Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference最新文献_第10页

A Novel Machine-Learning-Based Noise Detection Method for Photoplethysmography Signals.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10782126

Soheil Khooyooz, Anice Jahanjoo, Amin Aminifar, Nima TaheriNejad

引用次数: 0

Identifying Canonical multi-scale Intrinsic Connectivity Networks in Infant resting-state fMRI and their Association with Age.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10782404

Prerana Bajracharya, Ashkan Faghiri, Zening Fu, Vince D Calhoun, Sarah Shultz, Armin Iraji

{"title":"Identifying Canonical multi-scale Intrinsic Connectivity Networks in Infant resting-state fMRI and their Association with Age.","authors":"Prerana Bajracharya, Ashkan Faghiri, Zening Fu, Vince D Calhoun, Sarah Shultz, Armin Iraji","doi":"10.1109/EMBC53108.2024.10782404","DOIUrl":"https://doi.org/10.1109/EMBC53108.2024.10782404","url":null,"abstract":"Intrinsic Connectivity Networks (ICNs) reflect functional brain organization responsible for various cognitive processes, including sensory perception, motor control, memory, and attention. In this study, we used the Multivariate-Objective Optimization Independent Component Analysis with Reference (MOO-ICAR) and the NeuroMark 2.1 (adult) template to estimate subject-specific ICNs in resting-state functional magnetic resonance imaging (rsfMRI) data of infants. The NeuroMark 2.1 template contains 105 multi-scale canonical ICNs derived from 100k+ adults across multiple datasets. The multi-scale ICNs capture functional segregation across various levels of granularity across brain, revealing functional sources and their interactions. The results showed that the 105 ICNs in infants were spatially aligned with those in the template and revealed age-related distinctive patterns in static Functional Network Connectivity (sFNC), particularly in the sub-cortical and high-level cognitive domains. This study is the first to investigate the presence and development of these multi-scale ICNs in infant rsfMRI data. Our findings confirmed the presence of identifiable canonical ICNs in infants as young as six months, showcasing a strong association between these networks and age and suggesting potential biomarkers for early identification of neurodevelopmental disability.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143559559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Identifying Prediabetes in Canadian Populations Using Machine Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10782174

Katherine Lu, Paijani Sheth, Zhi Lin Zhou, Kamyar Kazari, Aziz Guergachi, Karim Keshavjee, Mohammad Noaeen, Zahra Shakeri

{"title":"Identifying Prediabetes in Canadian Populations Using Machine Learning.","authors":"Katherine Lu, Paijani Sheth, Zhi Lin Zhou, Kamyar Kazari, Aziz Guergachi, Karim Keshavjee, Mohammad Noaeen, Zahra Shakeri","doi":"10.1109/EMBC53108.2024.10782174","DOIUrl":"https://doi.org/10.1109/EMBC53108.2024.10782174","url":null,"abstract":"Prediabetes is a critical health condition characterized by elevated blood glucose levels that fall below the threshold for Type 2 diabetes (T2D) diagnosis. Accurate identification of prediabetes is essential to forestall the progression to T2D among at-risk individuals. This study aims to pinpoint the most effective machine learning (ML) model for prediabetes prediction and to elucidate the key biological variables critical for distinguishing individuals with prediabetes. Utilizing data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN), our analysis included 6,414 participants identified as either nondiabetic or prediabetic. A rigorous selection process led to the identification of ten variables for the study, informed by literature review, data completeness, and the evaluation of collinearity. Our comparative analysis of seven ML models revealed that the Deep Neural Network (DNN), enhanced with early stop regularization, outshined others by achieving a recall rate of 60%. This model's performance underscores its potential in effectively identifying prediabetic individuals, showcasing the strategic integration of ML in healthcare. While the model reflects a significant advancement in prediabetes prediction, it also opens avenues for further research to refine prediction accuracy, possibly by integrating novel biological markers or exploring alternative modeling techniques. The results of our work represent a pivotal step forward in the early detection of prediabetes, contributing significantly to preventive healthcare measures and the broader fight against the global epidemic of Type 2 diabetes.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143559562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluation of Cough Sound Segmentation Algorithms in the Presence of Background Noise.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10782675

