Pub Date : 2024-01-03DOI: 10.4015/s1016237223500400
Rahul Kumar, Yogender Aggarwal, Vinod Kumar Nigam, R. K. Sinha
Atherosclerosis-generated coronary artery disease (CAD) causes the anomaly of autonomic activity. The assessment of autonomic balance under disease conditions is of clinical interest. Thus, this work aimed to evaluate the CAD using Poincare-derived stress score (SS) and sympathetic/sympathetic-parasympathetic (S/PS). A total of 60 male (50–55 years) volunteers including CAD ([Formula: see text]= 30) and control ([Formula: see text]= 30) participated in this work. Digital lead-II electrocardiogram (ECG) was recorded for 10 min in a supine position and filtered to remove noises. A total of 10 tachogram samples were computed from each subject for a 5 min ECG signal duration with a shift of 30 s on the processed signal using Acqknowledge 4.0 (Biopac Systems Inc., USA). The heart rate variability (HRV) parameters were computed from the obtained tachogram. The computed Poincare plot parameters were used to derive the SS and S/PS ratios. The regression model was employed to observe the correlation of SS and S/PS to the HRV parameters. The obtained results revealed reduced HRV with higher sympathetic and reduced parasympathetic activity under CAD than the control subjects. The autonomic dysfunction was observed with significantly higher values of SS and S/PS in CAD than in the control subjects. The S/PS was observed to be positively associated with LF/HF and SD2/SD1 parameters. While SS was found to be positively correlated to the LF and LF/HF parameters in CAD and control subjects. The obtained linear strong correlation of SS and S/PS with control and CAD subjects suggested the use of SS and S/PS as excellent sympathetic activity and sympathovagal balance markers.
动脉粥样硬化引起的冠状动脉疾病（CAD）会导致自律神经活动异常。评估疾病条件下的自律神经平衡具有临床意义。因此，这项研究旨在使用 Poincare 衍生的压力评分（SS）和交感/交感-副交感（S/PS）对 CAD 进行评估。共有 60 名男性（50-55 岁）志愿者参与了这项工作，包括 CAD（[公式：见正文]= 30）和对照组（[公式：见正文]= 30）。志愿者取仰卧位，记录 10 分钟数字 II 导联心电图（ECG），并进行滤波以去除杂音。使用 Acqknowledge 4.0（Biopac Systems Inc.）根据获得的心动图计算心率变异性（HRV）参数。计算出的 Poincare 图参数用于得出 SS 和 S/PS 比率。采用回归模型观察 SS 和 S/PS 与心率变异参数的相关性。结果显示，与对照组相比，CAD 患者的心率变异降低，交感神经活动增加，副交感神经活动减少。与对照组受试者相比，CAD 受试者的 SS 和 S/PS 值明显较高，这表明自律神经功能紊乱。据观察，S/PS 与 LF/HF 和 SD2/SD1 参数呈正相关。而在 CAD 和对照组受试者中，SS 与 LF 和 LF/HF 参数呈正相关。SS和S/PS与对照组和CAD受试者的线性强相关性表明，SS和S/PS是极佳的交感活性和交感摇摆平衡标记物。
{"title":"CORRELATION OF POINCARE PLOT DERIVED STRESS SCORE AND HEART RATE VARIABILITY PARAMETERS IN THE ASSESSMENT OF CORONARY ARTERY DISEASE","authors":"Rahul Kumar, Yogender Aggarwal, Vinod Kumar Nigam, R. K. Sinha","doi":"10.4015/s1016237223500400","DOIUrl":"https://doi.org/10.4015/s1016237223500400","url":null,"abstract":"Atherosclerosis-generated coronary artery disease (CAD) causes the anomaly of autonomic activity. The assessment of autonomic balance under disease conditions is of clinical interest. Thus, this work aimed to evaluate the CAD using Poincare-derived stress score (SS) and sympathetic/sympathetic-parasympathetic (S/PS). A total of 60 male (50–55 years) volunteers including CAD ([Formula: see text]= 30) and control ([Formula: see text]= 30) participated in this work. Digital lead-II electrocardiogram (ECG) was recorded for 10 min in a supine position and filtered to remove noises. A total of 10 tachogram samples were computed from each subject for a 5 min ECG signal duration with a shift of 30 s on the processed signal using Acqknowledge 4.0 (Biopac Systems Inc., USA). The heart rate variability (HRV) parameters were computed from the obtained tachogram. The computed Poincare plot parameters were used to derive the SS and S/PS ratios. The regression model was employed to observe the correlation of SS and S/PS to the HRV parameters. The obtained results revealed reduced HRV with higher sympathetic and reduced parasympathetic activity under CAD than the control subjects. The autonomic dysfunction was observed with significantly higher values of SS and S/PS in CAD than in the control subjects. The S/PS was observed to be positively associated with LF/HF and SD2/SD1 parameters. While SS was found to be positively correlated to the LF and LF/HF parameters in CAD and control subjects. The obtained linear strong correlation of SS and S/PS with control and CAD subjects suggested the use of SS and S/PS as excellent sympathetic activity and sympathovagal balance markers.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"3 2","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139450981","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}
