Automated algorithms for seizure forecast: a systematic review and meta-analysis.

This study aims to review the proposed methodologies and reported performances of automated algorithms for seizure forecast. A systematic review was conducted on studies reported up to May 10, 2024. Four databases and registers were searched, and studies were included when they proposed an original algorithm for automatic human epileptic seizure forecast that was patient specific, based on intraindividual cyclic distribution of events and/or surrogate measures of the preictal state and provided an evaluation of the performance.

Exploring the electrical robustness of conductive textile fasteners for wearable devices in different human motion conditions.

Conventional snap fasteners used in clothing are often used as electrical connectors in e-textile and wearable applications for signal transmission due to their wide availability and ease of use. Nonetheless, limited research exists on the validation of these fasteners, regarding the impact of contact-induced high-amplitude artefacts, especially under motion conditions. In this work, three types of fasteners were used as electromechanical connectors, establishing the interface between a regular sock and an acquisition device.

A Magnetic Field-Based Wearable Respiration Sensor for Real-Time Monitoring During Pulmonary Rehabilitation.

Objective: This work explores Hall effect sensing paired with a permanent magnet, in the context of pulmonary rehabilitation exercise training.

Methods: Experimental evaluation was performed considering as reference the gold-standard of respiratory monitoring, an airflow transducer, and performance was compared to another wearable device with analogous usability - a piezoelectric sensor. A total of 16 healthy participants performed 15 activities, representative of pulmonary rehabilitation exercises, simultaneously using all devices.

A real-world dataset of group emotion experiences based on physiological data.

Affective computing has experienced substantial advancements in recognizing emotions through image and facial expression analysis. However, the incorporation of physiological data remains constrained. Emotion recognition with physiological data shows promising results in controlled experiments but lacks generalization to real-world settings.

Effect of mixed Mycobacterium tuberculosis infection on rapid molecular diagnostics among patients starting MDR-TB treatment in Uganda.

Background: Mixed M. tuberculosis (MTB) infection occurs when one is infected with more than one clonally distinct MTB strain. This form of infection can assist MTB strains to acquire additional mutations, facilitate the spread of drug-resistant strains, and boost the rate of treatment failure.

PreEpiSeizures: description and outcomes of physiological data acquisition using wearable devices during video-EEG monitoring in people with epilepsy.

The PreEpiSeizures project was created to better understand epilepsy and seizures through wearable technologies. The motivation was to capture physiological information related to epileptic seizures, besides Electroencephalography (EEG) during video-EEG monitorings. If other physiological signals have reliable information of epileptic seizures, unobtrusive wearable technology could be used to monitor epilepsy in daily life.

Effect of mixed Mycobacterium tuberculosis infection on rapid molecular diagnostics among patients starting MDR-TB treatment in Uganda.

Background: We evaluated the effect of mixed-MTB strain infection on the performance of Line Probe Assay (LPA) and GeneXpert MTB/RIF (Xpert) assays among patients initiating MDR-TB treatment in Uganda.

Methods: This was a cross-sectional study using sputum specimens collected from participants screened for STREAM 2 clinical trial between October 2017 and October 2019. Samples from 62 MTB smear-positive patients and rifampicin-resistant patients from the peripheral health facilities were processed for Xpert and LPA as screening tests for eligibility in the trial.

A novel approach to automatic seizure detection using computer vision and independent component analysis.

Objective: Epilepsy is a neurological disease that affects ~50 million people worldwide, 30% of which have refractory epilepsy and recurring seizures, which may contribute to higher anxiety levels and poorer quality of life. Seizure detection may contribute to addressing some of the challenges associated with this condition, by providing information to health professionals regarding seizure frequency, type, and/or location in the brain, thereby improving diagnostic accuracy and medication adjustment, and alerting caregivers or emergency services of dangerous seizure episodes. The main focus of this work was the development of an accurate video-based seizure-detection method that ensured unobtrusiveness and privacy preservation, and provided novel approaches to reduce confounds and increase reliability.

