Applying Quality Index Criterion for Flexible Multi:Detection of Heartbeat Using Features of Multimodal Data

Mollakazemi, Mohammad Javad; Asadi, Fatemeh; Ghiasi, Shadi; Sadati, Seyed Hossein

doi:10.22489/cinc.2016.307-451

Cited by 5 publications

(7 citation statements)

References 11 publications

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“…The use of ECG and BP signals has also been extensively studied due to their direct relationship with cardiac activity [58,59,60,61,62,63,64,65], from which other approaches that integrate up to 3 additional signals have also been presented: SV [66], EEG [67,68], and EOG [69,70] signals; EOG and EMG signals [71]; SV and EOG signals [72]; EEG, EOG, and EMG signals [73]; and SV, EEG, and PPG signals [74].…”

Section: Heartbeat Detection From Multiple Physiological Signalsmentioning

confidence: 99%

“…Two quality indexes for the ECG and BP signals based on the noise estimation from the coefficients of the second level of detail output by the DWT have been proposed in Reference [64]. To do so, each segment in which the distribution of noise is lower will be assigned a higher quality index.…”

Section: Heartbeat Detection From Multiple Physiological Signalsmentioning

confidence: 99%

“…The simplest approach is to use a fixed delay value for each signal taken from the literature (for example, 200 ms for the BP signal) [52,64,78,79]. Some authors also use constant delay values for all the records (and each signal), although they estimate the values from the data [45,75,81].…”

Section: Heartbeat Detection From Multiple Physiological Signalsmentioning

confidence: 99%

“…Usually, spurious annotations are directly removed whereas missing annotations are predicted using interpolation or the mean RR interval. This approach or similar ones are used in References [42,46,57,58,59,61,64,72,73,78,79]. The process may be repeated several times until convergence [81].…”

Section: Heartbeat Detection From Multiple Physiological Signalsmentioning

confidence: 99%

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Multiple Physiological Signals Fusion Techniques for Improving Heartbeat Detection: A Review

Tejedor

García

Márquez

et al. 2019

Sensors

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This paper presents a review of the techniques found in the literature that aim to achieve a robust heartbeat detection from fusing multi-modal physiological signals (e.g., electrocardiogram (ECG), blood pressure (BP), artificial blood pressure (ABP), stroke volume (SV), photoplethysmogram (PPG), electroencephalogram (EEG), electromyogram (EMG), and electrooculogram (EOG), among others). Techniques typically employ ECG, BP, and ABP, of which usage has been shown to obtain the best performance under challenging conditions. SV, PPG, EMG, EEG, and EOG signals can help increase performance when included within the fusion. Filtering, signal normalization, and resampling are common preprocessing steps. Delay correction between the heartbeats obtained over some of the physiological signals must also be considered, and signal-quality assessment to retain the best signal/s must be considered as well. Fusion is usually accomplished by exploiting regularities in the RR intervals; by selecting the most promising signal for the detection at every moment; by a voting process; or by performing simultaneous detection and fusion using Bayesian techniques, hidden Markov models, or neural networks. Based on the results of the review, guidelines to facilitate future comparison of the performance of the different proposals are given and promising future lines of research are pointed out.

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Section: Heartbeat Detection From Multiple Physiological Signalsmentioning

confidence: 99%

Section: Heartbeat Detection From Multiple Physiological Signalsmentioning

confidence: 99%

Section: Heartbeat Detection From Multiple Physiological Signalsmentioning

confidence: 99%

Section: Heartbeat Detection From Multiple Physiological Signalsmentioning

confidence: 99%

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Multiple Physiological Signals Fusion Techniques for Improving Heartbeat Detection: A Review

Tejedor

García

Márquez

et al. 2019

Sensors

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“…Baseline wandering removal and Butterworth low and high pass filter are first applied to all signals. Then, the fiducal points of ECG signals (P, QRS complex and T) are detected using the classical pan Tompkins algorithm [6]. Each heart beat is extracted using the detected R-peaks without the inclusion of the first and last beat.…”

Section: Preprocessingmentioning

confidence: 99%

Atrial Fibrillation Detection Using Feature Based Algorithm and Deep Conventional Neural Network

Ghiasi

Abdollahpur

Madani

et al. 2017

Computing in Cardiology Conference (CinC)

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Aims: Electrocardiographic waveforms (ECG) are recognized as the most reliable method to detect abnormal heart rhythms such as atrial fibrillation. This task is challenging when the signals are distorted by noise. This paper presents an automatic classification algorithm to classify short lead ECGs in terms of abnormality of heart rhythm (AF or alternative rhythms) and quality (noisy recordings). Methods: To meet this end, at first baseline wander removal and Butterworth filter for each signal are applied as a preprocessing stage. Due to the existence of noise in recordings, high quality beats are selected for any further analysis using cycle quality assessment. Then, three sets of features defined as correlation coefficient, fractal dimension and variance of R peaks are extracted to predict noisy recordings. Two separate approaches are employed to classify other three classes. The first approach is the feature based methodology and the second one is the applying deep neural networks. In the first approach, features from different domains are extracted .The method for AF detection utilizes and characterizes variability in RR-intervals which are extracted by applying classic Pan-Tompkins algorithm. To improve the accuracy of the AFdetection, atrial activity is analyzed by understanding whether the P-wave is present in signal. This is done by investigating the morphology of P-waves. Heart rate abnormality and the existence of premature beats in a signal are regarded as two characteristics to distinguish non-AF rhythms. The whole sets of features are fed into a neural network classifier. Another approach uses the segments with 600 samples as the input of a 1 dimensional convolutional neural network. The output obtained from both approaches are combined using a decision table and finally the recordings are classified into three classes. Results: The proposed method is evaluated using scoring function from 2017 PhysioNet/CinC Challenge and achieved an overall score of 80% and 71% on the training dataset and hidden test dataset, respectively.

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Hidden Markov model-based heartbeat detector using electrocardiogram and arterial pressure signals

Altuve

Monroy

2021

Biomed. Eng. Lett.

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Applying Quality Index Criterion for Flexible Multi:Detection of Heartbeat Using Features of Multimodal Data

Abstract: Introduction:In

Cited by 5 publications

References 11 publications

Multiple Physiological Signals Fusion Techniques for Improving Heartbeat Detection: A Review

Multiple Physiological Signals Fusion Techniques for Improving Heartbeat Detection: A Review

Atrial Fibrillation Detection Using Feature Based Algorithm and Deep Conventional Neural Network

Hidden Markov model-based heartbeat detector using electrocardiogram and arterial pressure signals

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