The word epilepsy is related to a neurological disease occurred by abnormalities of brain neurons. Timely detection of epilepsy is helpful for patients to decrease the mortality rate. To detect seizures, the Encephalogram (EEG) signals are analyzed based on monitoring the conditions of patients, and seizures can be detected from the EEG signal at appropriate times. The manual detection from the EEG signal requires more time for detecting the seizures and also it needs domain knowledge. The miss detection is eliminated by improving the classification performance in automatic epilepsy detection. Nowadays, deep learning models have not been greatly harnessed in the detection of epileptic seizures due to inappropriate descriptions of time-domain signals and sub-optimal classifier design. The aforementioned issues are combated by the novel Adaptive Hybrid Deep Learning (AHDL) approaches for epilepsy detection using EEG signals. Initially, the required EEG signal is collected from benchmark datasets. The collected signals are subjected to a signal decomposition phase that is accomplished by five levels of decomposition using Dual-Tree Complex Wavelet Transform (DTCWT), where the parameters are tuned by Improved Probability-based Coyote Optimization Algorithm (IP-COA). Further, the decomposed signal is given for feature extraction, where it divides the signal into two phases. In the first phase, the first feature set is obtained by using One-Dimensional Convolutional Neural Network (1DCNN), whereas in the second phase, the proposed model utilizes Auto Encoder (AE) to provide the second feature set. These resultant features are getting fused and the optimal feature selection process is found, where the features are obtained optimally by the IP-COA. Finally, epilepsy detection is accomplished with the aid of proposed AHDL with both Radial Basis-Recurrent Neural Networks (RB-RNN), where the hyperparameters are optimized using IP-COA. Thus, the experimental results illustrate that the suggested model enhances the detection and classification rate.
{"title":"HEURISTIC-ASSISTED ADAPTIVE HYBRID DEEP LEARNING MODEL WITH FEATURE SELECTION FOR EPILEPSY DETECTION USING EEG SIGNALS","authors":"Nilankar Bhanja, Sanjib Kumar Dhara, Prabodh Khampariya","doi":"10.4015/s1016237223500369","DOIUrl":"https://doi.org/10.4015/s1016237223500369","url":null,"abstract":"The word epilepsy is related to a neurological disease occurred by abnormalities of brain neurons. Timely detection of epilepsy is helpful for patients to decrease the mortality rate. To detect seizures, the Encephalogram (EEG) signals are analyzed based on monitoring the conditions of patients, and seizures can be detected from the EEG signal at appropriate times. The manual detection from the EEG signal requires more time for detecting the seizures and also it needs domain knowledge. The miss detection is eliminated by improving the classification performance in automatic epilepsy detection. Nowadays, deep learning models have not been greatly harnessed in the detection of epileptic seizures due to inappropriate descriptions of time-domain signals and sub-optimal classifier design. The aforementioned issues are combated by the novel Adaptive Hybrid Deep Learning (AHDL) approaches for epilepsy detection using EEG signals. Initially, the required EEG signal is collected from benchmark datasets. The collected signals are subjected to a signal decomposition phase that is accomplished by five levels of decomposition using Dual-Tree Complex Wavelet Transform (DTCWT), where the parameters are tuned by Improved Probability-based Coyote Optimization Algorithm (IP-COA). Further, the decomposed signal is given for feature extraction, where it divides the signal into two phases. In the first phase, the first feature set is obtained by using One-Dimensional Convolutional Neural Network (1DCNN), whereas in the second phase, the proposed model utilizes Auto Encoder (AE) to provide the second feature set. These resultant features are getting fused and the optimal feature selection process is found, where the features are obtained optimally by the IP-COA. Finally, epilepsy detection is accomplished with the aid of proposed AHDL with both Radial Basis-Recurrent Neural Networks (RB-RNN), where the hyperparameters are optimized using IP-COA. Thus, the experimental results illustrate that the suggested model enhances the detection and classification rate.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"33 9","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139008980","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}
Pub Date : 2023-12-01DOI: 10.4015/s1016237223500370
S. Tripathi
Magnetic Resonance Imaging (MRI) provides detailed information about soft tissues, which is essential for disease analysis. However, the presence of Rician noise in MR images introduces uncertainties that challenge medical practitioners during analysis. The objective of our research paper is to introduce an innovative dual-channel deep learning (DL) model designed to effectively denoize MR images. The methodology of this model integrates two distinct pathways, each equipped with unique normalization and activation techniques, facilitating the creation of a wide range of image features. Specifically, we employ Group Normalization in combination with Parametric Rectified Linear Units (PRELU) and Local Response Normalizations (LRN) alongside Scaled Exponential Linear Units (SELU) within both channels of our denoizing network. The outcomes of our proposed network exhibit clinical