A Robust Method for Detecting the QRS Complex of the ECG Signal

Федотов, А. А.

doi:10.1007/s10527-016-9583-5

Cited by 6 publications

(5 citation statements)

References 8 publications

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“…The latency and amplitude of the cue-and target-locked P3 were determined by extracting the maximal positive peak in EEG signal (at electrode Pz) between 250 and 400 ms after the onset of cue-and target stimuli, while the most negative peak (at electrode FCz) in the time window 100 to 250 ms was identified to extract the latency and amplitude of the N2 in response to target stimuli. HR signal was recorded from two electrodes placed on participants' wrists, and band-pass filtered (8-20 Hz), to reduce the baseline fluctuation of the cardiac signal and minimize the impact of artifacts or high frequency noise (Fedotov, 2016). Automatic detection of cardiac beats was carried out in Brainstorm (Tadel et al, 2011), followed by visual correction of potentially erroneous or missing peaks, before calculating the Inter-Beat Intervals (IBI), calculated as the time (in ms) between successive heartbeats.…”

Section: Methodsmentioning

confidence: 99%

“…HR signal was recorded from two electrodes placed on participants’ wrists, and band-pass filtered (8–20 Hz), to reduce the baseline fluctuation of the cardiac signal and minimize the impact of artifacts or high frequency noise ( Fedotov, 2016 ). Automatic detection of cardiac beats was carried out in Brainstorm ( Tadel et al, 2011 ), followed by visual correction of potentially erroneous or missing peaks, before calculating the Inter-Beat Intervals (IBI), calculated as the time (in ms) between successive heartbeats.…”

Section: Methodsmentioning

confidence: 99%

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Indices of Heart Rate Variability and Performance During a Response-Conflict Task Are Differently Associated With ADHD and Autism

et al. 2021

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We investigated autonomic arousal, attention and response conflict, in ADHD and autism. Heart rate variability (HRV), and behavioral/electrophysiological indices of performance, were recorded during a task with low and high levels of response conflict in 78 children/adolescents (7–15 years old) with ADHD, autism, comorbid ADHD+autism, or neurotypical. ANOVA models were used to investigate effects of ADHD and autism, while a mediation model was tested to clarify the relationship between ADHD and slower performance. Slower and less accurate performance characterized ADHD and autism; however, atypical electrophysiological indices differently characterized these conditions. The relationship between ADHD and slower task performance was mediated by reduced HRV in response to the cue stimulus. Autonomic hypo-arousal and difficulties in mobilizing energetic resources in response to sensory information (associated with ADHD), and atypical electrophysiological indices of information processing (associated with autism), might negatively affect cognitive performance in those with ADHD+autism.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Indices of Heart Rate Variability and Performance During a Response-Conflict Task Are Differently Associated With ADHD and Autism

et al. 2021

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“…Raw HR signal was processed to obtain non-linear measures of HRV, the Cardiac Sympathetic Index (CSI) and the Cardiac Vagal Index (CVI). To derive these measures, heart rate signal was band-pass filtered (8-20 Hz) to minimise the impact of artifacts and high frequency noise (Fedotov, 2016), before automatic detection of cardiac beats was carried out in Brainstorm (Tadel et al, 2011). This was followed by visual correction of potentially erroneous or missing peaks, before calculating the inter-beat intervals (IBI), i.e., the time differences between successive heartbeats.…”

Section: Procedure Data Processing and Outcome Measuresmentioning

confidence: 99%

Heart Rate Variability in Children and Adolescents with Autism, ADHD and Co-occurring Autism and ADHD, During Passive and Active Experimental Conditions

Bellato

Arora

Kochhar

et al. 2021

J Autism Dev Disord

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Despite overlaps in clinical symptomatology, autism and ADHD may be associated with opposite autonomic arousal profiles which might partly explain altered cognitive and global functioning. We investigated autonomic arousal in 106 children/adolescents with autism, ADHD, co-occurring autism/ADHD, and neurotypical controls. Heart rate variability was recorded during resting-state, a ‘passive’ auditory oddball task and an ‘active’ response conflict task. Autistic children showed hyper-arousal during the active task, while those with ADHD showed hypo-arousal during resting-state and the passive task. Irrespective of diagnosis, children characterised by hyper-arousal showed more severe autistic symptomatology, increased anxiety and reduced global functioning than those displaying hypo-arousal, suggesting the importance of considering individual autonomic arousal profiles for differential diagnosis of autism/ADHD and when developing personalised interventions.

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“…(4) Finally, the ECG signals of MIT-BIH-AR database and INCART database were tested and analyzed. [12], short-time Fourier transform (STFT) [13], and wavelet transfer [3,4] for noise removal and analysis. Wavelet transform is a new transform analysis method, which inherits and develops the idea of localization of short-time Fourier transform.…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction and Recognition of Human Physiological Signals Based on the Convolutional Neural Network

Hurr

2022

Mobile Information Systems

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Human physiological signal processing is one of the research fields widely used in recent years. Research on human physiological signals plays a vital role in predicting human health and detecting and classifying certain disease outbreaks. The network of human physiological signals is difficult to determine because it contains a lot of information about human activities. To this end, a variety of feature extraction, feature selection, and classification algorithms have been implemented in the anomaly prediction process. However, it has the main disadvantage of classification results, using a large number of features and increasing complexity. In order to solve these problems, this paper proposes a convolutional neural network-based extraction technique for human physiological signal features and uses an MPL classifier to detect whether the ECG signal is normal or not, taking the ECG signal as an example. In this paper, the signal preprocessing method based on wavelet transform and morphological filtering is adopted, and the high-frequency signal is removed by wavelet transform, and the low-frequency signal is removed by morphological filtering. A wide range of tests on ECG signals obtained from the MIT-BIH-AR databank and INCART database showed that the method has good detection performance with sensitivity Sen = 99.54%, positive prediction rate PPR = 99.65%, detecting mistake ratio DER = 0.35% and precision Acc = 99.55%, which is an improved performance compared to other techniques, proving the superiority of the present technique.

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A Robust Method for Detecting the QRS Complex of the ECG Signal

Cited by 6 publications

References 8 publications

Indices of Heart Rate Variability and Performance During a Response-Conflict Task Are Differently Associated With ADHD and Autism

Indices of Heart Rate Variability and Performance During a Response-Conflict Task Are Differently Associated With ADHD and Autism

Heart Rate Variability in Children and Adolescents with Autism, ADHD and Co-occurring Autism and ADHD, During Passive and Active Experimental Conditions

Feature Extraction and Recognition of Human Physiological Signals Based on the Convolutional Neural Network

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