ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2021
DOI: 10.1109/icassp39728.2021.9414709
|View full text |Cite
|
Sign up to set email alerts
|

ECG Heart-Beat Classification Using Multimodal Image Fusion

Abstract: In this paper, we present a novel Image Fusion Model (IFM) for ECG heart-beat classification to overcome the weaknesses of existing machine learning techniques that rely either on manual feature extraction or direct utilization of 1D raw ECG signal. At the input of IFM, we first convert the heart-beats of ECG into three different images using Gramian Angular Field (GAF), Recurrence Plot (RP) and Markov Transition Field (MTF) and then fuse these images to create a single imaging modality. We use AlexNet for fea… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
13
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
3
3
1

Relationship

2
5

Authors

Journals

citations
Cited by 18 publications
(13 citation statements)
references
References 25 publications
0
13
0
Order By: Relevance
“…Data from each modality are applied individually or combined with others. In 374 articles, im- age modality was present in 247, which were image data individually used in 129 articles [49], [51], [52], [54], [56], [57], [59]- [61], [63], [64] [103], [105], [106], [108], [110], [112]- [115], [117], [120]- [123], [128], [130], [134], [135], [137]- [149], [151], [152], [154], [155], [157], [158], [160], [162], [172]- [174], [176], [178], [191], [194]- [196], [198], [202], [203], [209], [210], [212], [214],…”
Section: B Taskmentioning
confidence: 99%
See 2 more Smart Citations
“…Data from each modality are applied individually or combined with others. In 374 articles, im- age modality was present in 247, which were image data individually used in 129 articles [49], [51], [52], [54], [56], [57], [59]- [61], [63], [64] [103], [105], [106], [108], [110], [112]- [115], [117], [120]- [123], [128], [130], [134], [135], [137]- [149], [151], [152], [154], [155], [157], [158], [160], [162], [172]- [174], [176], [178], [191], [194]- [196], [198], [202], [203], [209], [210], [212], [214],…”
Section: B Taskmentioning
confidence: 99%
“…A total of 212 articles related to fusion learning were encountered. Of 155 articles, 99 were model-agnostic, where 62 pertained to early [55], [56], [58], [59], [62], [63], [75], [76], [79], [98], [102]- [105], [111], [115], [119], [120], [133], [141], [142], [166], [173], [207], [213], [240], [242], [250], [252], [254], [258], [259], [270], [271], [280], [282], [299], [303], [305]- [307], [313], [320], [324], [326], [330], [334], [337], [347], [349], [357], [359], [364], [367], [381],…”
Section: F Fusionmentioning
confidence: 99%
See 1 more Smart Citation
“…Physiological signals such as EEG, ECG and GSR are 1D signal in raw form. In [ 87 , 88 ], raw ECG signal was converted into 2D form, i.e., into three statistical images namely Gramian Angular Field (GAF) images, Recurrence Plot (RP) images and Markov Transition Field (MTF) images as shown in Figure 8 . Experimental results show the superiority of 1D to 2D pre-processing.…”
Section: Data Pre-processingmentioning
confidence: 99%
“…Physiological signals such as EEG, ECG and GSR are 1D signal in raw form. In [77] and [78], raw ECG signal was converted into 2D form i.e into three statistical images namely Gramian Angular Field (GAF) images, Recurrence Plot (RP) images and Markov Transition Field (MTF) images as shown in Fig. 7.…”
Section: D To 2d Conversionmentioning
confidence: 99%