relevance, empowering medical professionals to conduct more efficient disease analysis. When evaluated by experienced radiologists, our results were deemed satisfactory. The network achieved a noteworthy improvement in performance metrics without requiring retraining. Specifically, there was a ([Formula: see text])% enhancement in Peak Signal-to-Noise Ratio (PSNR) values and a ([Formula: see text])% improvement in Structural Similarity Index (SSIM) values. Furthermore, when evaluated on the dataset on which the network was initially trained, the increase in PSNR and SSIM values was even more pronounced, with a ([Formula: see text])% improvement in PSNR and a ([Formula: see text])% enhancement in SSIM. Evaluation metrics, such as SSIM and PSNR, demonstrated a notable enhancement in the results obtained using our network. The statistical significance of our findings is evident, with [Formula: see text]-values consistently less than 0.05 ([Formula: see text] < 0.05). Importantly, our network demonstrates exceptional generalizability, as it performs remarkably well on different datasets without the need for retraining.
{"title":"MAGNETIC RESONANCE IMAGE DENOIZING USING A DUAL-CHANNEL DISCRIMINATIVE DENOIZING NETWORK","authors":"S. Tripathi","doi":"10.4015/s1016237223500370","DOIUrl":"https://doi.org/10.4015/s1016237223500370","url":null,"abstract":"Magnetic Resonance Imaging (MRI) provides detailed information about soft tissues, which is essential for disease analysis. However, the presence of Rician noise in MR images introduces uncertainties that challenge medical practitioners during analysis. The objective of our research paper is to introduce an innovative dual-channel deep learning (DL) model designed to effectively denoize MR images. The methodology of this model integrates two distinct pathways, each equipped with unique normalization and activation techniques, facilitating the creation of a wide range of image features. Specifically, we employ Group Normalization in combination with Parametric Rectified Linear Units (PRELU) and Local Response Normalizations (LRN) alongside Scaled Exponential Linear Units (SELU) within both channels of our denoizing network. The outcomes of our proposed network exhibit clinical relevance, empowering medical professionals to conduct more efficient disease analysis. When evaluated by experienced radiologists, our results were deemed satisfactory. The network achieved a noteworthy improvement in performance metrics without requiring retraining. Specifically, there was a ([Formula: see text])% enhancement in Peak Signal-to-Noise Ratio (PSNR) values and a ([Formula: see text])% improvement in Structural Similarity Index (SSIM) values. Furthermore, when evaluated on the dataset on which the network was initially trained, the increase in PSNR and SSIM values was even more pronounced, with a ([Formula: see text])% improvement in PSNR and a ([Formula: see text])% enhancement in SSIM. Evaluation metrics, such as SSIM and PSNR, demonstrated a notable enhancement in the results obtained using our network. The statistical significance of our findings is evident, with [Formula: see text]-values consistently less than 0.05 ([Formula: see text] < 0.05). Importantly, our network demonstrates exceptional generalizability, as it performs remarkably well on different datasets without the need for retraining.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":" 523","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138610882","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}
Pub Date : 2023-11-30DOI: 10.4015/s1016237223500394
Xia Zhang, C. Yan
Epilepsy seizures are caused by abnormal, excessive, or synchronized neuronal activity in the brain, which is difficult to treat and is extremely stubborn. Therefore, studying the activity of epilepsy can greatly contribute to its diagnosis and treatment. The original signal is decomposed into IMFs and residual by ensemble empirical mode decomposition (EEMD), and then the first three intrinsic mode functions (IMF) are selected to replace the original signal, and the nonlinear and non-stationary problems of the original signal are solved. The Least Squares Support Vector Machine (LSSVM) was used as the classifier, its parameters (gam and sig2) are optimized by Particle Swarm Optimization (PSO). The experiment used the EEG database published by the University of Bonn (UoB) to realize the classification of normal, interictal and ictal periods. When PSO was employed, the recognition accuracy of the test set was 93.33%, with a classification time of 0.035 s and the Information Transfer Rate (ITR) of 3.77 bpm in training 70 classes with 100 samples each. In contrast, without PSO, the recognition accuracy of the test set was 92%, with a classification time of 0.039 s and the ITR of 2.88 bpm without PSO in training 70 classes with 100 samples each. The experimental results show that EEMD and LSSVM can effectively implement the three-classification problem and provide an effective means for the onset prediction of epilepsy patients.
