Exploring the Use of Granger Causality for the Identification of Chemical Exposure Based on Physiological Data

Wearable sensors offer new opportunities for the early detection and identification of toxic chemicals in situations where medical evaluation is not immediately possible. We previously found that continuously recorded physiology in guinea pigs can be used for early detection of exposure to an opioid (fentanyl) or a nerve agent (VX), as well as...

Early detection of exposure to toxic chemicals using continuously recorded multi-sensor physiology

Early detection of exposure to a toxic chemical, e.g., in a military context, can be life-saving. We propose to use machine learning techniques and multiple continuously measured physiological signals to detect exposure, and to identify the chemical agent. Such detection and identification could be used to alert individuals to take...

Cardiopulmonary coupling indices to assess weaning readiness from mechanical ventilation

The ideal moment to withdraw respiratory supply of patients under Mechanical Ventilation at Intensive Care Units (ICU), is not easy to be determined for clinicians. Although the Spontaneous Breathing Trial (SBT) provides a measure of the patients’ readiness, there is still around 15–20% of predictive failure rate. This work is a proof of...

Utility metric for unsupervised feature selection

Feature selection techniques are very useful approaches for dimensionality reduction in data analysis. They provide interpretable results by reducing the dimensions of the data to a subset of the original set of features. When the data lack annotations, unsupervised feature selectors are required for their analysis. Several algorithms for...

Model-Based Evaluation of Methods for Respiratory Sinus Arrhythmia Estimation

Objective: Respiratory sinus arrhythmia (RSA) refers to heart rate oscillations synchronous with respiration, and it is one of the major representations of cardiorespiratory coupling. Its strength has been suggested as a biomarker to monitor different conditions, and diseases. Some approaches have been proposed to quantify the RSA, but it is...

A Comparative Study of ECG-derived Respiration in Ambulatory Monitoring using the Single-lead ECG

Cardiorespiratory monitoring is crucial for the diagnosis and management of multiple conditions such as stress and sleep disorders. Therefore, the development of ambulatory systems providing continuous, comfortable, and inexpensive means for monitoring represents an important research topic. Several techniques have been proposed in the...

Using Biosensors and Digital Biomarkers to Assess Response to Cardiac Rehabilitation: Observational Study

Background: Cardiac rehabilitation (CR) is known for its beneficial effects on functional capacity and is a key component within current cardiovascular disease management strategies. In addition, a larger increase in functional capacity is accompanied by better clinical outcomes. However, not all patients respond in a similar way to CR....

Short-term exercise progression of cardiovascular patients throughout cardiac rehabilitation: An observational study

Cardiac rehabilitation (CR) is a highly recommended secondary prevention measure for patients with diagnosed cardiovascular disease. Unfortunately, participation rates are low due to enrollment and adherence issues. As such, new CR delivery strategies are of interest, as to improve overall CR delivery. The goal of the study was to obtain a...

Can Laplacian Eigenmaps Be Used for Differentiation between Healthy Subjects and Patients with Corrected Tetralogy of Fallot?

Tetralogy of Fallot (ToF) is a congenital structural heart disease. While early diagnosis and corrective surgery allow most patients to live normal lives, some patients slowly deteriorate. The current inability to quantify the deterioration and predict these events prompts a data driven approach. Laplacian Eigenmaps (LEs) are a dimensionality...

Predicting Left Ventricular Mass Using ECG, Demographic and DXA Features

The gold standard for the assessment of cardiac mass is cardiac magnetic resonance imaging (CMR). However, it is costly and requires specific expertise. Electrocardiographic (ECG) criteria could provide a low-cost solution, but have shown to be poorly correlated with LVM in athletes. We hypothesize that this poor correlation could be overcome...

Unobtrusive, Through-Clothing ECG and Bioimpedance Monitoring in Sleep Apnea Patients

A real-life validation of a system for simultaneous acquisition of capacitively-coupled ECG (ccECG) and capacitively-coupled bioimpedance (ccBioz) is presented. The heart rate (HR) and respiration rate (RR) estimation performance was evaluated using polysomnography (PSG) signals as ground-truth, in recordings from 28 patients with suspected...

Fragmented QRS Dynamics towards Electrical Storm in ICD Patients

Electrical storm (ES) in ICD patients, defined as 3 or more appropriate ICD interventions within a time span of 24 hours, is a medical emergency associated with adverse outcome. However, it is debated if ES is only a marker of progressive near end-stage cardiac disease or an ar-rhythmogenic entity on its own. Better understanding and...

A Robust Estimation of the Cardiorespiratory Coupling in the Presence of Abnormal Beats

Respiratory sinus arrhythmia (RSA) is one of the forms of cardiorespiratory coupling. It has been suggested as a potential biomarker for diverse illnesses and conditions. In general, methods for non-invasive quantification of the RSA combine information from heart rate variability (HRV) and respiratory signals. Abnormal beats, which commonly...

Sleep-Wake Classification for Home Monitoring of Sleep Apnea Patients

Sleep apnea is a common sleep disorder, whose diagnosis can strongly benefit from home-based screening. As the total sleep time is essential to assess the sleep apnea severity, a sleep-wake classifier was developed based on heart rate and respiration. These two signals were selected as they can be measured using unobtrusive sensors. A 1D...

Wearable monitoring and interpretable machine learning can objectively track progression in patients during cardiac rehabilitation

Cardiovascular diseases (CVD) are often characterized by their multifactorial complexity. This makes remote monitoring and ambulatory cardiac rehabilitation (CR) therapy challenging. Current wearable multimodal devices enable remote monitoring. Machine learning (ML) and artificial intelligence (AI) can help in tackling multifaceted datasets....

Personalizing Heart Rate-Based Seizure Detection Using Supervised SVM Transfer Learning

Objective: Automated seizure detection is a key aspect of wearable seizure warning systems. As a result, the quality of life of refractory epilepsy patients could be improved. Most state-of-the-art algorithms for heart rate-based seizure detection use a so-called patient-independent approach, which do not take into account patient-specific...