ECG Heart-Beat Classification Using Multimodal Image Fusion

Ahmad, Zeeshan; Tabassum, Anika; Guan, Ling; Khan, Naimul

doi:10.1109/icassp39728.2021.9414709

Cited by 18 publications

(13 citation statements)

References 25 publications

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“…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%

“…Model-agnostic fusion has three kinds: early, late, and hybrid fusion, where early and late are used most often. Early fusion unites features directly when they are extracted as in [56], [152], [269], [312]. On the other hand, late fusion is performed after making decisions such as voting [55], [114] and weighting [278] schemes.…”

Section: B Inspection Of Rq2mentioning

confidence: 99%

See 2 more Smart Citations

A Systematic Literature Review on Multimodal Machine Learning: Applications, Challenges, Gaps and Future Directions

2023

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Multimodal machine learning (MML) is a tempting multidisciplinary research area where heterogeneous data from multiple modalities and machine learning (ML) are combined to solve critical problems. Usually, research works use data from a single modality, such as images, audio, text, and signals. However, real-world issues have become critical now, and handling them using multiple modalities of data instead of a single modality can significantly impact finding solutions. ML algorithms play an essential role by tuning parameters in developing MML models. This paper reviews recent advancements in the challenges of MML, namely: representation, translation, alignment, fusion and co-learning, and presents the gaps and challenges. A systematic literature review (SLR) applied to define the progress and trends on those challenges in the MML domain. In total, 1032 articles were examined in this review to extract features like source, domain, application, modality, etc. This research article will help researchers understand the constant state of MML and navigate the selection of future research directions.

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Section: B Taskmentioning

confidence: 99%

Section: F Fusionmentioning

confidence: 99%

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A Systematic Literature Review on Multimodal Machine Learning: Applications, Challenges, Gaps and Future Directions

2023

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“…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%

A Survey on Physiological Signal-Based Emotion Recognition

Ahmad

Khan

2022

Bioengineering

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Physiological signals are the most reliable form of signals for emotion recognition, as they cannot be controlled deliberately by the subject. Existing review papers on emotion recognition based on physiological signals surveyed only the regular steps involved in the workflow of emotion recognition such as pre-processing, feature extraction, and classification. While these are important steps, such steps are required for any signal processing application. Emotion recognition poses its own set of challenges that are very important to address for a robust system. Thus, to bridge the gap in the existing literature, in this paper, we review the effect of inter-subject data variance on emotion recognition, important data annotation techniques for emotion recognition and their comparison, data pre-processing techniques for each physiological signal, data splitting techniques for improving the generalization of emotion recognition models and different multimodal fusion techniques and their comparison. Finally, we discuss key challenges and future directions in this field.

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“…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%

A Survey on Physiological Signal Based Emotion Recognition

Ahmad¹,

Khan²

2022

Preprint

Self Cite

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Physiological Signals are the most reliable form of signals for emotion recognition, as they cannot be controlled deliberately by the subject. Existing review papers on emotion recognition based on physiological signals surveyed only the regular steps involved in the workflow of emotion recognition such as preprocessing, feature extraction, and classification. While these are important steps, such steps are required for any signal processing application. Emotion recognition poses its own set of challenges that are very important to address for a robust system. Thus, to bridge the gap in the existing literature, in this paper, we review the effect of inter-subject data variance on emotion recognition, important data annotation techniques for emotion recognition and their comparison, data preprocessing techniques for each physiological signal, data splitting techniques for improving the generalization of emotion recognition models and different multimodal fusion techniques and their comparison. Finally we discuss key challenges and future directions in this field.

show abstract

ECG Heart-Beat Classification Using Multimodal Image Fusion

Cited by 18 publications

References 25 publications

A Systematic Literature Review on Multimodal Machine Learning: Applications, Challenges, Gaps and Future Directions

A Systematic Literature Review on Multimodal Machine Learning: Applications, Challenges, Gaps and Future Directions

A Survey on Physiological Signal-Based Emotion Recognition

A Survey on Physiological Signal Based Emotion Recognition

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