R. K. Sinha, Joyani Das, P. Mazumder, Yogender Aggarwal
{"title":"支持向量机描述芦丁在链脲佐菌素诱导糖尿病模型下的疗效的非线性心率变异性特征","authors":"R. K. Sinha, Joyani Das, P. Mazumder, Yogender Aggarwal","doi":"10.4015/s1016237223500151","DOIUrl":null,"url":null,"abstract":"Diabetes mellitus (DM) is a multifaceted disease that leads to higher cardiovascular events with neuronal damage, inflammation, and oxidative stress in subjects. It also causes an autonomic imbalance with the onset of the disease which disturbs the cardiac dynamics. This work demonstrates the rutin in treating the inflammation caused by hyperglycemia through nonlinear heart rate variability features in predicting diabetes using a support vector machine (SVM). The lead-I electrocardiogram was acquired from the control, experimental, and treated group of the male Wister rats ([Formula: see text] gm and age 10–12 weeks). A dataset of 669 samples was obtained from the recorded ECG signal and taken as input vectors to the SVM. The observed results presented an accuracy of 92.9% in classifying the control and experimental group. Further, the same model with the treated group dataset showed an accuracy of 7.7% (samples nearer to the experimental group) while 92.3% of samples were close to the control group. The findings suggested the efficacy of rutin drugs in restoring the blood sugar level and the sympathovagal balance. The usefulness of the non-invasive technique in the prognosis of the disease gives direction in the design and development of the computer-aided cost-effective wearable system. However, the need for expert clinicians cannot be ignored.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"96 1","pages":""},"PeriodicalIF":0.6000,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"NONLINEAR HEART RATE VARIABILITY FEATURES IN DEPICTING THE EFFICACY OF RUTIN UNDER STREPTOZOTOCIN-INDUCED DIABETES MODEL WITH SUPPORT VECTOR MACHINE\",\"authors\":\"R. K. Sinha, Joyani Das, P. Mazumder, Yogender Aggarwal\",\"doi\":\"10.4015/s1016237223500151\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Diabetes mellitus (DM) is a multifaceted disease that leads to higher cardiovascular events with neuronal damage, inflammation, and oxidative stress in subjects. It also causes an autonomic imbalance with the onset of the disease which disturbs the cardiac dynamics. This work demonstrates the rutin in treating the inflammation caused by hyperglycemia through nonlinear heart rate variability features in predicting diabetes using a support vector machine (SVM). The lead-I electrocardiogram was acquired from the control, experimental, and treated group of the male Wister rats ([Formula: see text] gm and age 10–12 weeks). A dataset of 669 samples was obtained from the recorded ECG signal and taken as input vectors to the SVM. The observed results presented an accuracy of 92.9% in classifying the control and experimental group. Further, the same model with the treated group dataset showed an accuracy of 7.7% (samples nearer to the experimental group) while 92.3% of samples were close to the control group. The findings suggested the efficacy of rutin drugs in restoring the blood sugar level and the sympathovagal balance. The usefulness of the non-invasive technique in the prognosis of the disease gives direction in the design and development of the computer-aided cost-effective wearable system. However, the need for expert clinicians cannot be ignored.\",\"PeriodicalId\":8862,\"journal\":{\"name\":\"Biomedical Engineering: Applications, Basis and Communications\",\"volume\":\"96 1\",\"pages\":\"\"},\"PeriodicalIF\":0.6000,\"publicationDate\":\"2023-07-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biomedical Engineering: Applications, Basis and Communications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4015/s1016237223500151\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biomedical Engineering: Applications, Basis and Communications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4015/s1016237223500151","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
NONLINEAR HEART RATE VARIABILITY FEATURES IN DEPICTING THE EFFICACY OF RUTIN UNDER STREPTOZOTOCIN-INDUCED DIABETES MODEL WITH SUPPORT VECTOR MACHINE
Diabetes mellitus (DM) is a multifaceted disease that leads to higher cardiovascular events with neuronal damage, inflammation, and oxidative stress in subjects. It also causes an autonomic imbalance with the onset of the disease which disturbs the cardiac dynamics. This work demonstrates the rutin in treating the inflammation caused by hyperglycemia through nonlinear heart rate variability features in predicting diabetes using a support vector machine (SVM). The lead-I electrocardiogram was acquired from the control, experimental, and treated group of the male Wister rats ([Formula: see text] gm and age 10–12 weeks). A dataset of 669 samples was obtained from the recorded ECG signal and taken as input vectors to the SVM. The observed results presented an accuracy of 92.9% in classifying the control and experimental group. Further, the same model with the treated group dataset showed an accuracy of 7.7% (samples nearer to the experimental group) while 92.3% of samples were close to the control group. The findings suggested the efficacy of rutin drugs in restoring the blood sugar level and the sympathovagal balance. The usefulness of the non-invasive technique in the prognosis of the disease gives direction in the design and development of the computer-aided cost-effective wearable system. However, the need for expert clinicians cannot be ignored.
期刊介绍:
Biomedical Engineering: Applications, Basis and Communications is an international, interdisciplinary journal aiming at publishing up-to-date contributions on original clinical and basic research in the biomedical engineering. Research of biomedical engineering has grown tremendously in the past few decades. Meanwhile, several outstanding journals in the field have emerged, with different emphases and objectives. We hope this journal will serve as a new forum for both scientists and clinicians to share their ideas and the results of their studies.
Biomedical Engineering: Applications, Basis and Communications explores all facets of biomedical engineering, with emphasis on both the clinical and scientific aspects of the study. It covers the fields of bioelectronics, biomaterials, biomechanics, bioinformatics, nano-biological sciences and clinical engineering. The journal fulfils this aim by publishing regular research / clinical articles, short communications, technical notes and review papers. Papers from both basic research and clinical investigations will be considered.