8 to v12), YOLO-World, YOLOE, and RT-DETR, to accurately detect pathological biomarkers in two retinal OCT datasets. The AROI dataset focuses on fluid detection in Age-related Macular Degeneration, while the OCT5k dataset contains a wide range of retinal pathologies. Results: The experiments performed show that YOLOv12 offers the best balance between detection accuracy and computational efficiency, while YOLOE manages to consistently outperform all other models across both datasets and most classes, particularly in detecting pathologies that cover a smaller area. Conclusions: This work provides a comprehensive benchmark of the capabilities of state-of-the-art object detection for medical applications, specifically for identifying retinal pathologies from OCT scans, offering insights and a starting point for the development of future automated solutions for analysis in a clinical setting

A Study of Deep Learning Models for Audio Classification of Infant Crying in a Baby Monitoring System

This study investigates the ability of well-known deep learning models, such as ResNet and EfficientNet, to perform audio-based infant cry detection. By comparing the performance of different machine learning algorithms, this study seeks to determine the most effective approach for the detection of infant crying, enhancing the functionality of baby monitoring systems and contributing to a more advanced understanding of audio-based deep learning applications. Understanding and accurately detecting a baby’s cries is crucial for ensuring their safety and well-being, a concern shared by new and expecting parents worldwide. Despite advancements in child health, as noted by UNICEF’s 2022 report of the lowest ever recorded child mortality rate, there is still room for technological improvement. This paper presents a comprehensive evaluation of deep learning models for infant cry detection, analyzing the performance of various architectures on spectrogram and MFCC feature representations. A key focus is the comparison between pretrained and non-pretrained models, assessing their ability to generalize across diverse audio environments. Through extensive experimentation, ResNet50 and DenseNet trained on spectrograms emerged as the most effective architectures, significantly outperforming other models in classification accuracy. Additionally, the study investigates the impact of feature extraction techniques, dataset augmentation, and model fine-tuning, providing deeper insights into the role of representation learning in audio classification. The findings contribute to the growing field of audio-based deep learning applications, offering a detailed comparative study of model architectures, feature representations, and training strategies for infant cry detection

Improved space breakdown method – A robust clustering technique for spike sorting

Space Breakdown Method (SBM) is a clustering algorithm that was developed specifically for low-dimensional neuronal spike sorting. Cluster overlap and imbalance are common characteristics of neuronal data that produce difficulties for clustering methods. SBM is able to identify overlapping clusters through its design of cluster centre identification and the expansion of these centres. SBM’s approach is to divide the distribution of values of each feature into chunks of equal size. In each of these chunks, the number of points is counted and based on this number the centres of clusters are found and expanded. SBM has been shown to be a contender for other well-known clustering algorithms especially for the particular case of two dimensions while being too computationally expensive for high-dimensional data. Here, we present two main improvements to the original algorithm in order to increase its ability to deal with high-dimensional data while preserving its performance: the initial array structure was substituted with a graph structure and the number of partitions has been made feature-dependent, denominating this improved version as the Improved Space Breakdown Method (ISBM). In addition, we propose a clustering validation metric that does not punish overclustering and such obtains more suitable evaluations of clustering for spike sorting. Extracellular data recorded from the brain is unlabelled, therefore we have chosen simulated neural data, to which we have the ground truth, to evaluate more accurately the performance. Evaluations conducted on synthetic data indicate that the proposed improvements reduce the space and time complexity of the original algorithm, while simultaneously leading to an increased performance on neural data when compared with other state-of-the-art algorithms.Code available athttps://github.com/ArdeleanRichard/Space-Breakdown-Method.</jats:sec

A study of autoencoders as a feature extraction technique for spike sorting

Spike sorting is the process of grouping spikes of distinct neurons into their respective clusters. Most frequently, this grouping is performed by relying on the similarity of features extracted from spike shapes. In spite of recent developments, current methods have yet to achieve satisfactory performance and many investigators favour sorting manually, even though it is an intensive undertaking that requires prolonged allotments of time. To automate the process, a diverse array of machine learning techniques has been applied. The performance of these techniques depends however critically on the feature extraction step. Here, we propose deep learning using autoencoders as a feature extraction method and evaluate extensively the performance of multiple designs. The models presented are evaluated on publicly available synthetic and real “in vivo” datasets, with various numbers of clusters. The proposed methods indicate a higher performance for the process of spike sorting when compared to other state-of-the-art techniques

