Diabetes mellitus is a globally prevalent metabolic disease which results in altered plantar mechanical properties and foot ulcer. In this study, the bilateral asymmetry of mechanical properties for plantar soft tissue is investigated in healthy and diabetic conditions. Myotonometric signals are acquired from sub-metatarsal region of the plantar faces of healthy subjects and patients with varied diabetic age. Mechanical parameters such as dynamic stiffness and logarithmic decrement are extracted from the recorded signal. The asymmetry indices between right and left feet are computed. Statistical analysis shows that the spatial pattern of dynamic stiffness and logarithmic decrement varies significantly between healthy and diabetic subjects. The asymmetry index of dynamic stiffness in the fifth sub-metatarsal head can differentiate between healthy subjects and patients with both high and low diabetic age (p<0.05). The asymmetry index of logarithmic decrement is found to vary significantly between the healthy subjects and patients with higher diabetic age (p<0.05). These results indicate that bilateral asymmetry of myotonometric parameters can be exploited as a possible biomarker to differentiate diabetic patients from healthy subjects and can aid in the early detection of foot ulcer.
{"title":"STUDY OF THE BILATERAL ASYMMETRY OF PLANTAR MECHANICAL PROPERTIES AS A BIOMARKER FOR THE DIFFERENTIATION OF DIABETIC CONDITION","authors":"S. Banerjee, Srivasta Ananthan, Sree Clinic","doi":"10.34107/yhpn9422.04114","DOIUrl":"https://doi.org/10.34107/yhpn9422.04114","url":null,"abstract":"Diabetes mellitus is a globally prevalent metabolic disease which results in altered plantar mechanical properties and foot ulcer. In this study, the bilateral asymmetry of mechanical properties for plantar soft tissue is investigated in healthy and diabetic conditions. Myotonometric signals are acquired from sub-metatarsal region of the plantar faces of healthy subjects and patients with varied diabetic age. Mechanical parameters such as dynamic stiffness and logarithmic decrement are extracted from the recorded signal. The asymmetry indices between right and left feet are computed. Statistical analysis shows that the spatial pattern of dynamic stiffness and logarithmic decrement varies significantly between healthy and diabetic subjects. The asymmetry index of dynamic stiffness in the fifth sub-metatarsal head can differentiate between healthy subjects and patients with both high and low diabetic age (p<0.05). The asymmetry index of logarithmic decrement is found to vary significantly between the healthy subjects and patients with higher diabetic age (p<0.05). These results indicate that bilateral asymmetry of myotonometric parameters can be exploited as a possible biomarker to differentiate diabetic patients from healthy subjects and can aid in the early detection of foot ulcer.","PeriodicalId":75599,"journal":{"name":"Biomedical sciences instrumentation","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47287955","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}
H. Benghuzzi, Christopher B Powe, Dennis Watts, Todd A Barrett, M. Tucci
Introduction: The federal government estimates that per mile traveled in 2018, the number of deaths on motorcycles was nearly 27 times the number in cars. In the United States there is no universal helmet law. In twenty-two states, motorcycle helmets are entirely optional, while in nineteen states and the District of Columbia universal motorcycle helmets laws requiring helmets for all riders regardless of age are implemented and nine states only require younger motorcycle riders to wear a helmet, with varying age limits. Objectives: The overall objectives of this study were to evaluate the following: (1) number of fatalities (with and without helmet use), (2) fatality rate per motorcycle registration, (3) fatality percentage with age, (4) percent fatality due to alcohol impairment, and (5) location of collision impact to the rider in two southern states (Mississippi and Alabama) where helmet laws are established compared with a southern state (Florida) that only requires riders less than 20 years of age to be helmeted. Methods: Data from 2015-2018 were obtained from the National Highway Transportation Safety Administration Reporting System (FARS) and supplemented with state related and CDC data. Results: In all three states, the most common collision was a front-end impact. Mississippi had the highest percentage of motorcycle fatalities even with >80% of riders helmeted when the fatal accident occurred, followed by Florida motorcyclist who are only 50% of the time helmeted. In all three southern states similar percentage of fatalities were seen in each age group with higher fatalities associated with age range of 30-39 years. Conclusions: Variables such as helmet type, distance from a level 1 trauma center, poor roads, weather conditions, and visibility of the rider may also be factors that contribute to a higher incidence of fatality and need to be further investigated to improve motorcycle safety.