{"title":"PREDICTION OF EPILEPSY BASED ON EEMD AND LSSVM DOUBLE CLASSIFICATION","authors":"Xia Zhang, C. Yan","doi":"10.4015/s1016237223500394","DOIUrl":"https://doi.org/10.4015/s1016237223500394","url":null,"abstract":"Epilepsy seizures are caused by abnormal, excessive, or synchronized neuronal activity in the brain, which is difficult to treat and is extremely stubborn. Therefore, studying the activity of epilepsy can greatly contribute to its diagnosis and treatment. The original signal is decomposed into IMFs and residual by ensemble empirical mode decomposition (EEMD), and then the first three intrinsic mode functions (IMF) are selected to replace the original signal, and the nonlinear and non-stationary problems of the original signal are solved. The Least Squares Support Vector Machine (LSSVM) was used as the classifier, its parameters (gam and sig2) are optimized by Particle Swarm Optimization (PSO). The experiment used the EEG database published by the University of Bonn (UoB) to realize the classification of normal, interictal and ictal periods. When PSO was employed, the recognition accuracy of the test set was 93.33%, with a classification time of 0.035 s and the Information Transfer Rate (ITR) of 3.77 bpm in training 70 classes with 100 samples each. In contrast, without PSO, the recognition accuracy of the test set was 92%, with a classification time of 0.039 s and the ITR of 2.88 bpm without PSO in training 70 classes with 100 samples each. The experimental results show that EEMD and LSSVM can effectively implement the three-classification problem and provide an effective means for the onset prediction of epilepsy patients.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"1 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139202284","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}
Pub Date : 2023-11-22DOI: 10.4015/s1016237223500382
A. Siddiqi
Image de-noising is an essential tool for removing unwanted signals from an image. In Computed Tomography (CT) images, the image quality is degraded by the absorption of X-rays and quantum noise, which is generated due to the excitement of X-ray photons. Removal of noise and preservation of information in the CT images becomes a challenge for an imaging algorithm design. During the algorithm design selection of dataset is an important aspect for deducing results. The dataset used in this research comprises of 60 CT scan images of liver cancer archived from the arterial contrast enhanced phase. In this phase the cancer cells appear more intense as compared to the healthy liver tissue due to the absorption of contrast enhancing reagent. The experimentation for appropriate noise removal filter selection is done by testing the images using Mean, Median and Weiner Filters. The filter selected should give an image output which has minimal randomness, sharper boundaries and no blur. The de-noised image will provide a better visibility of the disease to the radiologist and physician. The performance parameters used for the assessment of various filters used in the study include visual assessment, entropy and signal to noise ratio (SNR) of the images. Median filter gives an accuracy of 96%, mean filter is 76.2% accurate with respect to original information and Weiner filters has an accuracy of 79.7%.
图像去噪是去除图像中不需要的信号的重要工具。在计算机断层扫描（CT）图像中，图像质量会因 X 射线的吸收和量子噪声而下降，量子噪声是由于 X 射线光子的激发而产生的。去除 CT 图像中的噪声并保留其信息成为成像算法设计的一项挑战。在算法设计过程中，数据集的选择是推导结果的一个重要方面。本研究使用的数据集包括 60 张动脉造影剂增强阶段存档的肝癌 CT 扫描图像。在这一阶段，由于吸收了对比度增强试剂，与健康的肝脏组织相比，癌细胞的密度更高。通过使用平均值滤波器、中值滤波器和韦纳滤波器对图像进行测试，以选择合适的去噪滤波器。所选滤波器输出的图像应具有最小的随机性、更清晰的边界和无模糊。去噪后的图像将为放射科医生和内科医生提供更好的疾病可见度。用于评估研究中使用的各种滤波器的性能参数包括图像的视觉评估、熵和信噪比（SNR）。中值滤波器的准确率为 96%，平均滤波器对原始信息的准确率为 76.2%，而韦纳滤波器的准确率为 79.7%。
{"title":"FILTER SELECTION FOR REMOVING NOISE FROM CT SCAN IMAGES USING DIGITAL IMAGE PROCESSING ALGORITHM","authors":"A. Siddiqi","doi":"10.4015/s1016237223500382","DOIUrl":"https://doi.org/10.4015/s1016237223500382","url":null,"abstract":"Image de-noising is an essential tool for removing unwanted signals from an image. In Computed Tomography (CT) images, the image quality is degraded by the absorption of X-rays and quantum noise, which is generated due to the excitement of X-ray photons. Removal of noise and preservation of information in the CT images becomes a challenge for an imaging algorithm design. During the algorithm design selection of dataset is an important aspect for deducing results. The dataset used in this research comprises of 60 CT scan images of liver cancer archived from the arterial contrast enhanced phase. In this phase the cancer cells appear more intense as compared to the healthy liver tissue due to the absorption of contrast enhancing reagent. The experimentation for appropriate noise removal filter selection is done by testing the images using Mean, Median and Weiner Filters. The filter selected should give an image output which has minimal randomness, sharper boundaries and no blur. The de-noised image will provide a better visibility of the disease to the radiologist and physician. The performance parameters used for the assessment of various filters used in the study include visual assessment, entropy and signal to noise ratio (SNR) of the images. Median filter gives an accuracy of 96%, mean filter is 76.2% accurate with respect to original information and Weiner filters has an accuracy of 79.7%.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"16 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139247400","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}