Roneel V Sharan, Hao Xiong

{"title":"Evaluation of Cough Sound Segmentation Algorithms in the Presence of Background Noise.","authors":"Roneel V Sharan, Hao Xiong","doi":"10.1109/EMBC53108.2024.10782675","DOIUrl":"https://doi.org/10.1109/EMBC53108.2024.10782675","url":null,"abstract":"Automated cough sound segmentation is important for the objective analysis of cough sounds. While various cough sound segmentation algorithms have been proposed over the years, it is not clear how these algorithms perform in the presence of background noise, which can vary in intensity across different environments. Therefore, in this study, we evaluate the performance of cough sound segmentation algorithms in the presence of background noise. Specifically, we examine algorithms employing conventional feature engineering and machine learning methods, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and a combination of CNNs and RNNs. These algorithms are developed using relatively clean cough signals but evaluated under both clean and noisy conditions. The results indicate that, while the performance of all algorithms declined in the presence of background noise, the combination of CNNs and RNNs yielded the best cough segmentation results under both clean and noisy conditions. These findings can contribute to the development of noise-robust cough sound segmentation algorithms for objective cough sound analysis in noisy conditions.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143559577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluation of FES-induced Muscle Fatigue and Recovery using Torque and Surface Electromyography.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10782626

Chenglin Lyu, Georgios Panteli, L Cornelius Bollheimer, Steffen Leonhardt, Philip von Platen

{"title":"Evaluation of FES-induced Muscle Fatigue and Recovery using Torque and Surface Electromyography.","authors":"Chenglin Lyu, Georgios Panteli, L Cornelius Bollheimer, Steffen Leonhardt, Philip von Platen","doi":"10.1109/EMBC53108.2024.10782626","DOIUrl":"https://doi.org/10.1109/EMBC53108.2024.10782626","url":null,"abstract":"Functional Electrical Stimulation (FES) plays a crucial role in the rehabilitation and mobility of patients, but it introduces muscle fatigue which can impact the treatment process. This work presents a novel approach for monitoring FES-induced muscle fatigue and recovery by torque and surface electromyography (sEMG) signals. A predefined pattern of FES is applied on the rectus femoris muscle to induce isometric contraction, while torque and sEMG data are collected to assess muscle fatigue and subsequent recovery. The sEMG data are filtered using notch stop and high-pass filters, and subsequently assessed in both the time domain (Root Mean Square, RMS) and frequency domain (mean frequency). The results indicated that torque and RMS decreased during fatigue and increased during recovery, while the mean frequency of the sEMG signal exhibited an opposite trend. These findings provide valuable insights into the dynamics of muscle fatigue under FES and have implications for enhancing the understanding and management of muscle fatigue in rehabilitation therapy.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143559583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust Sequence-to-sequence Voice Conversion for Electrolaryngeal Speech Enhancement in Noisy and Reverberant Conditions. 嘈杂和混响条件下用于电喉音增强的稳健序列到序列语音转换

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10781979

Ding Ma, Yeonjong Choi, Fengji Li, Chao Xie, Kazuhiro Kobayashi, Tomoki Toda

{"title":"Robust Sequence-to-sequence Voice Conversion for Electrolaryngeal Speech Enhancement in Noisy and Reverberant Conditions.","authors":"Ding Ma, Yeonjong Choi, Fengji Li, Chao Xie, Kazuhiro Kobayashi, Tomoki Toda","doi":"10.1109/EMBC53108.2024.10781979","DOIUrl":"https://doi.org/10.1109/EMBC53108.2024.10781979","url":null,"abstract":"Electrolaryngeal (EL) speech, an artificial speech produced by an electrolarynx for laryngectomees, lacks essential phonetic features, and differs in temporal structure from normal speech, resulting in poor naturalness and intelligibility. To address this deficiency, sequence-to-sequence (seq2seq) voice conversion (VC) models have been applied in converting EL speech to normal speech (EL2SP), showing some promising performances. However, previous studies mostly focus on converting clean EL speech, thereby restricting the further applicability in real-world scenarios, especially when the EL speech is inevitably interfered with background noise and reverberation. In light of this, we suggest novel training techniques based on seq2seq VC to enhance the robustness of real-world EL2SP. We first pretrain a normal-to-normal seq2seq VC model based on a text-to-speech model. Then, a two-stage fine-tuning is conducted by effectively using pseudo noisy and reverberant EL speech data artificially generated from only a small amount of original clean data available. Several design options are investigated to figure out the effectiveness of our method. The significant improvements presented in experimental results indicate that our method can non-trivially handle both clean and noisy-reverberant EL speech, enhancing the robustness of EL2SP in real-world scenarios.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143559989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Synthetic ultrasound images to benchmark echocardiography-based biomechanics. 合成超声图像，为基于超声心动图的生物力学提供基准。