{"title":"MOTORCYCLE HELMET USE AND FATALITIES IN THE SOUTHEAST REGION OF THE USA","authors":"H. Benghuzzi, Christopher B Powe, Dennis Watts, Todd A Barrett, M. Tucci","doi":"10.34107/yhpn9422.04145","DOIUrl":"https://doi.org/10.34107/yhpn9422.04145","url":null,"abstract":"Introduction: The federal government estimates that per mile traveled in 2018, the number of deaths on motorcycles was nearly 27 times the number in cars. In the United States there is no universal helmet law. In twenty-two states, motorcycle helmets are entirely optional, while in nineteen states and the District of Columbia universal motorcycle helmets laws requiring helmets for all riders regardless of age are implemented and nine states only require younger motorcycle riders to wear a helmet, with varying age limits. Objectives: The overall objectives of this study were to evaluate the following: (1) number of fatalities (with and without helmet use), (2) fatality rate per motorcycle registration, (3) fatality percentage with age, (4) percent fatality due to alcohol impairment, and (5) location of collision impact to the rider in two southern states (Mississippi and Alabama) where helmet laws are established compared with a southern state (Florida) that only requires riders less than 20 years of age to be helmeted. Methods: Data from 2015-2018 were obtained from the National Highway Transportation Safety Administration Reporting System (FARS) and supplemented with state related and CDC data. Results: In all three states, the most common collision was a front-end impact. Mississippi had the highest percentage of motorcycle fatalities even with >80% of riders helmeted when the fatal accident occurred, followed by Florida motorcyclist who are only 50% of the time helmeted. In all three southern states similar percentage of fatalities were seen in each age group with higher fatalities associated with age range of 30-39 years. Conclusions: Variables such as helmet type, distance from a level 1 trauma center, poor roads, weather conditions, and visibility of the rider may also be factors that contribute to a higher incidence of fatality and need to be further investigated to improve motorcycle safety.","PeriodicalId":75599,"journal":{"name":"Biomedical sciences instrumentation","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41433574","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}
E. Florez, T. V. Thomas, C. Howard, H. Khosravi, S. Lirette, A. Fatemi
Surveillance imaging of HNSCC in patients treated with chemoradiotherapy suffers from difficulty in differentiating residual disease from radiation changes and inflammation. Thus, this study assessed ML models based on RadFs extracted from standard CT images pre- and post-chemoradiation to predict HNSCC treatment response. A retrospective analysis of HNSCC patients treated with definitive chemoradiotherapy at our institution between 2006 and 2015 was performed. Thirty-six patients with residual disease on CT scans of the soft tissue of the neck at a two- month interval-either in the primary site, nodal stations, or both-were enrolled. GTV contours from the treatment planning CT (CT1), post-treatment CT (CT2), and CT portion of the PET/CT (CT3) of the neck were exported to MatLab®, where 2D and 3D RadFs were extracted using different methods. Finally, ML models were used to identify the RadFs that predict changes and progression in HNSCC patients treated with chemoradiotherapy. SVM models using 2D RadFs, extracted from CT2, were associated with residual disease on PET/CT exams (AUC = 0.702). 2D RadFs extracted from PET/CT had moderate predictive ability to predict positive pathology for residual tumor (AUC = 0.667). NN and RF models of 3D RadFs extracted from CT2 and PET/CT had good and moderate predictive ability to predict positive pathology for residual tumor (AUC = 0.720 and 0.678, respectively). ML models using 2D and 3D RadFs derived from pre- and post-treatment CT data show promise for predicting residual tumor from radiation changes and inflammation in a small group of HNSCC cancer patients treated with chemoradiotherapy.
