Pub Date : 2021-09-20DOI: 10.2174/1574362416666210920164335
Tanu Sharma, K. Veer, K. Sharma
Electromyogram (EMG) signals are produced by the human body and are used in prosthetic design due to its significant functionality with human biomechanics. Engineers are capable of developing a variety of prosthetic limbs with the advancement of technology in the domain of biomedical signal processing, as limb amputees can restore their lives with the help of prosthetic limbs. This current review paper looks at the signals that are used to monitor the device, explaining the various steps and techniques involved (such as data acquisition, feature vector conversion after noise, and redundant data removal) and reviewing previously developed electromyogram-based prosthetic controls. Furthermore, this research also focuses on a variety of electromyogram controlled applications.
{"title":"Evaluation of Electromyogram Signals in the Control of Prosthetic Limb: A Review","authors":"Tanu Sharma, K. Veer, K. Sharma","doi":"10.2174/1574362416666210920164335","DOIUrl":"https://doi.org/10.2174/1574362416666210920164335","url":null,"abstract":"Electromyogram (EMG) signals are produced by the human body and are used in prosthetic design due to its significant functionality with human biomechanics. Engineers are capable of developing a variety of prosthetic limbs with the advancement of technology in the domain of biomedical signal processing, as limb amputees can restore their lives with the help of prosthetic limbs. This current review paper looks at the signals that are used to monitor the device, explaining the various steps and techniques involved (such as data acquisition, feature vector conversion after noise, and redundant data removal) and reviewing previously developed electromyogram-based prosthetic controls. Furthermore, this research also focuses on a variety of electromyogram controlled applications.","PeriodicalId":10868,"journal":{"name":"Current Signal Transduction Therapy","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48136696","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 : 2021-09-06DOI: 10.2174/1574362416666210906155929
Chaitali Mallick, Mitali Mishra, Vivek Asati, Varsha Kashaw, R. Das, A. Iyer, S. Kashaw
The development of multi-resistant strains of the Plasmodium parasite has become a global problem. Therefore, designing of new antimalarial agents is an exclusive solution.: To improve the activity and identify potentially efficacious new antimalarial agents, integrated computational perspectives such as pharmacophore mapping, 3D-QSAR and docking study have been applied to a series of indolo-quinoline derivatives. The pharmacophore mapping generated various hypotheses based on key functional features and the best hypothesis ADRRR_1 revealed that indolo-quinoline scaffold is essential for antimalarial activity. 3D-QSAR model was established based on CoMFA and CoMSIA models by using 30 indolo-quinoline analogues as training set and the rest of 19 as test set. The molecular field analysis (MFA) with PLS (partial least-squares) method was used to develop significant CoMFA (q2=0.756, r2=0.996) and CoMSIA (q2=0.703, r2=0.812) models. The CoMFA and CoMSIA models showed good predictive ability with r2pred values of 0.9623 and 0.9214 respectively. Docking studies were performed by using pfLDH to identify structural insight into the active site and results signify that the quinoline nitrogen acts as a hydrogen bond acceptor region to facilitate interaction with Glu122. Finally, designed molecules were screened through the ADMET tool to evaluate the pharmacokinetic and drug-likeness parameters. Thus, these studies suggested that established models have good predictability and would help in the optimization of newly designed molecules that may produce potent antimalarial activity.
