A novel robust index to assess beat-to-beat variability in heart rate time-series analysis

Bang

Med. Biol. Eng. Comput.

2004

807

352

A physiological signal-based emotion recognition system is reported. The system was developed to operate as a user-independent system, based on physiological signal databases obtained from multiple subjects. The input signals were electrocardiogram, skin temperature variation and electrodermal activity, all of which were acquired without much discomfort from the body surface, and can reflect the influence of emotion on the autonomic nervous system. The system consisted of preprocessing, feature extraction and pattern classification stages. Preprocessing and feature extraction methods were devised so that emotion-specific characteristics could be extracted from short-segment signals. Although the features were carefully extracted, their distribution formed a classification problem, with large overlap among clusters and large variance within clusters. A support vector machine was adopted as a pattern classifier to resolve this difficulty. Correct-classification ratios for 50 subjects were 78.4% and 61.8%, for the recognition of three and four categories, respectively.

Section: Preprocessing Waveform Detection and Feature Extractionmentioning

confidence: 99%

Emotion recognition system using short-term monitoring of physiological signals

Bang

Med. Biol. Eng. Comput.

2004

807

352

“…HRV contains abundant information on ANS activity, including degrees of the sympathetic and parasympathetic nervous system activities [Task Force of the European Society of Cardiology & The North American Society of Pacing and Electrophysiology, ]. HRV can be extracted in both time and frequency domains and is frequently used in psychophysiological studies and cardiac function assessment [García‐González & Pallàs‐Areny, ; Kim, Bang, & Kim, ; McCraty, Atkinson, Tiller, Rein, & Watkins, ]. Time‐domain features that were selected included the standard deviation of the RR‐interval sequence (SD) and the root mean square successive difference (rMSSD).…”

Section: Methodsmentioning

confidence: 99%

Specificity of autonomic arousal to anxiety in children with autism spectrum disorder

et al. 2015

Anxiety is one of the most concerning comorbidities in autism spectrum disorder (ASD) due to its high prevalence, negative impact on physical and psychological well-being, and interaction with core deficits of ASD. Current assessment and treatment of anxiety, which rely on the observation of behavior and self-reports, are often ineffective as ASD is associated with deficits in communication and diminished introspective ability. In this light, autonomic nervous system (ANS) activity has been suggested as a marker of physiological arousal associated with anxiety. However, physiological arousal measured by ANS indices also occurs with other cognitive and emotional processes, and it is unclear whether anxiety-related arousal can be differentiated from that related to other cognitive processes. To address this gap, we investigated the use of linear and nonlinear classification techniques for differentiating anxiety-related arousal from arousal due to three cognitive processes (attention, inhibitory control, and social cognition) and physical activity based on electrocardiography signal features. Our results indicate that over 80% classification accuracy can be achieved, suggesting that ANS response can be used as a specific marker of anxiety-related arousal in a subgroup of children with ASD who demonstrate an increase in heart rate in response to anxiogenic stimuli.

IEEE J. Electromagn. RF Microw. Med. Biol.

“…Because of this advantage, numerous studies have been published in this field, with most studies based on contact sensors, such as electrocardiography (ECG) and photoplethysmography [4]- [8]. These studies focused on various aspects of HRV: Stanley et al proposed a statistical model [4], Mateo and Laguna proposed a signal processing technique [5], Garcia-Gonzalez and Pallas-Areny proposed a new HRV index [6], and Leor-Librach et al [7] proposed a mathematical model of the LF HRV. Sarkar and Koehler [8] reported on HRV as a marker of heart failure.…”

Section: Introductionmentioning

confidence: 99%

Noncontact Measurement of Autonomic Nervous System Activities Based on Heart Rate Variability Using Ultra-Wideband Array Radar

Sakamoto

Yamashita

2020

The noncontact measurement of vital signs using ultra-wideband radar has been attracting increasing attention because it can unobtrusively provide information about the physical and mental condition of people. In particular, the continuous measurement of a person's time-varying instantaneous heart rate can estimate the activity level of the autonomic nervous system without the person wearing any sensors. Continuous heart rate measurement using radar is, however, a difficult task because accuracy is compromised by numerous factors, such as the posture and motion of the target person. In this study, we introduce techniques for increasing the accuracy and reliability of the noncontact measurement of heart rate variability. We demonstrate the performance of the proposed techniques by applying them to radar measurement data from a sleeping person, and we also compare its accuracy with electrocardiogram data.