{"title":"MACHINE LEARNING BASED ON CT RADIOMIC FEATURES PREDICTS RESIDUAL TUMOR IN HEAD AND NECK CANCER PATIENTS TREATED WITH CHEMORADIOTHERAPY","authors":"E. Florez, T. V. Thomas, C. Howard, H. Khosravi, S. Lirette, A. Fatemi","doi":"10.34107/yhpn9422.04199","DOIUrl":"https://doi.org/10.34107/yhpn9422.04199","url":null,"abstract":"Surveillance imaging of HNSCC in patients treated with chemoradiotherapy suffers from difficulty in differentiating residual disease from radiation changes and inflammation. Thus, this study assessed ML models based on RadFs extracted from standard CT images pre- and post-chemoradiation to predict HNSCC treatment response. A retrospective analysis of HNSCC patients treated with definitive chemoradiotherapy at our institution between 2006 and 2015 was performed. Thirty-six patients with residual disease on CT scans of the soft tissue of the neck at a two- month interval-either in the primary site, nodal stations, or both-were enrolled. GTV contours from the treatment planning CT (CT1), post-treatment CT (CT2), and CT portion of the PET/CT (CT3) of the neck were exported to MatLab®, where 2D and 3D RadFs were extracted using different methods. Finally, ML models were used to identify the RadFs that predict changes and progression in HNSCC patients treated with chemoradiotherapy. SVM models using 2D RadFs, extracted from CT2, were associated with residual disease on PET/CT exams (AUC = 0.702). 2D RadFs extracted from PET/CT had moderate predictive ability to predict positive pathology for residual tumor (AUC = 0.667). NN and RF models of 3D RadFs extracted from CT2 and PET/CT had good and moderate predictive ability to predict positive pathology for residual tumor (AUC = 0.720 and 0.678, respectively). ML models using 2D and 3D RadFs derived from pre- and post-treatment CT data show promise for predicting residual tumor from radiation changes and inflammation in a small group of HNSCC cancer patients treated with chemoradiotherapy.","PeriodicalId":75599,"journal":{"name":"Biomedical sciences instrumentation","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42296290","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}
Y. R. Veeranki, Nagarajan Ganapathy, R. Swaminathan
Prediction and recognition of happy and sad emotional states play important roles in many aspects of human life. In this work, an attempt has been made to classify them using Electrodermal Activity (EDA). For this, EDA signals are obtained from a public database and decomposed into tonic and phasic components. Features, namely Hjorth and higher-order crossing, are extracted from the phasic component of the signal. Further, these extracted features are fed to four parametric classifiers, namely, linear discriminant analysis, logistic regression, multilayer perceptron, and naive bayes for the classification. The results show that the proposed approach can classify the dichotomous happy and sad emotional states. The multilayer perceptron classifier is accurate (85.7%) in classifying happy and sad emotional states. The proposed method is robust in handling the dynamic variation of EDA signals for happy and sad emotional states. Thus, it appears that the proposed method could be able to understand the neurological, psychiatrical, and biobehavioural mechanisms of happy and sad emotional states.