{"title":"Integrated computational analysis on some Indolo-quinoline derivatives for the development of novel antiplasmodium agents: CoMFA, Pharmacophore mapping, molecular docking and ADMET studies","authors":"Chaitali Mallick, Mitali Mishra, Vivek Asati, Varsha Kashaw, R. Das, A. Iyer, S. Kashaw","doi":"10.2174/1574362416666210906155929","DOIUrl":"https://doi.org/10.2174/1574362416666210906155929","url":null,"abstract":"\u0000\u0000 The development of multi-resistant strains of the Plasmodium parasite has become a global problem. Therefore, designing of new antimalarial agents is an exclusive solution.: \u0000\u0000\u0000\u0000\u0000To improve the activity and identify potentially efficacious new antimalarial agents, integrated computational perspectives such as pharmacophore mapping, 3D-QSAR and docking study have been applied to a series of indolo-quinoline derivatives. \u0000\u0000\u0000\u0000\u0000The pharmacophore mapping generated various hypotheses based on key functional features and the best hypothesis ADRRR_1 revealed that indolo-quinoline scaffold is essential for antimalarial activity. 3D-QSAR model was established based on CoMFA and CoMSIA models by using 30 indolo-quinoline analogues as training set and the rest of 19 as test set. \u0000\u0000\u0000\u0000\u0000The molecular field analysis (MFA) with PLS (partial least-squares) method was used to develop significant CoMFA (q2=0.756, r2=0.996) and CoMSIA (q2=0.703, r2=0.812) models. The CoMFA and CoMSIA models showed good predictive ability with r2pred values of 0.9623 and 0.9214 respectively. Docking studies were performed by using pfLDH to identify structural insight into the active site and results signify that the quinoline nitrogen acts as a hydrogen bond acceptor region to facilitate interaction with Glu122. Finally, designed molecules were screened through the ADMET tool to evaluate the pharmacokinetic and drug-likeness parameters. \u0000\u0000\u0000\u0000\u0000Thus, these studies suggested that established models have good predictability and would help in the optimization of newly designed molecules that may produce potent antimalarial activity. \u0000\u0000","PeriodicalId":10868,"journal":{"name":"Current Signal Transduction Therapy","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49175353","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 : 2021-08-01DOI: 10.2174/157436241602210927103004
M. Khosravi
{"title":"Computer Methods and Network Applications in Healthcare Systems","authors":"M. Khosravi","doi":"10.2174/157436241602210927103004","DOIUrl":"https://doi.org/10.2174/157436241602210927103004","url":null,"abstract":"","PeriodicalId":10868,"journal":{"name":"Current Signal Transduction Therapy","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46431383","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 : 2021-08-01DOI: 10.2174/157436241602210525104532
M. Khosravi
{"title":"Multimedia and Information Technology in Healthcare Systems, Biosignal Communications and Biometrics","authors":"M. Khosravi","doi":"10.2174/157436241602210525104532","DOIUrl":"https://doi.org/10.2174/157436241602210525104532","url":null,"abstract":"","PeriodicalId":10868,"journal":{"name":"Current Signal Transduction Therapy","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45124588","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 : 2021-06-18DOI: 10.2174/1574362416666210618113305
Keertisudha S. Rajput, K. Veer
On multiple muscle locations, surface electromyography (sEMG) signals were recorded to predict the effect of different hand movements. Myoelectric information is a non-stationary signal, so extracting correct features is important to boost any myoelectric control devices' performance. The myoelectric signal is an electrical activity recorded by a surface electrode at various movements of the muscles. The study presented pattern recognition classification methods to select an excellent algorithm for controlling the SEMG signal. Various time domain and frequency domain parameters were extracted prior to conduct the classifier test. For the evaluation of the results for the recorded data (of all six movements), confusion matrix for neural network, support vector machine (SVM), DT, and linear discriminant analysis (LDA) classifiers is presented. This present study will be a step in analyzing different problems for developing lower limb prostheses.
{"title":"Semg Based Recognition Of Hand Motions For Lower Limb Prostheses","authors":"Keertisudha S. Rajput, K. Veer","doi":"10.2174/1574362416666210618113305","DOIUrl":"https://doi.org/10.2174/1574362416666210618113305","url":null,"abstract":"\u0000\u0000On multiple muscle locations, surface electromyography (sEMG) signals were recorded to predict the effect of different hand movements.\u0000\u0000\u0000\u0000Myoelectric information is a non-stationary signal, so extracting correct features is important to boost any myoelectric control devices' performance. The myoelectric signal is an electrical activity recorded by a surface electrode at various movements of the muscles.\u0000\u0000\u0000\u0000The study presented pattern recognition classification methods to select an excellent algorithm for controlling the SEMG signal.\u0000\u0000\u0000\u0000Various time domain and frequency domain parameters were extracted prior to conduct the classifier test.\u0000\u0000\u0000\u0000For the evaluation of the results for the recorded data (of all six movements), confusion matrix for neural network, support vector machine (SVM), DT, and linear discriminant analysis (LDA) classifiers is presented.\u0000\u0000\u0000\u0000This present study will be a step in analyzing different problems for developing lower limb prostheses.\u0000","PeriodicalId":10868,"journal":{"name":"Current Signal Transduction Therapy","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46617989","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 : 2021-06-10DOI: 10.2174/157436241601210505090706
M. Khosravi
{"title":"Signals and Network Applications for Medical Informatics, Wireless Body Area Systems and Remotely-Sensed E-Health Monitoring","authors":"M. Khosravi","doi":"10.2174/157436241601210505090706","DOIUrl":"https://doi.org/10.2174/157436241601210505090706","url":null,"abstract":"<jats:sec>\u0000<jats:title />\u0000<jats:p />\u0000</jats:sec>","PeriodicalId":10868,"journal":{"name":"Current Signal Transduction Therapy","volume":"16 1","pages":"2-2"},"PeriodicalIF":0.0,"publicationDate":"2021-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47129948","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 : 2021-06-10DOI: 10.2174/1574362414666191018104225
