Predicting Ventricular Fibrillation Through Deep Learning

Tseng, Li-Ming; Tseng, Vincent S.

doi:10.1109/access.2020.3042782

Cited by 24 publications

(9 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Obtaining information about the frequency content of a signal is a usual task of the FFT (Chorro et al, 2006 ; Nash et al, 2006 ; Masse et al, 2007 ; Umapathy et al, 2010 ; Caldwell et al, 2012 ) or of the Short-Time Fourier Transform STFT (Tseng and Tseng, 2020 ; Coult et al, 2021 ). However, both require stationary signals (Clayton and Murray, 1993 ; Mansier et al, 1996 ; Seely and Macklem, 2004 ) and are therefore only suited for providing information about a signal as a whole.…”

Section: Methodsmentioning

confidence: 99%

Patterned Illumination Techniques in Optogenetics: An Insight Into Decelerating Murine Hearts

2022

View full text Add to dashboard Cite

Much has been reported about optogenetic based cardiac arrhythmia treatment and the corresponding characterization of photostimulation parameters, but still, our capacity to interact with the underlying spatiotemporal excitation patterns relies mainly on electrical and/or pharmacological approaches. However, these well-established treatments have always been an object of somehow heated discussions. Though being acutely life-saving, they often come with potential side-effects leading to a decreased functionality of the complex cardiac system. Recent optogenetic studies showed the feasibility of the usage of photostimulation as a defibrillation method with comparatively high success rates. Although, these studies mainly concentrated on the description as well as on the comparison of single photodefibrillation approaches, such as locally focused light application and global illumination, less effort was spent on the description of excitation patterns during actual photostimulation. In this study, the authors implemented a multi-site photodefibrillation technique in combination with Multi-Lead electrocardiograms (ECGs). The technical connection of real-time heart rhythm measurements and the arrhythmia counteracting light control provides a further step toward automated arrhythmia classification, which can lead to adaptive photodefibrillation methods. In order to show the power effectiveness of the new approach, transgenic murine hearts expressing channelrhodopsin-2 ex vivo were investigated using circumferential micro-LED and ECG arrays. Thus, combining the best of two methods by giving the possibility to illuminate either locally or globally with differing pulse parameters. The optical technique presented here addresses a number of challenges of technical cardiac optogenetics and is discussed in the context of arrhythmic development during photostimulation.

show abstract

Section: Methodsmentioning

confidence: 99%

Patterned Illumination Techniques in Optogenetics: An Insight Into Decelerating Murine Hearts

2022

View full text Add to dashboard Cite

show abstract

“…Ultimately, a total of 46 studies were included in this review. 15 , 16 , 17 , 18 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 Out of these 46 studies, 36 used one or more ad-hoc dataset(s) and were pooled in separate meta-analysis. 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 39 , 40 , 41 , 42 , 43 , 44 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 54 , 55 , 57 , …”

Section: Resultsmentioning

confidence: 99%

Machine learning of electrophysiological signals for the prediction of ventricular arrhythmias: systematic review and examination of heterogeneity between studies

Kolk¹,

Deb²,

Ruipérez-Campillo³

et al. 2023

eBioMedicine

View full text Add to dashboard Cite

“…In general, visually diferentiating the ECG signal of two individuals is very challenging due to the subtle changes in amplitude and duration. Hence, this L.-M. Tseng and V. S. Tseng (2020) [87] STFT and continuous wavelet transform (CWT) Two-dimensional frequency domain feature Te current method uses the 2D-STFT/CWT and CNN to detect ventricular fbrillation (VF). An accuracy of 97% was achieved using the proposed method.…”

Section: Biometric Identifcationmentioning

confidence: 99%

A Review on the Applications of Time‐Frequency Methods in ECG Analysis

Pradhan

Neelappu

Jayaraman

et al. 2023

Journal of Healthcare Engineering

View full text Add to dashboard Cite

The joint time-frequency analysis method represents a signal in both time and frequency. Thus, it provides more information compared to other one-dimensional methods. Several researchers recently used time-frequency methods such as the wavelet transform, short-time Fourier transform, empirical mode decomposition and reported impressive results in various electrophysiological studies. The current review provides comprehensive knowledge about different time-frequency methods and their applications in various ECG-based analyses. Typical applications include ECG signal denoising, arrhythmia detection, sleep apnea detection, biometric identification, emotion detection, and driver drowsiness detection. The paper also discusses the limitations of these methods. The review will form a reference for future researchers willing to conduct research in the same field.

show abstract

Predicting Ventricular Fibrillation Through Deep Learning

Cited by 24 publications

References 31 publications

Patterned Illumination Techniques in Optogenetics: An Insight Into Decelerating Murine Hearts

Patterned Illumination Techniques in Optogenetics: An Insight Into Decelerating Murine Hearts

Machine learning of electrophysiological signals for the prediction of ventricular arrhythmias: systematic review and examination of heterogeneity between studies

A Review on the Applications of Time‐Frequency Methods in ECG Analysis

Contact Info

Product

Resources

About