{"title":"DIFFERENTIATION OF DICHOTOMOUS EMOTIONAL STATES IN ELECTRODERMAL ACTIVITY SIGNALS USING HIGHER-ORDER CROSSING FEATURES AND PARAMETRIC CLASSIFIERS","authors":"Y. R. Veeranki, Nagarajan Ganapathy, R. Swaminathan","doi":"10.34107/yhpn9422.04322","DOIUrl":"https://doi.org/10.34107/yhpn9422.04322","url":null,"abstract":"Prediction and recognition of happy and sad emotional states play important roles in many aspects of human life. In this work, an attempt has been made to classify them using Electrodermal Activity (EDA). For this, EDA signals are obtained from a public database and decomposed into tonic and phasic components. Features, namely Hjorth and higher-order crossing, are extracted from the phasic component of the signal. Further, these extracted features are fed to four parametric classifiers, namely, linear discriminant analysis, logistic regression, multilayer perceptron, and naive bayes for the classification. The results show that the proposed approach can classify the dichotomous happy and sad emotional states. The multilayer perceptron classifier is accurate (85.7%) in classifying happy and sad emotional states. The proposed method is robust in handling the dynamic variation of EDA signals for happy and sad emotional states. Thus, it appears that the proposed method could be able to understand the neurological, psychiatrical, and biobehavioural mechanisms of happy and sad emotional states.","PeriodicalId":75599,"journal":{"name":"Biomedical sciences instrumentation","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45667343","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}
Nicole C H Lim, V. M. Pedro, E. Oggero, Llc Cheyenne Wy Usa Vestibular Technologies
Migraine is a common neurological disorder that is characterized by a host of symptoms including severe throbbing headaches. In this retrospective chart review, the effectiveness of Cortical Integrative Therapy (PedroCIT®) was examined in adults with migraines. Multivariate General Linear Model (M-GLM) was utilized to determine if the emotional, functional, and overall difficulties, as well as the intensity of pain experienced with headaches decreased from before to after PedroCIT® treatment in individuals with mild to complete disability resulting from headaches. Repeated Measures General Linear Model (RM-GLM) was also used to investigate if postural stability increased from pre- to post-treatment. The results of the M-GLM showed that PedroCIT® was effective in reducing emotional, functional, overall disability, and intensity of pain resulting from headaches. Furthermore, RM-GLM indicated that patients who underwent PedroCIT® improved their postural stability from pre- to post-treatment. Finally, the findings also showed that the duration of the treatment did not have any effect among patients with varied degrees of headache disability. This study illustrates the effectiveness of PedroCIT® in the treatment of headaches and postural instability in migraine patients.
{"title":"CORTICAL INTEGRATIVE THERAPY FOR THE TREATMENT OF MIGRAINES AND HEADACHES","authors":"Nicole C H Lim, V. M. Pedro, E. Oggero, Llc Cheyenne Wy Usa Vestibular Technologies","doi":"10.34107/yhpn9422.04168","DOIUrl":"https://doi.org/10.34107/yhpn9422.04168","url":null,"abstract":"Migraine is a common neurological disorder that is characterized by a host of symptoms including severe throbbing headaches. In this retrospective chart review, the effectiveness of Cortical Integrative Therapy (PedroCIT®) was examined in adults with migraines. Multivariate General Linear Model (M-GLM) was utilized to determine if the emotional, functional, and overall difficulties, as well as the intensity of pain experienced with headaches decreased from before to after PedroCIT® treatment in individuals with mild to complete disability resulting from headaches. Repeated Measures General Linear Model (RM-GLM) was also used to investigate if postural stability increased from pre- to post-treatment. The results of the M-GLM showed that PedroCIT® was effective in reducing emotional, functional, overall disability, and intensity of pain resulting from headaches. Furthermore, RM-GLM indicated that patients who underwent PedroCIT® improved their postural stability from pre- to post-treatment. Finally, the findings also showed that the duration of the treatment did not have any effect among patients with varied degrees of headache disability. This study illustrates the effectiveness of PedroCIT® in the treatment of headaches and postural instability in migraine patients.","PeriodicalId":75599,"journal":{"name":"Biomedical sciences instrumentation","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46035422","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}
Seong Hyun Moon, Rahul Soangra, C. Frames, T. Lockhart
Parkinson’s Disease (PD) is a neurodegenerative disorder affecting the substantia nigra, which leads to more than half of PD patients are considered to be at high risk of falling. Recently, Inertial Measurement Unit (IMU) sensors have shown great promise in the classification of activities of daily living (ADL) such as walking, standing, sitting, and laying down, considered to be normal movement in daily life. Measuring physical activity level from longitudinal ADL monitoring among PD patients could provide insights into their fall mechanisms. In this study, six PD patients (mean age=74.3±6.5 years) and six young healthy subjects (mean age=19.7±2.7 years) were recruited. All the subjects were asked to wear the single accelerometer, DynaPort MM+ (Motion Monitor+, McRoberts BV, The Hague, Netherlands), with a sampling frequency of 100 Hz located at the L5-S1 spinal area for 3 days. Subjects maintained a log of activities they performed and only removed the sensor while showering or performing other aquatic activities. The resultant acceleration was filtered using high and low pass Butterworth filters to determine dynamic and stationary activities. As a result, it was found that healthy young subjects performed significantly more dynamic activities (13.2%) when compared to PD subjects (7%), in contrast, PD subjects (92.9%) had significantly more stationary activities than young healthy subjects (86.8%).