K. Balasubramanian, Ananthamoorthy N.P.
Diagnosis of ophthalmologic and cardiovascular systems most often rely on the prerequisite step of segmentation of retinal blood vessels. Analysis of vascular structures in the retinal fundus images can aid in the early screening or detection of many ophthalmological diseases like glaucoma, diabetic retinopathy, vein occlusions, hemorrhages etc. In most cases, optic nerve gets damaged causing a blind spot. In this paper, a method of blood vessel segmentation using improved SOM (iSOM) and ANN classifier is presented. Morphological operations are carried out to enhance the input image. Clustering of pixels is done using improved Kohonen Self- Organizing Map (SOM) based on texture feature wherein a new node is introduced and new learning methodology is adopted using constrained weight updation. Finally, modified Otsu method is designed to label the output neuron class as vessel and non -vessel. Segmentation is tested on public image sets, High Resolution Fundus (HRF) images and DRIONS-DB databases for Accuracy, Recall rate, Precision, F-Score, AUC and JC. The results achieve an appreciable level of accuracy (~97%) as compared to other similar methods of classification. The average time taken is less in estimating the neuron class and is about 12.1 sec per image when evaluated on Intel Core i5 CPU running at 2.30 GHz coupled with 4 GB RAM. The mean squared error for the segmented images is found to be in the range of 4-5%. Segmentation of retinal blood vessels based on artificial neural networks employing iSOM preserves the topology consuming less time for constrained weight updation achieving better results than SOM. A new model to detect vessels can be developed by concatenating iSOMs in parallel for multi class functions.
{"title":"ANN Classification and Modified Otsu Labeling on Retinal Blood Vessels","authors":"K. Balasubramanian, Ananthamoorthy N.P.","doi":"10.2174/1574362414666191018104225","DOIUrl":"https://doi.org/10.2174/1574362414666191018104225","url":null,"abstract":"\u0000\u0000Diagnosis of ophthalmologic and cardiovascular systems most often rely\u0000on the prerequisite step of segmentation of retinal blood vessels. Analysis of vascular structures in\u0000the retinal fundus images can aid in the early screening or detection of many ophthalmological\u0000diseases like glaucoma, diabetic retinopathy, vein occlusions, hemorrhages etc. In most cases, optic\u0000nerve gets damaged causing a blind spot. In this paper, a method of blood vessel segmentation\u0000using improved SOM (iSOM) and ANN classifier is presented.\u0000\u0000\u0000\u0000Morphological operations are carried out to enhance the input image. Clustering of pixels\u0000is done using improved Kohonen Self- Organizing Map (SOM) based on texture feature wherein\u0000a new node is introduced and new learning methodology is adopted using constrained weight\u0000updation. Finally, modified Otsu method is designed to label the output neuron class as vessel and\u0000non -vessel.\u0000\u0000\u0000\u0000 Segmentation is tested on public image sets, High Resolution Fundus (HRF) images and\u0000DRIONS-DB databases for Accuracy, Recall rate, Precision, F-Score, AUC and JC. The results\u0000achieve an appreciable level of accuracy (~97%) as compared to other similar methods of classification.\u0000The average time taken is less in estimating the neuron class and is about 12.1 sec per image\u0000when evaluated on Intel Core i5 CPU running at 2.30 GHz coupled with 4 GB RAM. The\u0000mean squared error for the segmented images is found to be in the range of 4-5%.\u0000\u0000\u0000\u0000Segmentation of retinal blood vessels based on artificial neural networks employing\u0000iSOM preserves the topology consuming less time for constrained weight updation achieving better\u0000results than SOM. A new model to detect vessels can be developed by concatenating iSOMs in\u0000parallel for multi class functions.\u0000","PeriodicalId":10868,"journal":{"name":"Current Signal Transduction Therapy","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47124181","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 : 2021-06-07DOI: 10.2174/1574362416666210607124228
O. Akbarzadeh, Mehrshid Baradaran, Mohammad Hossein Khosravi
The paper aims to review existing solutions for the Smart Building context that increase guests' hospitality levels in locations like hospitals. The answers could support features such as online appointments, smart navigation, and queue management in the building through mobile phone and navigation to the desired location by highlighting the point of interest and facilities and checking the spaces' occupancy. Such a solution addresses all mentioned issues regarding the smart building by integrating and utilizing various data sources collected by the internet of things (IoT) sensors. This solution mainly deals with location determining, also known as positioning, using Bluetooth Low Energy radio. The goal is to implement a low-power, low-cost indoor positioning system that utilizes existing hardware. Positions determining is considered a significant section of these kinds of solutions. Previous attempts with indoor positioning systems intensify statistical fingerprinting methods, mainly using IEEE 802.11 (WLAN) as the platform. some efforts have been made with purely signal strength-based positioning, but indoor environments have shown to work inauspiciously for these kinds of methods.