Pub Date : 2023-11-18DOI: 10.4015/s101623722350031x
Samla Salim, R. Sarath
Breast cancer detection is a highly fatal disease and is normally detected considering histopathological images (HPIs). However, the complexity of the HPIs makes it challenging to detect breast cancer accurately. Further, manual detection is highly time-consuming and subjective and depends on the experience of the medical professionals. To overcome these issues, an effective deep learning (DL) method for detecting breast cancer from HPIs is proposed. Here, the proposed approach is realized using various processes, such as pre-processing, blood cell segmentation, feature extraction, and classification. Segmentation is accomplished using the SegAN, and classification is performed using the deep convolutional neural network (DCNN). Both networks are trained using the proposed invasive water Ebola optimization (IWEO) algorithm. The efficiency of breast cancer detection is improved by using various features, such as shape features, histogram of gradients (HOG) and local gradient patterns (LGP). Further, the IWEO-DCNN is inspected for its dominance by considering measures, such as accuracy, test negative rate (TNR) and test positive rate (TPR), and the experimental results show that the IWEO-DCNN attained a maximal accuracy of 0.963, TNR of 0.963 and TPR of 0.950.
{"title":"HYBRID OPTIMIZATION ENABLED SEGMENTATION AND DEEP LEARNING FOR BREAST CANCER DETECTION AND CLASSIFICATION USING HISTOPATHOLOGICAL IMAGES","authors":"Samla Salim, R. Sarath","doi":"10.4015/s101623722350031x","DOIUrl":"https://doi.org/10.4015/s101623722350031x","url":null,"abstract":"Breast cancer detection is a highly fatal disease and is normally detected considering histopathological images (HPIs). However, the complexity of the HPIs makes it challenging to detect breast cancer accurately. Further, manual detection is highly time-consuming and subjective and depends on the experience of the medical professionals. To overcome these issues, an effective deep learning (DL) method for detecting breast cancer from HPIs is proposed. Here, the proposed approach is realized using various processes, such as pre-processing, blood cell segmentation, feature extraction, and classification. Segmentation is accomplished using the SegAN, and classification is performed using the deep convolutional neural network (DCNN). Both networks are trained using the proposed invasive water Ebola optimization (IWEO) algorithm. The efficiency of breast cancer detection is improved by using various features, such as shape features, histogram of gradients (HOG) and local gradient patterns (LGP). Further, the IWEO-DCNN is inspected for its dominance by considering measures, such as accuracy, test negative rate (TNR) and test positive rate (TPR), and the experimental results show that the IWEO-DCNN attained a maximal accuracy of 0.963, TNR of 0.963 and TPR of 0.950.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"40 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139261402","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}
Pub Date : 2023-11-18DOI: 10.4015/s1016237223500357
B. Sangeetha, R. Periyasamy
Lung sound (LS) signals are vital for diagnosing pulmonary disorders. However, heart sound (HS) interferes with the analysis of LS, leading to the misdiagnosis of lung disorders. To address this issue, we propose an Enhanced Variational Mode Decomposition (E-VMD) technique to remove HS interference from LSs effectively. The E-VMD method automatically determines the mode number for signal decomposition based on the characteristics of variational mode functions (VMFs) such as normalized permutation entropy, kurtosis index, extreme frequency domain, and energy loss coefficient. The performance of the proposed denoising technique was evaluated using six performance metrics: Signal-to-noise ratio (SNR), root mean square error (RMSE), normalized mean absolute error (nMAE), correlation coefficient factor (CCF), CPU[Formula: see text], and CPU[Formula: see text]. In comparison to other denoising methods such as empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), complementary ensemble empirical mode decomposition (CEEMD), singular spectrum analysis (SSA), and variational mode decomposition (VMD), the new E-VMD method demonstrates superior denoising outcome. The proposed method was evaluated using LS recorded from the outpatient department of Thoracic Medicine at Thanjavur Medical College and Hospital, Thanjavur. The obtained performance measures are as follows: RMSE: 0.02103 ± 0.00054, SNR: 28.52464 ± 0.00253, nMAE: 0.00009 ± 0.00056, CCF: 0.9962, CPU[Formula: see text]: 34.586, and CPU[Formula: see text]: 0.452 s. These results affirm the adaptability and robustness of the proposed method, even in the existence of HS noise. This method improves denoising accuracy and computational efficacy, making it a useful tool for improving the analysis of LS signals and assisting in medical diagnostics. This technique utilizes an electronic stethoscope, a common clinical device used by healthcare professionals for detecting lung disease.