帕金森病(PD)是一种影响黑质的神经退行性疾病，超过一半的PD患者被认为是跌倒的高危人群。最近，惯性测量单元(IMU)传感器在日常生活活动(ADL)的分类中显示出巨大的前景，例如行走，站立，坐着和躺着，被认为是日常生活中的正常运动。通过对PD患者进行纵向ADL监测，测量他们的身体活动水平，可以深入了解他们的跌倒机制。本研究招募了6例PD患者(平均年龄=74.3±6.5岁)和6例年轻健康受试者(平均年龄=19.7±2.7岁)。所有受试者被要求在L5-S1脊柱区域佩戴单个加速度计DynaPort MM+(运动监视器+，McRoberts BV, the Hague, Netherlands)，采样频率为100 Hz，持续3天。受试者对他们所做的活动进行记录，只有在洗澡或进行其他水上活动时才取下传感器。由此产生的加速度使用高通和低通巴特沃斯滤波器进行滤波，以确定动态和静止活动。结果发现，健康青年受试者的动态活动(13.2%)明显多于PD受试者(7%)，而PD受试者(92.9%)的静态活动明显多于年轻健康受试者(86.8%)。
{"title":"THREE DAYS MONITORING OF ACTIVITIES OF DAILY LIVING AMONG YOUNG HEALTHY ADULTS AND PARKINSON’S DISEASE PATIENTS","authors":"Seong Hyun Moon, Rahul Soangra, C. Frames, T. Lockhart","doi":"10.34107/yhpn9422.04177","DOIUrl":"https://doi.org/10.34107/yhpn9422.04177","url":null,"abstract":"Parkinson’s Disease (PD) is a neurodegenerative disorder affecting the substantia nigra, which leads to more than half of PD patients are considered to be at high risk of falling. Recently, Inertial Measurement Unit (IMU) sensors have shown great promise in the classification of activities of daily living (ADL) such as walking, standing, sitting, and laying down, considered to be normal movement in daily life. Measuring physical activity level from longitudinal ADL monitoring among PD patients could provide insights into their fall mechanisms. In this study, six PD patients (mean age=74.3±6.5 years) and six young healthy subjects (mean age=19.7±2.7 years) were recruited. All the subjects were asked to wear the single accelerometer, DynaPort MM+ (Motion Monitor+, McRoberts BV, The Hague, Netherlands), with a sampling frequency of 100 Hz located at the L5-S1 spinal area for 3 days. Subjects maintained a log of activities they performed and only removed the sensor while showering or performing other aquatic activities. The resultant acceleration was filtered using high and low pass Butterworth filters to determine dynamic and stationary activities. As a result, it was found that healthy young subjects performed significantly more dynamic activities (13.2%) when compared to PD subjects (7%), in contrast, PD subjects (92.9%) had significantly more stationary activities than young healthy subjects (86.8%).","PeriodicalId":75599,"journal":{"name":"Biomedical sciences instrumentation","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48116379","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}
Segmentation of breast cancer tumor plays an important role in identifying the location of the tumor, to know the shape of tumor and hence the stage of breast cancer. This paper deals with the segmentation of tumor from whole mammographic mass images using Generative Adversarial Network (GAN). A mini dataset was considered with mammograms and their corresponding ground truth images. Pre-processing like image format conversion, enhancement, pectoral muscle removal and resizing was performed on raw mammogram images. GANs have two neural nets called generative and discriminative networks that compete against each other to obtain the segmentation output. PIX2PIX is a conditional GAN variant which has U-Net as the Generator network and a simple deep neural net as the discriminator. The input to the network was pair of pre-processed mass image and the associated ground truth. A binary image with highlighted tumor was obtained as output. The performance of GAN was evaluated by plotting Generator and discriminator loss. The segmented output was compared with corresponding ground truth. Metrics like Jaccard index, Jaccard distance and Dice-coefficient were calculated. A Dice-coefficient and Jaccard index of 90% and 88.38% was achieved. In future, higher accuracy could be achieved by involving larger dataset to make the system robust.