{"title":"IoT Solutions for Smart Management of Hospital Buildings: A General Review towards COVID-19, Future Pandemics and Infectious Diseases","authors":"O. Akbarzadeh, Mehrshid Baradaran, Mohammad Hossein Khosravi","doi":"10.2174/1574362416666210607124228","DOIUrl":"https://doi.org/10.2174/1574362416666210607124228","url":null,"abstract":"\u0000\u0000The paper aims to review existing solutions for the Smart Building context that increase guests' hospitality levels in locations like hospitals. The answers could support features such as online appointments, smart navigation, and queue management in the building through mobile phone and navigation to the desired location by highlighting the point of interest and facilities and checking the spaces' occupancy. \u0000\u0000\u0000\u0000Such a solution addresses all mentioned issues regarding the smart building by integrating and utilizing various data sources collected by the internet of things (IoT) sensors. This solution mainly deals with location determining, also known as positioning, using Bluetooth Low Energy radio. The goal is to implement a low-power, low-cost indoor positioning system that utilizes existing hardware. \u0000\u0000\u0000\u0000Positions determining is considered a significant section of these kinds of solutions. Previous attempts with indoor positioning systems intensify statistical fingerprinting methods, mainly using IEEE 802.11 (WLAN) as the platform. \u0000\u0000\u0000\u0000some efforts have been made with purely signal strength-based positioning, but indoor environments have shown to work inauspiciously for these kinds of methods.\u0000","PeriodicalId":10868,"journal":{"name":"Current Signal Transduction Therapy","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48070057","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 : 2021-05-05DOI: 10.2174/1574362416666210505111726
G. Brindha, G. Rohini
Currently, the clinical data stored in the cloud is easily accessible, and the patient’s data can be shared among treatment centers. In such a case, to handle additional data, the cloud data must be of a lesser scale. A process of compression was introduced to minimize the data with no losing data in order to achieve this size reduction. This paper conducts the experiment in two approaches: fast routing operations and compression from the chip in the DMFB approach. To apply this process of compression, the collected data from the chip was transformed into an image, and then compression of the image was performed utilizing a genetic algorithm (GA) based on a ring crossover. Consequently, the biochip of the 8x8 array is integrated into the power and area with the ring cross-module for an effective energy consumption operation. The technique of the process is utilized by the Microfluidic (MF) feature to handle and maintain the droplets. Also, the optimization process is performed by combining related pin actuation segments in parallel and the control pin to prevent pin-actuation conflicts. Through the optimization process, it synchronizes the length. This proposed approach decreases the consumption of the power and area. The outcome of the simulation indicates an increase in dynamic power, static power, and delay. Image compression is performed with the aid of this algorithm. In addition, for better outcomes, this GA compression application was contrasted with wavelet compressions.
{"title":"Ring Cross-Over Based Ga For Dfmb Chip Design And Medical Image Compression","authors":"G. Brindha, G. Rohini","doi":"10.2174/1574362416666210505111726","DOIUrl":"https://doi.org/10.2174/1574362416666210505111726","url":null,"abstract":"\u0000\u0000Currently, the clinical data stored in the cloud is easily accessible, and the patient’s data can be shared among treatment centers. In such a case, to handle additional data, the cloud data must be of a lesser scale. A process of compression was introduced to minimize the data with no losing data in order to achieve this size reduction. This paper conducts the experiment in two approaches: fast routing operations and compression from the chip in the DMFB approach. To apply this process of compression, the collected data from the chip was transformed into an image, and then compression of the image was performed utilizing a genetic algorithm (GA) based on a ring crossover. Consequently, the biochip of the 8x8 array is integrated into the power and area with the ring cross-module for an effective energy consumption operation. The technique of the process is utilized by the Microfluidic (MF) feature to handle and maintain the droplets. Also, the optimization process is performed by combining related pin actuation segments in parallel and the control pin to prevent pin-actuation conflicts. Through the optimization process, it synchronizes the length. This proposed approach decreases the consumption of the power and area. The outcome of the simulation indicates an increase in dynamic power, static power, and delay. Image compression is performed with the aid of this algorithm. In addition, for better outcomes, this GA compression application was contrasted with wavelet compressions.\u0000","PeriodicalId":10868,"journal":{"name":"Current Signal Transduction Therapy","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43518431","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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