肺音（LS）信号对诊断肺部疾病至关重要。然而，心音（HS）会干扰 LS 的分析，导致肺部疾病的误诊。为解决这一问题，我们提出了一种增强变异模式分解（E-VMD）技术，以有效去除 LS 中的 HS 干扰。E-VMD 方法根据变异模态函数（VMF）的归一化排列熵、峰度指数、极频域和能量损失系数等特征，自动确定信号分解的模态数。利用六项性能指标对所提出的去噪技术的性能进行了评估：信噪比（SNR）、均方根误差（RMSE）、归一化平均绝对误差（nMAE）、相关系数因子（CCF）、CPU[计算公式：见正文]和 CPU[计算公式：见正文]。与其他去噪方法（如经验模式分解法（EMD）、集合经验模式分解法（EEMD）、互补集合经验模式分解法（CEEMD）、奇异频谱分析法（SSA）和变异模式分解法（VMD））相比，新的 E-VMD 方法显示出更优越的去噪效果。我们使用坦贾武尔医学院和坦贾武尔医院胸腔内科门诊部记录的 LS 对所提出的方法进行了评估。获得的性能指标如下RMSE：0.02103 ± 0.00054，SNR：28.52464 ± 0.00253，nMAE：0.00009 ± 0.00056，CCF：0.9962，CPU[计算公式：见正文]：34.586，CPU[计算公式：见正文]：0.9962：34.586，CPU[公式：见文本]：0.452 秒：这些结果肯定了所提方法的适应性和鲁棒性，即使在存在 HS 噪声的情况下也是如此。该方法提高了去噪精度和计算效率，使其成为改进 LS 信号分析和辅助医疗诊断的有用工具。这项技术利用了电子听诊器，这是医护人员用于检测肺部疾病的常用临床设备。
{"title":"HEART SOUND NOISE SEPARATION FROM LUNG SOUND BASED ON ENHANCED VARIATIONAL MODE DECOMPOSITION FOR DIAGNOSING PULMONARY DISEASES","authors":"B. Sangeetha, R. Periyasamy","doi":"10.4015/s1016237223500357","DOIUrl":"https://doi.org/10.4015/s1016237223500357","url":null,"abstract":"Lung sound (LS) signals are vital for diagnosing pulmonary disorders. However, heart sound (HS) interferes with the analysis of LS, leading to the misdiagnosis of lung disorders. To address this issue, we propose an Enhanced Variational Mode Decomposition (E-VMD) technique to remove HS interference from LSs effectively. The E-VMD method automatically determines the mode number for signal decomposition based on the characteristics of variational mode functions (VMFs) such as normalized permutation entropy, kurtosis index, extreme frequency domain, and energy loss coefficient. The performance of the proposed denoising technique was evaluated using six performance metrics: Signal-to-noise ratio (SNR), root mean square error (RMSE), normalized mean absolute error (nMAE), correlation coefficient factor (CCF), CPU[Formula: see text], and CPU[Formula: see text]. In comparison to other denoising methods such as empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), complementary ensemble empirical mode decomposition (CEEMD), singular spectrum analysis (SSA), and variational mode decomposition (VMD), the new E-VMD method demonstrates superior denoising outcome. The proposed method was evaluated using LS recorded from the outpatient department of Thoracic Medicine at Thanjavur Medical College and Hospital, Thanjavur. The obtained performance measures are as follows: RMSE: 0.02103 ± 0.00054, SNR: 28.52464 ± 0.00253, nMAE: 0.00009 ± 0.00056, CCF: 0.9962, CPU[Formula: see text]: 34.586, and CPU[Formula: see text]: 0.452 s. These results affirm the adaptability and robustness of the proposed method, even in the existence of HS noise. This method improves denoising accuracy and computational efficacy, making it a useful tool for improving the analysis of LS signals and assisting in medical diagnostics. This technique utilizes an electronic stethoscope, a common clinical device used by healthcare professionals for detecting lung disease.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"19 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139262088","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}
Pub Date : 2023-11-10DOI: 10.4015/s1016237223500345
Supornpit Na Pibul, Pornchai Phukpattaranont
This research presents a method to select the best parameters of an extreme learning machine (ELM) for estimating grasping force from two-channel surface electromyography (sEMG) signals. Its advantages compared to the use of multi-channel sEMG signals include faster computing, lower electrode costs, and easier usage. The proposed method is appropriate for certain applications where time is very important but accuracy can be slightly compromised. The study recorded sEMG signals from 20 healthy volunteers, 10 males and 10 females, aged 22-52 years. The recorded sEMG signals were used in testing the proposed optimization method for grasping force estimation. It was found that the proposed method for optimizing the number of nodes, gain, and factor values would make the force estimation more efficient. The results show that the optimum number of nodes, gain, and factor values is 4, 0.05, and 1.0045, respectively. The resulting root mean square error and correlation coefficient values in force estimation from the ELM optimization method were 2.5642 and 0.9287, respectively, when the computing time was only 0.0038 s. These results show the feasibility of the proposed method for estimating force using only two-channel sEMG signals. As a result, real-time implementation, such as force estimation in robotic hand control for rehabilitation using sEMG signals, can be achieved efficiently.