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10782447

Tanmay Mukherjee, Sunder Neelakantan, Kyle Myers, Carl Tong, Reza Avazmohammadi

{"title":"Synthetic ultrasound images to benchmark echocardiography-based biomechanics.","authors":"Tanmay Mukherjee, Sunder Neelakantan, Kyle Myers, Carl Tong, Reza Avazmohammadi","doi":"10.1109/EMBC53108.2024.10782447","DOIUrl":"https://doi.org/10.1109/EMBC53108.2024.10782447","url":null,"abstract":"Brightness mode (B-mode) ultrasound is a common imaging modality in the clinical assessment of several cardiovascular diseases. The utility of ultrasound-based functional indices such as the ejection fraction (EF) and stroke volume (SV) is widely described in diagnosing advanced-stage cardiovascular diseases. Additionally, structural indices obtained through the analysis of cardiac motion have been found to be important in the early-stage assessment of structural heart diseases, such as hypertrophic cardiomyopathy and myocardial infarction. Estimating heterogeneous variations in cardiac motion through B-mode ultrasound imaging is a crucial component of patient care. Despite the benefits of such imaging techniques, motion estimation algorithms are susceptible to variability between vendors due to the lack of benchmark motion quantities. In contrast, finite element (FE) simulations of cardiac biomechanics leverage well-established constitutive models of the myocardium to ensure reproducibility. In this study, we developed a methodology to create synthetic B-mode ultrasound images from FE simulations. The proposed methodology provides a detailed representation of displacements and strains under complex mouse-specific loading protocols of the LV. A comparison between the synthetic images and FE simulations revealed qualitative similarity in displacement patterns, thereby yielding benchmark quantities to improve the reproducibility of motion estimation algorithms. Thus, the study provides a methodology to create an extensive repository of images describing complex motion patterns to facilitate the enhanced reproducibility of cardiac motion analysis.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143560036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Toward EEG-Based Objective Assessment of Emotion Intensity. 基于脑电图的情绪强度客观评估。

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10781662

Pin-Han Ho, Yong-Sheng Chen, Chun-Shu Wei

引用次数: 0

Wearable-oriented Support for Interpretation of Behavioural Effects on Sleep. 以可穿戴设备为导向，支持解释行为对睡眠的影响。

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10781768

Clauirton A Siebra, Jonysberg Quintino, Andre L M Santos, Fabio Q B Da Silva

{"title":"Wearable-oriented Support for Interpretation of Behavioural Effects on Sleep.","authors":"Clauirton A Siebra, Jonysberg Quintino, Andre L M Santos, Fabio Q B Da Silva","doi":"10.1109/EMBC53108.2024.10781768","DOIUrl":"https://doi.org/10.1109/EMBC53108.2024.10781768","url":null,"abstract":"Daily behaviour directly impacts health in the short and long term. Thus, embracing and maintaining healthy behaviours work like a preventive action, avoiding or delaying the emergence of chronic diseases. The process of changing daily routines toward healthy behaviours starts by understanding the current problems. Wearable and deep learning (DL) technologies represent important resources for supporting such an understanding. This paper discusses a strategy to interpret multifeatured longitudinal wearable data to analyse possible causes of health issues. We use the sleep domain as a case example where the aim is to clarify the reasons for poor sleep quality. A dataset with wearable data of 1874 days was used to create an explainable DL model, which indicates the main day-before-night sleep behaviours that may cause poor sleep quality. We use a comparative analysis with a hormone-based framework for sleep control as the form of validation. The results show that the explanations corroborate the results of the literature. However, other datasets with more features should be explored to verify the combination of these features and their effects on the health aspect under study.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143560175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A signal processing tool for extracting features from arterial blood pressure and photoplethysmography waveforms.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10782973

R Pal, A Rudas, S Kim, J N Chiang, M Cannesson

{"title":"A signal processing tool for extracting features from arterial blood pressure and photoplethysmography waveforms.","authors":"R Pal, A Rudas, S Kim, J N Chiang, M Cannesson","doi":"10.1109/EMBC53108.2024.10782973","DOIUrl":"10.1109/EMBC53108.2024.10782973","url":null,"abstract":"Arterial blood pressure (ABP) and photoplethysmography (PPG) waveforms contain valuable clinical information and play a crucial role in cardiovascular health monitoring, medical research, and managing medical conditions. The features extracted from PPG waveforms have various clinical applications ranging from blood pressure monitoring to nociception monitoring, while features from ABP waveforms can be used to calculate cardiac output and predict hypertension or hypotension. In recent years, many machine learning models have been proposed to utilize both PPG and ABP waveform features for these healthcare applications. However, the lack of standardized tools for extracting features from these waveforms could potentially affect their clinical effectiveness. In this paper, we propose an automatic signal processing tool for extracting features from ABP and PPG waveforms. Additionally, we generated a PPG feature library from a large perioperative dataset comprising 17,327 patients using the proposed tool. This PPG feature library can be used to explore the potential of these extracted features to develop machine learning models for non-invasive blood pressure estimation.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143558918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0