{"title":"BREAST CANCER SEGMENTATION OF MAMMOGRAPHICS IMAGES USING GENERATIVE","authors":"N. Swathi, T. Bobby","doi":"10.34107/yhpn9422.04247","DOIUrl":"https://doi.org/10.34107/yhpn9422.04247","url":null,"abstract":"Segmentation of breast cancer tumor plays an important role in identifying the location of the tumor, to know the shape of tumor and hence the stage of breast cancer. This paper deals with the segmentation of tumor from whole mammographic mass images using Generative Adversarial Network (GAN). A mini dataset was considered with mammograms and their corresponding ground truth images. Pre-processing like image format conversion, enhancement, pectoral muscle removal and resizing was performed on raw mammogram images. GANs have two neural nets called generative and discriminative networks that compete against each other to obtain the segmentation output. PIX2PIX is a conditional GAN variant which has U-Net as the Generator network and a simple deep neural net as the discriminator. The input to the network was pair of pre-processed mass image and the associated ground truth. A binary image with highlighted tumor was obtained as output. The performance of GAN was evaluated by plotting Generator and discriminator loss. The segmented output was compared with corresponding ground truth. Metrics like Jaccard index, Jaccard distance and Dice-coefficient were calculated. A Dice-coefficient and Jaccard index of 90% and 88.38% was achieved. In future, higher accuracy could be achieved by involving larger dataset to make the system robust.","PeriodicalId":75599,"journal":{"name":"Biomedical sciences instrumentation","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43136987","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}
D. Damani, Divaakar Siva Baala Sundaram, S. Damani, Anoushka Kapoor, Adelaide Olson, S. P. Arunachalam
Cardiac diseases are the leading cause of death in the world. Electrocardiogram (ECG and Phonocardiogram (PCG signals play a significant role in the diagnosis of various cardiac diseases. Simultaneous acquisition of ECG and PCG signals can open new avenues of signal processing approaches for electromechanical profiling of the heart. However, there are no standard approaches to ensure high fidelity synchronous data acquisition to enable the development of such novel technologies. In this work, the authors report results on various data capture positions that could lead to standardization of simultaneous ECG and PCG data collection. Presence of lung sounds, variations in posture, depth and frequency of breathing can lead to differences in the ECG-PCG signals recorded. This necessitates a standard approach to record and interpret the data collected. The authors recorded ECG-PCG simultaneously in six healthy subjects using a digital stethoscope to understand the differences in signal quality in various recording positions (prone, supine, bending, semi recumbent, standing, left lateral and sitting with normal and deep breathing conditions. The collected digitized signals are processed offline for signal quality using custom MATLAB software for SNR. The results indicate minimal differences in signal quality across different recording positions. Validation of this technique with larger dataset is required. Future work will investigate changes in characteristic ECG and PCG features due to position and breathing patterns.