Morphological Autoencoders for Beat-by-Beat Atrial Fibrillation Detection Using Single-Lead ECG.

Engineered feature extraction can compromise the ability of Atrial Fibrillation (AFib) detection algorithms to deliver near real-time results. Autoencoders (AEs) can be used as an automatic feature extraction tool, tailoring the resulting features to a specific classification task. By coupling an encoder to a classifier, it is possible to reduce the dimension of the Electrocardiogram (ECG) heartbeat waveforms and classify them.

Immunohistochemical detection of double-stranded RNA in formalin-fixed paraffin-embedded tissue.

Double-stranded RNA (dsRNA) is produced during most viral infections, and immunohistochemical detection of dsRNA has been proposed as a potential screening marker for viral replication. The anti-dsRNA monoclonal antibody clone 9D5 is more sensitive than the established clone J2 but has not been validated in formalin-fixed paraffin-embedded (FFPE) tissue. This study aimed to test and compare the performance of the anti-dsRNA monoclonal antibodies, 9D5 and J2, in FFPE tissue using an automated staining platform.

Feasibility of Electrodermal Activity and Photoplethysmography Data Acquisition at the Foot Using a Sock Form Factor.

Wearable devices have been shown to play an important role in disease prevention and health management, through the multimodal acquisition of peripheral biosignals. However, many of these wearables are exposed, limiting their long-term acceptability by some user groups. To overcome this, a wearable smart sock integrating a PPG sensor and an EDA sensor with textile electrodes was developed.

Pre-injury performance is most important for predicting the level of match participation after Achilles tendon ruptures in elite soccer players: a study using a machine learning classifier.

Purpose: Achilles tendon ruptures (ATR) are career-threatening injuries in elite soccer players due to the decreased sports performance they commonly inflict. This study presents an exploratory data analysis of match participation before and after ATRs and an evaluation of the performance of a machine learning (ML) model based on pre-injury features to predict whether a player will return to a previous level of match participation.

Methods: The website transfermarkt.

A Brief Introduction to Magnetoencephalography (MEG) and Its Clinical Applications.

Magnetoencephalography (MEG) plays a pivotal role in the diagnosis of brain disorders. In this review, we have investigated potential MEG applications for analysing brain disorders. The signal-to-noise ratio (SNRMEG = 2.

EpiBOX: An Automated Platform for Long-Term Biosignal Collection.

Biosignals represent a first-line source of information to understand the behavior and state of human biological systems, often used in machine learning problems. However, the development of healthcare-related algorithms that are both personalized and robust requires the collection of large volumes of data to capture representative instances of all possible states. While the rise of flexible biosignal acquisition solutions has enabled the expedition of data collection, they often require complicated frameworks or do not provide the customization required in some research contexts.

Neuroorganoleptics: Organoleptic Testing Based on Psychophysiological Sensing.

There is an increasing interest, in consumer behaviour research related to food and beverage, in taking a step further from the traditional self-report questionnaires and organoleptic properties assessment. With the growing availability of psychophysiological data acquisition devices, and advancements in the study of the underlying signal sources seeking affective state assessment, the use of psychophysiological data analysis is a natural evolution in organoleptic testing. In this paper we propose a protocol for what can be defined as neuroorganoleptic analysis, a method that combines traditional approaches with psychophysiological data acquired during sensory testing.

Ventilation Management and Outcomes for Subjects With Neuromuscular Disorders Admitted to ICUs With Acute Respiratory Failure.

Background: Patients with neuromuscular disorders (NMD) share the risk of acute respiratory failure (ARF) leading to ICU admissions. Noninvasive ventilation (NIV) is often proposed as an alternative to invasive ventilation. This study describes clinical features, ventilation management, and outcomes of subjects with NMD admitted to ICU and managed for ARF.

Immune Checkpoint Inhibitor-Induced Polymyositis and Myasthenia Gravis with Fatal Outcome.

