Influence of mental stress on the pulse wave features of photoplethysmograms.

Mental stress is a major burden for our society. Invasive and non-invasive methods have been proposed to monitor and quantify it using various sensors on and off body. In this Letter, the authors investigated the use of the arm photoplethysmogram (PPG) to assess mental stress in laboratory conditions.

Assessing mental stress from the photoplethysmogram: a numerical study.

Objective: Mental stress is detrimental to cardiovascular health, being a risk factor for coronary heart disease and a trigger for cardiac events. However, it is not currently routinely assessed. The aim of this study was to identify features of the photoplethysmogram (PPG) pulse wave which are indicative of mental stress.

Noise reduction in rhythmic and multitrial biosignals with applications to event-related potentials.

A new noise reduction algorithm is presented for signals displaying repeated patterns or multiple trials. Each pattern is stored in a matrix, forming a set of events, which is termed multievent signal. Each event is considered as an affine transform of a basic template signal that allows for time scaling and shifting.

Carmeli's S index assesses motion and muscle artefact reduction in rowers' electrocardiograms.

Electrocardiograms (ECG) suffer high perturbations from motion and muscle activities when measured on the chest of rowers. However, for cardiac assessment and sport performance measures, ECG complexes and their time occurrences contain information of great value. We thus propose to use electromyogram (EMG) sensors placed on the distal pectoral muscle to provide a reference signal for motion and muscle activity cancellation.

Phase synchronization for the recognition of mental tasks in a brain-computer interface.

Brain-computer interfaces (BCIs) may be a future communication channel for motor-disabled people. In surface electroencephalogram (EEG)-based BCIs, the extracted features are often derived from spectral estimates and autoregressive models. We examined the usefulness of synchronization between EEG signals for classifying mental tasks.

A computer-aided detection of EEG seizures in infants: a singular-spectrum approach and performance comparison.

This paper presents a scalp electroencephalogram (EEG) seizure detection scheme based on singular spectrum analysis (SSA) and Rissanen minimum description length (MDL) model-order selection (SSA-MDL). Preprocessing of the signals allows for the drastic reduction of the number of false alarms. Statistical performance comparison with seizure detection schemes of Gotman et al.