{"title":"INVESTIGATION OF SYNCHRONIZED ACQUISITION OF ELECTROCARDIOGRAM AND PHONOCARDIOGRAM SIGNALS TOWARDS ELECTROMECHANICAL PROFILING OF THE HEART","authors":"D. Damani, Divaakar Siva Baala Sundaram, S. Damani, Anoushka Kapoor, Adelaide Olson, S. P. Arunachalam","doi":"10.34107/yhpn9422.04305","DOIUrl":"https://doi.org/10.34107/yhpn9422.04305","url":null,"abstract":"Cardiac diseases are the leading cause of death in the world. Electrocardiogram (ECG and Phonocardiogram (PCG signals play a significant role in the diagnosis of various cardiac diseases. Simultaneous acquisition of ECG and PCG signals can open new avenues of signal processing approaches for electromechanical profiling of the heart. However, there are no standard approaches to ensure high fidelity synchronous data acquisition to enable the development of such novel technologies. In this work, the authors report results on various data capture positions that could lead to standardization of simultaneous ECG and PCG data collection. Presence of lung sounds, variations in posture, depth and frequency of breathing can lead to differences in the ECG-PCG signals recorded. This necessitates a standard approach to record and interpret the data collected. The authors recorded ECG-PCG simultaneously in six healthy subjects using a digital stethoscope to understand the differences in signal quality in various recording positions (prone, supine, bending, semi recumbent, standing, left lateral and sitting with normal and deep breathing conditions. The collected digitized signals are processed offline for signal quality using custom MATLAB software for SNR. The results indicate minimal differences in signal quality across different recording positions. Validation of this technique with larger dataset is required. Future work will investigate changes in characteristic ECG and PCG features due to position and breathing patterns.","PeriodicalId":75599,"journal":{"name":"Biomedical sciences instrumentation","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69805252","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}
V. M. Pedro, Juby Mathew, E. Oggero, Llc Cheyenne Wy Usa Vestibular Technologies
Cortical Visual Impairment (CVI) is resultant from neurological injury and damage to visual pathways or vision centers in the brain. CVI is sometimes undiagnosed in individuals with brain injuries due to the complexity of the human visual system. The International Institute for the Brain (iBrain) is a specialized school for students ages 5 to 21 who have a brain disorder or an acquired brain injury. They often present with noticeable CVI. While there are various metrics and interventions for the pediatric population, the adequacy has been lacking in terms of the vulnerability of this non-verbal population. Assessing the safety and effectiveness of rehabilitative interventions for this fragile student population can be challenging as most traditional metrics cannot be used. In this methodological review paper, available metrics were investigated and their applicability for this specific population is discussed with the end goal of identifying the best metrics that could be used to determine treatment effectiveness and providing a way for monitoring adverse effects. Combining pulse oximetry, cortisol response sensor, and galvanic skin response as biometrics theoretically offers a comprehensive assessment of autonomic activity and responses and establishes objective measures to identify treatment outcomes and adverse reactions. However, future experimental studies are needed to verify if the proposed protocol is feasible and if it is well tolerated by the iBrain students before it can be implemented to monitor adverse reaction to intervention and as a potential treatment outcome measure for children affected by CVI.
