Accurate detection of congestive heart failure using electrocardiomatrix technique

Sharma, Kavya; Rao, B. Mohan; Marwaha, Puneeta; Kumar, Aman

doi:10.1007/s11042-022-12773-8

Cited by 8 publications

(5 citation statements)

References 33 publications

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“…This might be because it could be easier to identify users that all have CVD due to the dissimilarity of EKG recordings. As it is shown in other studies, it is possible to identify different cardiac conditions through the study and classification of EKGs or EKMs [165,166,125,128,140,116]. That implies that each EKM changes depending on the user if they have any CVD.…”

Section: Study Over Users With Cvdmentioning

confidence: 92%

“…As it can be seen, there are many works that perform not user, but CVD identification through the EKG signal as the ones in [116,119,120,121,122,123,124]. In fact, in the particular case of the EKM, as it is commented in Section 1.2.5, all the previous work performed with the EKM is done to study the classification and diagnostics of CVD [125,126,127,128]. With all these works it is manifested that CVD affects the EKG signal to make it distinguishable enough to identify users.…”

Section: Intra-subject and Inter-subject Variability Of Ekgmentioning

confidence: 99%

“…To the best of our knowledge, the last work published in the study of the EKM (despite ELEKTRA [13]) is the one presented in [128]. In this case, the EKM is used to manually study patients with Congestive Heart Failure (CHF).…”

Section: Previous Work Over the Ekmmentioning

confidence: 99%

“…The EKM has already been proposed in several works for medical purposes [126,127,140,128,145]. In this thesis, the main goal is to show the feasibility of EKMs for identification (cybersecurity purposes).…”

Section: Ekmmentioning

confidence: 99%

See 3 more Smart Citations

ELEKTRA: ELEKTRokardiomatrix application to biometric identification with convolutional neural networks

Fuster-Barceló

Peris-López²,

Cámara³

2022

Neurocomputing

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Section: Study Over Users With Cvdmentioning

confidence: 92%

Section: Intra-subject and Inter-subject Variability Of Ekgmentioning

confidence: 99%

Section: Previous Work Over the Ekmmentioning

confidence: 99%

Section: Ekmmentioning

confidence: 99%

See 2 more Smart Citations

ELEKTRA: ELEKTRokardiomatrix application to biometric identification with convolutional neural networks

Fuster-Barceló

Peris-López²,

Cámara³

2022

Neurocomputing

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“…In 2018–2019, interest in ECM increased, demonstrated in three studies for visual estimation of single-lead ECM expressed as qualitative impression for diminished HRV in patients before cardiac arrest [ 28 ]; effective manual identification of AF and/or atrial flutter (AFL) in MIT-BIH Atrial Fibrillation Database [ 29 ] and in telemetry data from ischemic stroke and transient ischemic attack patients [ 30 ]. Recently, two studies reported their satisfaction with effectiveness, intuitiveness, and memory saving while using ECM images for visual detection of events in recordings from congestive heart failure patients [ 31 , 32 ]. After 2020, the power of deep learning technology for image processing has been applied for the analysis of different kinds of ECM images.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Supraventricular Arrhythmias in Holter ECG Recordings by ECHOView Color Map: A Case Series Study

Naydenov,

Jekova,

Krasteva

2023

JCDD

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Ambulatory 24–72 h Holter ECG monitoring is recommended for patients with suspected arrhythmias, which are often transitory and might remain unseen in resting standard 12-lead ECG. Holter manufacturers provide software diagnostic tools to assist clinicians in evaluating these large amounts of data. Nevertheless, the identification of short arrhythmia events and differentiation of the arrhythmia type might be a problem in limited Holter ECG leads. This observational clinical study aims to explore a novel and weakly investigated ECG modality integrated into a commercial diagnostic tool ECHOView (medilog DARWIN 2, Schiller AG, Switzerland), while used for the interpretation of long-term Holter-ECG records by a cardiologist. The ECHOView transformation maps the beat waveform amplitude to a color-coded bar. One ECHOView page integrates stacked color bars of about 1740 sequential beats aligned by R-peak in a window (R ± 750 ms). The collected 3-lead Holter ECG recordings from 86 patients had a valid duration of 21 h 20 min (19 h 30 min–22 h 45 min), median (quartile range). The ECG rhythm was reviewed with 3491 (3192–3723) standard-grid ECG pages and a substantially few number of 51 (44–59) ECHOView pages that validated the ECHOView compression ratio of 67 (59–74) times. Comments on the ECG rhythm and ECHOView characteristic patterns are provided for 14 examples representative of the most common rhythm disorders seen in our population, including supraventricular arrhythmias (supraventricular extrasystoles, paroxysmal supraventricular arrhythmia, sinus tachycardia, supraventricular tachycardia, atrial fibrillation, and flutter) and ventricular arrhythmias (ventricular extrasystoles, non-sustained ventricular tachycardia). In summary, the ECHOView color map transforms the ECG modality into a novel diagnostic image of the patient’s rhythm that is comprehensively interpreted by a cardiologist. ECHOView has the potential to facilitate the manual overview of Holter ECG recordings, to visually identify short-term arrhythmia episodes, and to refine the diagnosis, especially in high-rate arrhythmias.

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MULTI-head self-attention-based recurrent neural network with dwarf mongoose optimization algorithm-espoused QRS detector design

Malathi,

Kumar

2024

SIViP

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Accurate detection of congestive heart failure using electrocardiomatrix technique

Cited by 8 publications

References 33 publications

ELEKTRA: ELEKTRokardiomatrix application to biometric identification with convolutional neural networks

ELEKTRA: ELEKTRokardiomatrix application to biometric identification with convolutional neural networks

Recognition of Supraventricular Arrhythmias in Holter ECG Recordings by ECHOView Color Map: A Case Series Study

MULTI-head self-attention-based recurrent neural network with dwarf mongoose optimization algorithm-espoused QRS detector design

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