{"title":"GRASPING FORCE ESTIMATION FROM TWO-CHANNEL ELECTROMYOGRAPHY SIGNALS USING EXTREME LEARNING MACHINE","authors":"Supornpit Na Pibul, Pornchai Phukpattaranont","doi":"10.4015/s1016237223500345","DOIUrl":"https://doi.org/10.4015/s1016237223500345","url":null,"abstract":"This research presents a method to select the best parameters of an extreme learning machine (ELM) for estimating grasping force from two-channel surface electromyography (sEMG) signals. Its advantages compared to the use of multi-channel sEMG signals include faster computing, lower electrode costs, and easier usage. The proposed method is appropriate for certain applications where time is very important but accuracy can be slightly compromised. The study recorded sEMG signals from 20 healthy volunteers, 10 males and 10 females, aged 22-52 years. The recorded sEMG signals were used in testing the proposed optimization method for grasping force estimation. It was found that the proposed method for optimizing the number of nodes, gain, and factor values would make the force estimation more efficient. The results show that the optimum number of nodes, gain, and factor values is 4, 0.05, and 1.0045, respectively. The resulting root mean square error and correlation coefficient values in force estimation from the ELM optimization method were 2.5642 and 0.9287, respectively, when the computing time was only 0.0038 s. These results show the feasibility of the proposed method for estimating force using only two-channel sEMG signals. As a result, real-time implementation, such as force estimation in robotic hand control for rehabilitation using sEMG signals, can be achieved efficiently.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":" 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135191802","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}
Pub Date : 2023-11-07DOI: 10.4015/s1016237223500333
U. P. Prinith Kaveramma, U. Snekhalatha, Varshini Karthik, M. Anuradha
The primary objective of this study is to segment the uterine fibroids (leiomyoma) from the ultrasound images of the uterus through semantic segmentation, followed by second-order statistical feature extraction using the Gray-level Co-occurrence Matrix (GLCM). The next objective of the study is to compare the performance of the state-of-the-art method namely Vision Transformer (ViT) with three different machine learning (ML) classifiers such as the Support Vector Machine (SVM), Logistic Regression (LR) and [Formula: see text]-Nearest Neighbor ([Formula: see text]-NN) to classify the images into uterine fibroid and normal. The dataset consists of 50 ultrasound images of uterine fibroids and 50 normal images. Then the images are segmented using region-growing-based semantic segmentation followed by feature extraction and classification using the ML and deep learning (DL) classifiers. Among the ML classifiers, SVM produced a good accuracy of 93.1% compared to the other classifiers. ViT produced an excellent classification accuracy of 97.5%. Hence, ViT outperformed compared to the ML classifiers in uterine fibroid detection. These findings have important implications for clinical practice, as they could help physicians to diagnose and treat uterine fibroids more effectively.