We here report on a 74-year-old man diagnosed with a pT3cN0 -mutated and mismatch repair-deficient adenocarcinoma in the colon ascendens and 3 liver metastases. After hemicolectomy, the patient received treatment with the PD-1 inhibitor pembrolizumab. Three weeks later (on day 22), laboratory tests showed leukocytosis and an increase in transaminases; immune checkpoint inhibitor (ICI)-induced hepatitis was suspected and prednisolone therapy was initiated.

Emotion Assessment Using Feature Fusion and Decision Fusion Classification Based on Physiological Data: Are We There Yet?

Emotion recognition based on physiological data classification has been a topic of increasingly growing interest for more than a decade. However, there is a lack of systematic analysis in literature regarding the selection of classifiers to use, sensor modalities, features and range of expected accuracy, just to name a few limitations. In this work, we evaluate emotion in terms of low/high arousal and valence classification through Supervised Learning (SL), Decision Fusion (DF) and Feature Fusion (FF) techniques using multimodal physiological data, namely, Electrocardiography (ECG), Electrodermal Activity (EDA), Respiration (RESP), or Blood Volume Pulse (BVP).

Morphological autoencoders for apnea detection in respiratory gating radiotherapy.

Background And Objective: Respiratory gating training is a common technique to increase patient proprioception, with the goal of (e.g.) minimizing the effects of organ motion during radiotherapy.

ECG Biometrics Using Deep Learning and Relative Score Threshold Classification.

The field of biometrics is a pattern recognition problem, where the individual traits are coded, registered, and compared with other database records. Due to the difficulties in reproducing Electrocardiograms (ECG), their usage has been emerging in the biometric field for more secure applications. Inspired by the high performance shown by Deep Neural Networks (DNN) and to mitigate the intra-variability challenges displayed by the ECG of each individual, this work proposes two architectures to improve current results in both identification (finding the registered person from a sample) and authentication (prove that the person is whom it claims) processes: Temporal Convolutional Neural Network (TCNN) and Recurrent Neural Network (RNN).

Biosignal-Based Multimodal Emotion Recognition in a Valence-Arousal Affective Framework Applied to Immersive Video Visualization.

Many emotion recognition schemes have been proposed in the state-of-the-art. They generally differ in terms of the emotion elicitation methods, target emotional states to recognize, data sources or modalities, and classification techniques. In this work several biosignals are explored for emotion assessment during immersive video visualization, collecting multimodal data from Electrocardiography (ECG), Electrodermal Activity (EDA), Blood Volume Pulse (BVP) and Respiration sensors.

An AARS variant as the likely cause of Swedish type hereditary diffuse leukoencephalopathy with spheroids.

Swedish type Hereditary Diffuse Leukoencephalopathy with Spheroids (HDLS-S) is a severe adult-onset leukoencephalopathy with the histopathological hallmark of neuraxonal degeneration with spheroids, described in a large family with a dominant inheritance pattern. The initial stage of the disease is dominated by frontal lobe symptoms that develop into a rapidly advancing encephalopathy with pyramidal, deep sensory, extrapyramidal and optic tract symptoms. Median survival is less than 10 years.

Positive and Negative Evidence Accumulation Clustering for Sensor Fusion: An Application to Heartbeat Clustering.

In this work, a new clustering algorithm especially geared towards merging data arising from multiple sensors is presented. The algorithm, called PN-EAC, is based on the ensemble clustering paradigm and it introduces the novel concept of negative evidence. PN-EAC combines both positive evidence, to gather information about the elements that should be grouped together in the final partition, and negative evidence, which has information about the elements that should not be grouped together.

Benchmarking of the BITalino biomedical toolkit against an established gold standard.

The low-cost multimodal platform BITalino is being increasingly used for educational and research purposes. However, there is still a lack of well-structured work comparing data acquired by this toolkit against a reference device, using established experimental protocols. This work intends to fill the said gap by benchmarking the performance of BITalino against the BioPac MP35 Student Lab Pro device.