Signal Processing of Random Physiological Signals

Lessard, Charles S.

doi:10.2200/s00012ed1v01y200602bme001

Cited by 21 publications

(11 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The importance of statistical features in physiological signal processing was reported by Lessard 22 . The feature extraction methodology and clinical significance of GSR features was investigated by Healey and Picard 7 , Schmidt and Walach 16 and Soleymani et al 23 .…”

Section: Feature Extraction and Selectionmentioning

confidence: 96%

Assessment of Driver Stress from Physiological Signals collected under Real-Time Semi-Urban Driving Scenarios

Singh¹,

Conjeti²,

Banerjee³

2014

IJCIS

View full text Add to dashboard Cite

Designing a wearable driver assist system requires extraction of relevant features from physiological signals like galvanic skin response and photoplethysmogram collected from automotive drivers during real-time driving. In the discussed case, four stress-classes were identified using cascade forward neural network (CASFNN) which performed consistently with minimal intra-and inter-subject variability. Task-induced stress-trends were tracked using -based regression model with CASFNN configuration. The proposed framework will enable proactive initiation of rescue and relaxation procedures during accidents and emergencies.

show abstract

Section: Feature Extraction and Selectionmentioning

confidence: 96%

Assessment of Driver Stress from Physiological Signals collected under Real-Time Semi-Urban Driving Scenarios

Singh¹,

Conjeti²,

Banerjee³

2014

IJCIS

View full text Add to dashboard Cite

show abstract

“…Standard procedure was applied: in the first step, we calculated the median value of each epoch and each sample was classified as a unit (above median value) or zero (below median value). Further, the number of sequences (the so-called, ''runs'') of units or zeroes within the epoch compared to the values in table [24] which are derived from distribution function of independent, ergodic and stationary data. If the number of runs falls within the region of acceptance at the given significance level (usually 0.05), the epoch is considered to be stationary.…”

Section: Stationarity Of Brain Signalsmentioning

confidence: 99%

Fractal dimension values of cerebral and cerebellar activity in rats loaded with aluminium

Keković

Ćulić

Martać

et al. 2010

Med Biol Eng Comput

View full text Add to dashboard Cite

Aluminium interferes with a variety of cellular metabolic processes in the mammalian nervous system and its intake might increase a risk of developing Alzheimer's disease (AD). While cerebral involvement even at the early stages of intoxication is well known, the role of cerebellum is underestimated. Our aim was to investigate cerebral and cerebellar electrocortical activity in adult male rats exposed to chronic aluminium treatment by nonlinear analytic tools. The adult rats in an aluminium-treated group were injected by AlCl(3), intraperitoneally (2 mg Al/kg, daily for 4 weeks). Fractal analysis of brain activity was performed off-line using Higuchi's algorithm. The average fractal dimension of electrocortical activity in aluminium-treated animals was lower than the average fractal dimension of electrocortical activity in the control rats, at cerebral but not at cerebellar level. The changes in the stationary and nonlinear properties of time series were more expressed in cerebral electrocortical activity than in cerebellar activity. This can be useful for developing effective diagnostic and therapeutic strategies in neurodegenerative diseases.

show abstract

“…A stationary signal is characterised as an equilibrium condition, whereas the statistical properties are time invariant. There are four categories involving stationary process: first-order, second-order, wide sense (WSS) and strict stationaries [22]. In practice, WSS is deemed adequate for signal analysis when the statistical moments that describe the signals do not change over time, as implemented by Martin et al in 1997 [21].…”

Section: Introductionmentioning

confidence: 99%

Assessment on Stationarity of EMG Signals with Different Windows Size During Isotonic Contractions

et al. 2017

View full text Add to dashboard Cite

Abstract:In order to analyse surface electromyography (EMG) signals, it is necessary to extract the features based on a time or frequency domain. These approaches are based on the mathematical assumption of signal stationarity. Stationarity of EMG signals is thoroughly examined, especially in isotonic contractions. According to research, conflicting results have been identified depending on varying window sizes. Therefore, in this study, the authors endeavoured to determine the suitable window size to analyse EMG signals during isotonic contractions utilising stationary tests, reverse arrangement (RA), and modified reverse arrangement (MRA). There were slight differences in the average percentages of signal stationarity for RA and MRA tests in 100 ms, 500 ms, and 1000 ms window sizes. However, there was none for the 200 ms window size. On average, a window size of 200 ms provided stationary information with 88.57% of EMG signals compared to other window sizes. This study also recommended the MRA test to determine EMG signals stationarity for future studies, as the performances were better in comparison to RA tests. However, the following recommendation is only valid for window sizes greater than 200 ms. For a real-time application, the size of the analysis window together with the processing time should be less than 300 ms and a window size of 200 ms is applicable for isotonic contractions.

show abstract

Signal Processing of Random Physiological Signals

Cited by 21 publications

References 4 publications

Assessment of Driver Stress from Physiological Signals collected under Real-Time Semi-Urban Driving Scenarios

Assessment of Driver Stress from Physiological Signals collected under Real-Time Semi-Urban Driving Scenarios

Fractal dimension values of cerebral and cerebellar activity in rats loaded with aluminium

Assessment on Stationarity of EMG Signals with Different Windows Size During Isotonic Contractions

Contact Info

Product

Resources

About