Contactless Measurement of Human Systolic Time Intervals Based on Doppler Cardiograms in Clinical Environment

Dong, Shuqin; Wen, Li; Zhang, Zhi; Gu, Changzhan; Mao, Junfa

doi:10.1109/lmwc.2022.3157596

Cited by 4 publications

(4 citation statements)

References 8 publications

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“…Naive segmentation has the potential to damage the structure of an image and bring attention to noise that should be disregarded when one is seeking to extract an organ from a medical image. We implemented a progressive sampling strategy by taking cues from the ViT that was presented in Shahshahani et al ( 2020 ) and Dong et al ( 2022 ). The goal of this strategy was to avoid structural damage during the rigorous segmentation of an image by ViT and instead to focus on the regions of interest in the same manner as the human visual system, thereby minimizing the impact of irrelevant information on the segmented regions.…”

Section: Review Of Existing Multiorgan Disease Detection Techniquesmentioning

confidence: 99%

RETRACTED ARTICLE: DLMBHCO: design of an augmented bioinspired deep learning-based multidomain body parameter analysis via heterogeneous correlative body organ analysis

Ajani¹,

Mulla²,

Limkar³

et al. 2023

Soft Comput

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Progressive organ-level disorders in the human body are often correlated with diseases in other body parts. For instance, liver diseases can be linked with heart issues, while cancers can be linked with brain diseases (or psychological conditions). Defining such correlations is a complex task, and existing deep learning models that perform this task either showcase lower accuracy or are non-comprehensive when applied to real-time scenarios. To overcome these issues, this text proposes design of an augmented bioinspired deep learning-based multidomain body parameter analysis via heterogeneous correlative body organ analysis. The proposed model initially collects temporal and spatial data scans for different body parts and uses a multidomain feature extraction engine to convert these scans into vector sets. These vectors are processed by a Bacterial Foraging Optimizer (BFO), which assists in identification of highly variant feature sets, which are individually classified into different disease categories. A fusion of Inception Net, XCeption Net, and GoogLeNet Models is used to perform these classifications. The classified categories are linked with other disease types via temporal analysis of blood reports. The temporal analysis engine uses Modified Analytical Hierarchical Processing (MAHP) Model for calculating inter-organ disease dependency probabilities. Based on these probabilities, the model is able to generate a patient-level correlation map, which can be used by clinical experts to suggest remedial treatments, due to which the model was able to identify correlations between brain disorders and kidneys, heart diseases and lungs, heart diseases and liver, brain diseases and different types of cancers with high efficiency when evaluated under clinical scenarios. When validated on MITBIH, DEAP, CT Kidney, RIDER, and PLCO data samples, it was observed that the proposed model was capable of improving accuracy of correlation by 8.5%, while improving precision and recall by 3.2% when compared with existing correlation models under similar clinical scenarios.

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Section: Review Of Existing Multiorgan Disease Detection Techniquesmentioning

confidence: 99%

RETRACTED ARTICLE: DLMBHCO: design of an augmented bioinspired deep learning-based multidomain body parameter analysis via heterogeneous correlative body organ analysis

Ajani¹,

Mulla²,

Limkar³

et al. 2023

Soft Comput

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show abstract

“…Recent efforts in this domain involve the integration of various medical sensors and measuring instruments, simultaneously with radar devices, to detect cardiac activity in patients within clinical environments [88]. Through this approach, researchers have validated various cardiac activity intervals, such as STI, computed from radar cardiograms.…”

Section: Cardiac Timing Detectionmentioning

confidence: 99%

“…16. (a) Cardiac cycle phases (b) Modified Wiggers diagram with grey zone indicating 1 (PEP) and blue zone indicating 2 (LEVT) [88].…”

mentioning

confidence: 99%

A Review on Recent Advancements of Biomedical Radar for Clinical Applications

Dong,

Wen,

et al. 2024

IEEE Open J. Eng. Med. Biol.

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The field of biomedical radar has witnessed significant advancements in recent years, paving the way for innovative and transformative applications in clinical settings. Most medical instruments invented to measure human activities rely on contact electrodes, causing discomfort. Thanks to its noninvasive nature, biomedical radar is particularly valuable for clinical applications. A significant portion of the review discusses improvements in radar hardware, with a focus on miniaturization, increased resolution, and enhanced sensitivity. Then, this paper also delves into the signal processing and machine learning techniques tailored for radar data. This review will explore the recent breakthroughs and applications of biomedical radar technology, shedding light on its transformative potential in shaping the future of clinical diagnostics, patient and elderly care, and healthcare innovation.

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“…One application was the first smartphone with a microwave radar inside [211]. The effectiveness of a radar system for Doppler Cardiogram (DCG) has been clinically validated to provide diagnostic information for heart diseases that contained abnormal activities with non-invasive, continuous blood pressure sensing, and the detection of heart failure, atrial fibrillation (AF) and premature heartbeat (PHB) [212], [213], [214].…”

Section: ) Research By Mtt-28 Membersmentioning

confidence: 99%

Applications of Microwaves in Medicine

Chiao

Lin

et al. 2023

IEEE J. Microw.

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Advances and updates in medical applications utilizing microwave techniques and technologies are reviewed in this paper. The article aims to provide an overview of enablers for microwave medical applications and their recent progress. The emphasis focuses on the applications of microwaves, in the following order, for 1) signal and data communication for implants and wearables through the human body, 2) electromagnetic energy transfer through tissues, 3) noninvasive, remote or in situ physical and biochemical sensing, and 4) therapeutic purposes by changing tissue properties with controlled thermal effects. For signal and data communication and wireless power transfer, implant and wearable applications are discussed in the categories of pacemakers, endoscopic capsules, brain interfaces, intraocular, cardiac and intracranial pressure sensors, neurostimulators, endoluminal implants, artificial retina, smart lenses, and cochlear implants. For noninvasive sensing, remote vital sign radar, biological cell probing, magnetic resonance and microwave imaging, biochemical, blood glucose, hydration and biomarker sensing applications are introduced. For therapeutic uses, the developments of microwave ablation, balloon angioplasty, and hyperthermia applications are reviewed. The scopes of this article mainly concentrate on the research and development efforts in the past 20 years. Recent review articles on specific topics are cited with accomplishment highlights and trends deliberated. At the end of this article, a brief history of the IEEE Microwave Theory and Techniques Society (MTT-S) Biological Effects and Medical Applications committee and the contributions by its members to the promotion and advancement of microwave technologies in medical fields are chronicled.

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Contactless Measurement of Human Systolic Time Intervals Based on Doppler Cardiograms in Clinical Environment

Cited by 4 publications

References 8 publications

RETRACTED ARTICLE: DLMBHCO: design of an augmented bioinspired deep learning-based multidomain body parameter analysis via heterogeneous correlative body organ analysis

RETRACTED ARTICLE: DLMBHCO: design of an augmented bioinspired deep learning-based multidomain body parameter analysis via heterogeneous correlative body organ analysis

A Review on Recent Advancements of Biomedical Radar for Clinical Applications

Applications of Microwaves in Medicine

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