Robust detection of heart beats in multimodal data

Silva, Ikaro; Moody, Benjamin; Behar, Joachim A.; Johnson, Alistair E. W.; Oster, Julien; Clifford, Gari D.; Moody, G.B.

doi:10.1088/0967-3334/36/8/1629

Cited by 44 publications

(63 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a whole, our algorithm outperforms the others that relied on implementing a form of intelligent switching. Only Pangerc, who relied on their reimplemented beat detection algorithm (repdet) outperformed our own (Silva et al 2015).…”

Section: Discussionmentioning

confidence: 93%

“…This is likely due to their use of custom ECG and BP pulse detectors. Their QRS detector (repdet) provided a much improved performance over GQRS on set-p2 in particular, likely due to their inclusion of a step in the detector to identify double annotations (paced beats) (Silva et al 2015) (Pangerc & Jager 2014). This step may account for Pangerc's improved performance over the algorithms that used GQRS.…”

Section: Results -Overallmentioning

confidence: 99%

See 1 more Smart Citation

Probabilistic model-based approach for heart beat detection

Chen

Erol²,

Shen³

et al. 2016

Physiol. Meas.

View full text Add to dashboard Cite

Nowadays, hospitals are ubiquitous and integral to modern society. Patients flow in and out of a veritable whirlwind of paperwork, consultations, and potential inpatient admissions, through an abstracted system that is not without flaws. One of the biggest flaws in the medical system is perhaps an unexpected one: the patient alarm system. One longitudinal study reported an 88.8% rate of false alarms, with other studies reporting numbers of similar magnitudes. These false alarm rates lead to a number of deleterious effects that manifest in a significantly lower standard of care across clinics.This paper discusses a model-based probabilistic inference approach to identifying variables at a detection level. We design a generative model that complies with an overview of human physiology and perform approximate Bayesian inference. One primary goal of this paper is to justify a Bayesian modeling approach to increasing robustness in a physiological domain.We use three data sets provided by Physionet, a research resource for complex physiological signals, in the form of the Physionet 2014 Challenge set-p1 and set-p2, as well as the MGH/MF Waveform Database. On the extended data set our algorithm is on par with the other top six submissions to the Physionet 2014 challenge.

show abstract

Section: Discussionmentioning

confidence: 93%

Section: Results -Overallmentioning

confidence: 99%

Probabilistic model-based approach for heart beat detection

Chen

Erol²,

Shen³

et al. 2016

Physiol. Meas.

View full text Add to dashboard Cite

show abstract

“…The analysis of the proposed approach is performed on a dataset that is available as a part of the PhysioNet Challenge along with a detailed description [16]. The dataset consists of 200 samples and contains time-aligned physiological signals including ECG, ABP, respiration, and fingertip PPG, etc.…”

Section: Data Collection and Performance Evaluationmentioning

confidence: 99%

Probabilistic data fusion model for heart beat detection from multimodal physiological data

Zia¹,

Arif²

2017

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

Automatic detection of heart beats constitutes the basis for electrocardiogram (ECG) analysis and mainly relies on detecting QRS complexes. Detection is typically performed by analyzing the ECG signal. However, when signal quality is low, it often leads to the triggering of false alarms. A contemporary approach to reduce false alarm rate is to use multimodal data such as arterial blood pressure (ABP) or photoplethysmogram (PPG) signals. To leverage the correlated temporal nature of these signals, a probabilistic data fusion model for heart beat detection is proposed.A hidden Markov model is used to decode waveforms into segments. A Bayesian network is employed for capturing intersegmental coupling between waveforms and detecting heart beats. The performance of the proposed system was evaluated on a dataset provided by PhysioNet Challenge 2014: Robust Detection of Heart Beats in Multimodal Data. The proposed method is comparatively analyzed with a baseline hidden Markov model method for ECG and an improvement of 9% in sensitivity and 26% in positive predictivity is observed. The efficiency of the proposed model is also compared with related data fusion methods and a comparable performance is found. The robustness of the method is analyzed by inducting Gaussian noise into the dataset. A performance gain of 31% both in sensitivity and positive predictivity is obtained in the worst case where both ECG and ABP are noisy with -6 dB signal-to-noise ratio.

show abstract

“…In this study, 200 recordings of multimodal dataset of PhysioNet/CinC Challenge 2016 were used [9]. Each record contained four to eight signals, the first of which was always an ECG signal.…”

Section: Datamentioning

confidence: 99%