{"title":"HYPOTHETICAL FEASIBILITY OF USING STRESS BIOMETRICS IN STUDENTS WITH CORTICAL VISUAL IMPAIRMENT","authors":"V. M. Pedro, Juby Mathew, E. Oggero, Llc Cheyenne Wy Usa Vestibular Technologies","doi":"10.34107/yhpn9422.04184","DOIUrl":"https://doi.org/10.34107/yhpn9422.04184","url":null,"abstract":"Cortical Visual Impairment (CVI) is resultant from neurological injury and damage to visual pathways or vision centers in the brain. CVI is sometimes undiagnosed in individuals with brain injuries due to the complexity of the human visual system. The International Institute for the Brain (iBrain) is a specialized school for students ages 5 to 21 who have a brain disorder or an acquired brain injury. They often present with noticeable CVI. While there are various metrics and interventions for the pediatric population, the adequacy has been lacking in terms of the vulnerability of this non-verbal population. Assessing the safety and effectiveness of rehabilitative interventions for this fragile student population can be challenging as most traditional metrics cannot be used. In this methodological review paper, available metrics were investigated and their applicability for this specific population is discussed with the end goal of identifying the best metrics that could be used to determine treatment effectiveness and providing a way for monitoring adverse effects. Combining pulse oximetry, cortisol response sensor, and galvanic skin response as biometrics theoretically offers a comprehensive assessment of autonomic activity and responses and establishes objective measures to identify treatment outcomes and adverse reactions. However, future experimental studies are needed to verify if the proposed protocol is feasible and if it is well tolerated by the iBrain students before it can be implemented to monitor adverse reaction to intervention and as a potential treatment outcome measure for children affected by CVI.","PeriodicalId":75599,"journal":{"name":"Biomedical sciences instrumentation","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48256149","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}
Alzheimer’s Disease (AD) is an irreversible neurodegenerative disorder that affects brain structures. Corpus Callosum (CC) atrophy and Lateral ventricle (LV) enlargement are useful structural biomarkers in distinguishing the preclinical stages of AD. The shape of CC appears to be homogeneous from normal controls to AD images and LV shows shape dissimilarity across subjects. Therefore, effective methods to segment CC and LV are essential to characterize the magnitude of morphometric changes. In this study, an attempt has been made to segment CC and LV from MR brain images using the Spatial Fuzzy Clustering based Level Set (SFC-LS) method. For this, T1-weighted MR images of AD, Mild Cognitive Impairment (MCI), and normal controls are obtained from a public database. Spatial fuzzy clustering forms the initial contour for the level set and regularizes the evolution of curve. The segmented images are validated against ground truth using standard measures. Results indicate that SFC-LS is able to segment CC and LV with automated contour initialization. The final contours obtained are sharp and distinct with a high validation performance of accuracy and specificity greater than 97% for normal controls, MCI, and AD. A dice score of 83% and 84% is achieved in segmenting CC and LV respectively. As structural changes in CC and LV have the potential to predict the early stages of AD, the proposed approach seems to be clinically significant.
{"title":"SEGMENTATION OF BRAIN STRUCTURES IN ALZHEIMER MR IMAGES USING SPATIAL FUZZY CLUSTERING LEVEL SET","authors":"Sreelakshmi Shaji, R. Swaminathan","doi":"10.34107/yhpn9422.04234","DOIUrl":"https://doi.org/10.34107/yhpn9422.04234","url":null,"abstract":"Alzheimer’s Disease (AD) is an irreversible neurodegenerative disorder that affects brain structures. Corpus Callosum (CC) atrophy and Lateral ventricle (LV) enlargement are useful structural biomarkers in distinguishing the preclinical stages of AD. The shape of CC appears to be homogeneous from normal controls to AD images and LV shows shape dissimilarity across subjects. Therefore, effective methods to segment CC and LV are essential to characterize the magnitude of morphometric changes. In this study, an attempt has been made to segment CC and LV from MR brain images using the Spatial Fuzzy Clustering based Level Set (SFC-LS) method. For this, T1-weighted MR images of AD, Mild Cognitive Impairment (MCI), and normal controls are obtained from a public database. Spatial fuzzy clustering forms the initial contour for the level set and regularizes the evolution of curve. The segmented images are validated against ground truth using standard measures. Results indicate that SFC-LS is able to segment CC and LV with automated contour initialization. The final contours obtained are sharp and distinct with a high validation performance of accuracy and specificity greater than 97% for normal controls, MCI, and AD. A dice score of 83% and 84% is achieved in segmenting CC and LV respectively. As structural changes in CC and LV have the potential to predict the early stages of AD, the proposed approach seems to be clinically significant.","PeriodicalId":75599,"journal":{"name":"Biomedical sciences instrumentation","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49118494","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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