{"title":"ULTRASOUND-BASED MACHINE LEARNING-AIDED DETECTION OF UTERINE FIBROIDS: INTEGRATING VISION TRANSFORMER FOR IMPROVED ANALYSIS","authors":"U. P. Prinith Kaveramma, U. Snekhalatha, Varshini Karthik, M. Anuradha","doi":"10.4015/s1016237223500333","DOIUrl":"https://doi.org/10.4015/s1016237223500333","url":null,"abstract":"The primary objective of this study is to segment the uterine fibroids (leiomyoma) from the ultrasound images of the uterus through semantic segmentation, followed by second-order statistical feature extraction using the Gray-level Co-occurrence Matrix (GLCM). The next objective of the study is to compare the performance of the state-of-the-art method namely Vision Transformer (ViT) with three different machine learning (ML) classifiers such as the Support Vector Machine (SVM), Logistic Regression (LR) and [Formula: see text]-Nearest Neighbor ([Formula: see text]-NN) to classify the images into uterine fibroid and normal. The dataset consists of 50 ultrasound images of uterine fibroids and 50 normal images. Then the images are segmented using region-growing-based semantic segmentation followed by feature extraction and classification using the ML and deep learning (DL) classifiers. Among the ML classifiers, SVM produced a good accuracy of 93.1% compared to the other classifiers. ViT produced an excellent classification accuracy of 97.5%. Hence, ViT outperformed compared to the ML classifiers in uterine fibroid detection. These findings have important implications for clinical practice, as they could help physicians to diagnose and treat uterine fibroids more effectively.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"52 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135540446","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}
Pub Date : 2023-11-04DOI: 10.4015/s1016237223500291
Dharini Raghavan, H. H. Adithya, S. Raghuram, K. V. Suma, Tricha Kulhalli
The World Health Organization (WHO) estimates that over 15 million infants are born before the entire period of pregnancy. Over a million neonatal deaths occurred in 2015 as a result of preterm delivery, which is the prime cause for most deaths among children under the age of five. Although preterm birth has no hereditary link, only 10% of preterm deliveries in high-income settings result in deaths, compared to a mortality rate of up to 90% in low-income nations. When preterm cases are discovered, in addition to enhanced care provided at home for the expecting mother, the growing foetus may also benefit from medication, hospitalization for the duration of the pregnancy, or both. Low-income countries also struggle with a lack of access to a comprehensive healthcare system, which makes it difficult to proactively detect premature births. Machine learning techniques have a great potential to improve this situation by providing an intelligent framework for the detection of critical situations and consequently alerting the individual in cases of anomalies. This will require further follow-up with medical specialists. In this work, we investigate the application of deep learning and machine learning techniques for the analysis of electrohysterogram (EHG) data, which are uterine electrical impulses used to detect preterm deliveries. The TPEHG dataset is used to train a variety of machine learning classifiers, such as Support Vector Machines, Logistic Regression, and Decision Trees, as well as Deep Neural Networks like Convolutional Neural Networks and LSTMs. Additionally, we use plurality voting to build an ensemble of various neural networks and binary classifiers that are trained to classify EHG signals. The ensemble machine learning classifier with five base classifiers produced the best results overall, with an accuracy of 98.99%, sensitivity of 98.3%, and specificity of 97.9% outperforming several state-of-the-art algorithms for preterm birth detection.
{"title":"ANALYSIS OF ELECTROHYSTEROGRAM SIGNALS AND PREDICTION OF PRETERM BIRTHS USING MACHINE LEARNING","authors":"Dharini Raghavan, H. H. Adithya, S. Raghuram, K. V. Suma, Tricha Kulhalli","doi":"10.4015/s1016237223500291","DOIUrl":"https://doi.org/10.4015/s1016237223500291","url":null,"abstract":"The World Health Organization (WHO) estimates that over 15 million infants are born before the entire period of pregnancy. Over a million neonatal deaths occurred in 2015 as a result of preterm delivery, which is the prime cause for most deaths among children under the age of five. Although preterm birth has no hereditary link, only 10% of preterm deliveries in high-income settings result in deaths, compared to a mortality rate of up to 90% in low-income nations. When preterm cases are discovered, in addition to enhanced care provided at home for the expecting mother, the growing foetus may also benefit from medication, hospitalization for the duration of the pregnancy, or both. Low-income countries also struggle with a lack of access to a comprehensive healthcare system, which makes it difficult to proactively detect premature births. Machine learning techniques have a great potential to improve this situation by providing an intelligent framework for the detection of critical situations and consequently alerting the individual in cases of anomalies. This will require further follow-up with medical specialists. In this work, we investigate the application of deep learning and machine learning techniques for the analysis of electrohysterogram (EHG) data, which are uterine electrical impulses used to detect preterm deliveries. The TPEHG dataset is used to train a variety of machine learning classifiers, such as Support Vector Machines, Logistic Regression, and Decision Trees, as well as Deep Neural Networks like Convolutional Neural Networks and LSTMs. Additionally, we use plurality voting to build an ensemble of various neural networks and binary classifiers that are trained to classify EHG signals. The ensemble machine learning classifier with five base classifiers produced the best results overall, with an accuracy of 98.99%, sensitivity of 98.3%, and specificity of 97.9% outperforming several state-of-the-art algorithms for preterm birth detection.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"55 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135774828","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}
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