A study of autoencoders as a feature extraction technique for spike sorting

Spike sorting is the process of grouping spikes of distinct neurons into their respective clusters. Most frequently, this grouping is performed by relying on the similarity of features extracted from spike shapes. In spite of recent developments, current methods have yet to achieve satisfactory performance and many investigators favour sorting manually, even though it is an intensive undertaking that requires prolonged allotments of time. To automate the process, a diverse array of machine learning techniques has been applied. The performance of these techniques depends however critically on the feature extraction step. Here, we propose deep learning using autoencoders as a feature extraction method and evaluate extensively the performance of multiple designs. The models presented are evaluated on publicly available synthetic and real “in vivo” datasets, with various numbers of clusters. The proposed methods indicate a higher performance for the process of spike sorting when compared to other state-of-the-art techniques.</jats:p

A study of autoencoders as a feature extraction technique for spike sorting.

Spike sorting is the process of grouping spikes of distinct neurons into their respective clusters. Most frequently, this grouping is performed by relying on the similarity of features extracted from spike shapes. In spite of recent developments, current methods have yet to achieve satisfactory performance and many investigators favour sorting manually, even though it is an intensive undertaking that requires prolonged allotments of time. To automate the process, a diverse array of machine learning techniques has been applied. The performance of these techniques depends however critically on the feature extraction step. Here, we propose deep learning using autoencoders as a feature extraction method and evaluate extensively the performance of multiple designs. The models presented are evaluated on publicly available synthetic and real "in vivo" datasets, with various numbers of clusters. The proposed methods indicate a higher performance for the process of spike sorting when compared to other state-of-the-art techniques

Data_Sheet_1_Improved space breakdown method – A robust clustering technique for spike sorting.docx

Space Breakdown Method (SBM) is a clustering algorithm that was developed specifically for low-dimensional neuronal spike sorting. Cluster overlap and imbalance are common characteristics of neuronal data that produce difficulties for clustering methods. SBM is able to identify overlapping clusters through its design of cluster centre identification and the expansion of these centres. SBM’s approach is to divide the distribution of values of each feature into chunks of equal size. In each of these chunks, the number of points is counted and based on this number the centres of clusters are found and expanded. SBM has been shown to be a contender for other well-known clustering algorithms especially for the particular case of two dimensions while being too computationally expensive for high-dimensional data. Here, we present two main improvements to the original algorithm in order to increase its ability to deal with high-dimensional data while preserving its performance: the initial array structure was substituted with a graph structure and the number of partitions has been made feature-dependent, denominating this improved version as the Improved Space Breakdown Method (ISBM). In addition, we propose a clustering validation metric that does not punish overclustering and such obtains more suitable evaluations of clustering for spike sorting. Extracellular data recorded from the brain is unlabelled, therefore we have chosen simulated neural data, to which we have the ground truth, to evaluate more accurately the performance. Evaluations conducted on synthetic data indicate that the proposed improvements reduce the space and time complexity of the original algorithm, while simultaneously leading to an increased performance on neural data when compared with other state-of-the-art algorithms.Code available athttps://github.com/ArdeleanRichard/Space-Breakdown-Method.</p

Comparison of different autoencoder variations vs state-of-the-art methods on real data channel 6, the optimal value of neighbours for Isomap was found as 30 through grid search.

The metrics indicate that for this dataset the AE variant is able to outperform the conventional feature extraction algorithm with regard to all metrics while the other variants have similar performances to that of PCA and ICA. The values indicating the highest performance for each metric have been bolded.</p

Impact of alignment.

PCA applied on Sim14 with and without alignment. The white cluster is kept together but the overlap with the blue cluster remains.</p